Unit 4 Capstone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Overview:https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews/home  \n",
    "This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables:\n",
    "\n",
    "Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed.  \n",
    "Age: Positive Integer variable of the reviewers age.  \n",
    "Title: String variable for the title of the review.  \n",
    "Review Text: String variable for the review body.  \n",
    "Rating: Positive Ordinal Integer variable for the product score granted by the customer from 1 Worst, to 5 Best.  \n",
    "Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not   recommended.  \n",
    "Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive.  \n",
    "Division Name: Categorical name of the product high level division.  \n",
    "Department Name: Categorical name of the product department name.  \n",
    "Class Name: Categorical name of the product class name.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Division Name</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>767</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Absolutely wonderful - silky and sexy and comf...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Initmates</td>\n",
       "      <td>Intimate</td>\n",
       "      <td>Intimates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Clothing ID  Age                    Title  \\\n",
       "0           0          767   33                      NaN   \n",
       "1           1         1080   34                      NaN   \n",
       "2           2         1077   60  Some major design flaws   \n",
       "3           3         1049   50         My favorite buy!   \n",
       "4           4          847   47         Flattering shirt   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "\n",
       "   Positive Feedback Count   Division Name Department Name Class Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23486 entries, 0 to 23485\n",
      "Data columns (total 11 columns):\n",
      "Unnamed: 0                 23486 non-null int64\n",
      "Clothing ID                23486 non-null int64\n",
      "Age                        23486 non-null int64\n",
      "Title                      19676 non-null object\n",
      "Review Text                22641 non-null object\n",
      "Rating                     23486 non-null int64\n",
      "Recommended IND            23486 non-null int64\n",
      "Positive Feedback Count    23486 non-null int64\n",
      "Division Name              23472 non-null object\n",
      "Department Name            23472 non-null object\n",
      "Class Name                 23472 non-null object\n",
      "dtypes: int64(6), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                    0\n",
       "Clothing ID                   0\n",
       "Age                           0\n",
       "Title                      3810\n",
       "Review Text                 845\n",
       "Rating                        0\n",
       "Recommended IND               0\n",
       "Positive Feedback Count       0\n",
       "Division Name                14\n",
       "Department Name              14\n",
       "Class Name                   14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clothing ID                0\n",
      "Age                        0\n",
      "Title                      0\n",
      "Review Text                0\n",
      "Rating                     0\n",
      "Recommended IND            0\n",
      "Positive Feedback Count    0\n",
      "Division Name              0\n",
      "Department Name            0\n",
      "Class Name                 0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(19662, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "print(df.isnull().sum())\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General           59.322551\n",
      "General Petite    34.472587\n",
      "Initmates          6.204862\n",
      "Name: Division Name, dtype: float64\n",
      "Tops        44.313905\n",
      "Dresses     27.316651\n",
      "Bottoms     16.193673\n",
      "Intimate     7.161021\n",
      "Jackets      4.470552\n",
      "Trend        0.544197\n",
      "Name: Department Name, dtype: float64\n",
      "Dresses           27.316651\n",
      "Knits             20.247177\n",
      "Blouses           13.157359\n",
      "Sweaters           6.194690\n",
      "Pants              5.884447\n",
      "Jeans              4.933374\n",
      "Fine gauge         4.714678\n",
      "Skirts             4.048418\n",
      "Jackets            3.041400\n",
      "Lounge             2.919337\n",
      "Swim               1.490184\n",
      "Outerwear          1.429153\n",
      "Shorts             1.322348\n",
      "Sleep              0.884956\n",
      "Legwear            0.666260\n",
      "Intimates          0.610314\n",
      "Layering           0.584885\n",
      "Trend              0.544197\n",
      "Chemises           0.005086\n",
      "Casual bottoms     0.005086\n",
      "Name: Class Name, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((df['Division Name'].value_counts()/(df['Division Name'].value_counts().sum()))*100)\n",
    "print((df['Department Name'].value_counts()/(df['Department Name'].value_counts().sum()))*100)\n",
    "print((df['Class Name'].value_counts()/(df['Class Name'].value_counts().sum()))*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rating Range and NUM_Negative Feedbacks were dropped because it all contained the same value.Num of feedbacks was dropped because it no longer provided any value after NUM_Negative feedbacks was no longer useful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Age'])\n",
    "sns.set(rc={'figure.figsize':(10,10)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age range is from 18 to 99, but looking at some of the reviews above 80 don't seem to match the age associated with the review. So the distribution shown likely is not the correct shape. However, the majority of the reviews seem to come from ages 18 to 40, which is expected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Rating', hue='Recommended IND',data=df)\n",
    "sns.set(rc={'figure.figsize':(10,20)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    10858\n",
       "4     4289\n",
       "3     2464\n",
       "2     1360\n",
       "1      691\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rating'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of the reviews were positive with 77% at 4 or higher and 89% of reviews over 3. 3 would normally be the cutoff for an acceptable review while 2 or lower would be considered poor. However, it is interesting to see the high amount of non-recommended product recommendations at 3 with some even present at 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaI3hHgBeqmOOPrzaO/r/K7ynp7e273humCYCAA5FgphDVntHo26Zv6zf2rybLximaQCAQ5UjEwAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEa2kQf+Yzn6np06fX9OnTa9GiRVVVtXr16poxY0ZNmTKlbrvttr7nPvbYY3X++efX1KlTa/78+dXb21tVVRs3bqyZM2fWtGnT6oorrqhdu3a1cmQAAMK0LIhXr15dDz74YN1zzz21fPnyevTRR2vFihU1b968WrJkSa1cubLWrl1bDzzwQFVVXXvttXX99dfXfffdV81ms5YuXVpVVTfeeGNdfPHFtWrVqjrllFNqyZIlrRoZAIBALQvizs7Omjt3brW3t9fo0aPrxBNPrHXr1tWECRNq/Pjx1Wg0asaMGbVq1arasGFD7d69uyZOnFhVVeedd16tWrWq9u7dWw899FBNnTq13zoAAAyVRqsu/MY3vrHv63Xr1tXKlSvr0ksvrc7Ozr71rq6u6u7urk2bNvVb7+zsrO7u7tq2bVsdeeSR1Wg0+q0PxtixR77MVzJ4nZ1HHfQ9+Q9+/wDAYLQsiH/liSeeqDlz5tQnPvGJajQa9eSTT/b7fltbWzWbzQN+7sXWB2Pr1p21f/+B1xkKvym8Nm9+tiX70Z/fPwDwfKNGtb2kN0Nb+kd1a9asqdmzZ9c111xT5557bo0bN662bNnS9/1NmzZVV1fXAeubN2+urq6uOu6442rnzp21b9++fusAADBUWhbEP//5z+uqq66qxYsX1/Tp06uq6tRTT60nn3yy1q9fX/v27asVK1bUpEmT6oQTTqiOjo5as2ZNVVUtX768Jk2aVKNHj67TTz+9Vq5c2W8dAACGSsuOTNx5553V09NTCxcu7Fu78MILa+HChXX11VdXT09PTZ48uaZNm1ZVVYsXL64FCxbUrl276uSTT65Zs2ZVVdUNN9xQc+fOrc9+9rN1/PHH1yc/+clWjQwAQKC25gsd1H0FafUZ4lvmL+u3Nu/mC5xhPUj8/gGA5xuRZ4gBAGCkE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEE8QAAEQTxAAARBPEAABEG1AQd3d3H7D2b//2b0M+DAAAHGwvGsTPPPNMPfPMM/WBD3ygtm/f3vd4y5YtdeWVVx6sGQEAoGUaL/bNa665pr773e9WVdUf/MEf/McPNRr1zne+s7WTAQDAQfCiQXznnXdWVdV1111Xt95660vaYOfOnXXhhRfW5z73uXrd615X1113Xa1Zs6YOP/zwqqr60Ic+VGeeeWatXr26br311urp6amzzjqrPvrRj1ZV1WOPPVYLFiyonTt31umnn1433nhjNRovOjYAAAzYgMry1ltvrQ0bNtT27dur2Wz2rb/pTW960Z97+OGHa8GCBbVu3bq+tbVr19aXvvSl6urq6lvbvXt3zZs3r+6+++46/vjja86cOfXAAw/U5MmT69prr62/+Iu/qIkTJ9a8efNq6dKldfHFFw/yZQIAwAsbUBAvXry47r777ho7dmzfWltbW33nO9950Z9bunRp3XDDDfXxj3+8qqp+8Ytf1MaNG+v666+vjRs31plnnlkf+tCH6pFHHqkJEybU+PHjq6pqxowZtWrVqvrd3/3d2r17d02cOLGqqs4777y6/fbbBTEAAENmQEG8cuXK+ta3vlXjxo0b1MVvvvnmfo+3bt1ab33rW+umm26qI444oubMmVPLli2rI444ojo7O/ue19XVVd3d3bVp06Z+652dnS94xwsAAHipBhTExx9//KBj+IWMHz++7rjjjr7Hl156aS1fvrymTZt2wHPb2tr6Hc94/vpgjB175OAHfZk6O4866HvyH/z+AYDBGFAQn3HGGbVo0aL64z/+4xozZkzf+m87Q/zrHn/88Vq3bl1NnTq1qqqazWY1Go0aN25cbdmype95mzZtqq6urgPWN2/e3O/s8UBs3bqz9u8/MKyHwm8Kr82bn23JfvTn9w8APN+oUW0v6c3QAQXxP/zDP1RV1apVq/rWBnKG+Nc1m8265ZZb6q1vfWsdccQR9dWvfrXOPffcOvXUU+vJJ5+s9evX1+te97pasWJFnX/++XXCCSdUR0dHrVmzpk477bRavnx5TZo0aVB7AgDAixlQEN9///1DstlJJ51Ul19+eV100UXV29tbU6ZMqXPOOaeqqhYuXFhXX3119fT01OTJk/uOUSxevLgWLFhQu3btqpNPPrlmzZo1JLMAAEDVAIP4rrvuesH1yy67bECbPD+oZ86cWTNnzjzgOWeccUbde++9B6yfdNJJtWzZsgHtAwAAgzWgIP7Xf/3Xvq/37NlTa9as6ffJdQAAcKga8AdzPN/TTz/dd29hAAA4lI16KT903HHH1YYNG4Z6FgAAOOgGfYa42WzW2rVr+31qHQAAHKoGfYa46pcf1OHIBAAArwSDOkO8YcOG6u3trQkTJrR0KAAAOFgGFMTr16+vK6+8sjZt2lT79++vY489tj7/+c/XiSee2Or5AACgpQb0R3U33XRTvf/976+HHnqo1qxZU1dccUXdeOONrZ4NAABabkBBvHXr1jr33HP7Hp9//vm1bdu2lg0FAAAHy4CCeN++ffXMM8/0PX766adbNhAAABxMAzpDfMkll9Sf/umf1llnnVVVVd/85jfrPe95T0sHAwCAg2FA7xBPnjy5qqr27t1bP/7xj6u7u7vOPPPMlg4GAAAHw4DeIZ47d27NnDmzZs2aVT09PfWVr3yl5s2bV3/zN3/T6vkAAKClBvQO8bZt22rWrFlVVdXR0VGzZ8+uzZs3t3QwAAA4GAb8R3Xd3d19j7ds2VLNZrNlQwEAwMEyoCMTs2fPrne/+931jne8o9ra2mr16tU+uhkAgFeEAQXxBRdcUKecckp973vfq8MOO6ze97731e/93u+1ejYAAGi5AQVxVdVJJ51UJ510UitnAQCAg25AZ4gBAOCVShADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABCtMdwDcOg65ujDq72j/79Ce3p6a/uO54ZpIgCAwRPEvGTtHY26Zf6yfmvzbr5gmKYBAHhpHJkAACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACBExOT0AAAgAElEQVSaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIFpjuAc4VLz6qPYaPaZjuMcAAGCICeIBGj2mo1bOuqzf2tlfvGuYpgEAYKg4MgEAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABAtJYG8c6dO+ucc86pn/3sZ1VVtXr16poxY0ZNmTKlbrvttr7nPfbYY3X++efX1KlTa/78+dXb21tVVRs3bqyZM2fWtGnT6oorrqhdu3a1clwAAAK1LIgffvjhuuiii2rdunVVVbV79+6aN29eLVmypFauXFlr166tBx54oKqqrr322rr++uvrvvvuq2azWUuXLq2qqhtvvLEuvvjiWrVqVZ1yyim1ZMmSVo0LAEColgXx0qVL64Ybbqiurq6qqnrkkUdqwoQJNX78+Go0GjVjxoxatWpVbdiwoXbv3l0TJ06sqqrzzjuvVq1aVXv37q2HHnqopk6d2m8dAACGUqNVF7755pv7Pd60aVN1dnb2Pe7q6qru7u4D1js7O6u7u7u2bdtWRx55ZDUajX7rAAAwlFoWxL+u2WwesNbW1jbo9cEaO/bIQf/My9XZedRB33MkGe7XP9z7AwCHloMWxOPGjastW7b0Pd60aVN1dXUdsL558+bq6uqq4447rnbu3Fn79u2rww47rG99sLZu3Vn79x8Y14M1mMjavPnZl73foeA3/U4O1usf7v0BgJFl1Ki2l/Rm6EG77dqpp55aTz75ZK1fv7727dtXK1asqEmTJtUJJ5xQHR0dtWbNmqqqWr58eU2aNKlGjx5dp59+eq1cubLfOgAADKWD9g5xR0dHLVy4sK6++urq6empyZMn17Rp06qqavHixbVgwYLatWtXnXzyyTVr1qyqqrrhhhtq7ty59dnPfraOP/74+uQnP3mwxgUAIETLg/j+++/v+/qMM86oe++994DnnHTSSbVs2bID1k844YS6++67WzofAADZfFIdAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0QQxAADRBDEAANEEMQAA0RrDPQAMxKuPaq/RYzqGewwA4BVIEHNIGD2mo1bOuqzf2tlfvGuYpgEAXkkcmQAAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgmiAGACCaIAYAIJogBgAgWmO4BxiJjj6mozra24d7DAAADgJB/AI62ttr9l0f6bf2hcs+NUzTAADQSo5MAAAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEE0QAwAQTRADABBNEAMAEK0x3APw0h1z9OHV3tH/H+Gent7avuO5YZoIAODQI4gPYe0djbpl/rJ+a/NuvmCYpoGDy/8QAjBUBDFwSPI/hAAMFWeIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIgmiAEAiCaIAQCIJogBAIjWGO4BAIBDyzFHH17tHf0TYk9Pb23f8dwwTQQvjyAGAAalvaNRt8xf1m9t3s0XDNM08PI5MgEAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABANEEMAEA0QQwAQDRBDABAtMZwD8Ch4dVHtdfoMR3DPQYAwJATxAzI6DEdtXLWZf3Wzv7iXcM0DQDA0HFkAgCAaIIYAIBoghgAgGiCGACAaIIYAIBoghgAgGiCGACAaIIYAIBoPpgD4BB0zNGHV3tH//+E7+npre07nhumiQAOXYKYEefoYzqqo719uMeAEa29o1G3zF/Wb23ezRcM0zQAhzZBzIjT0d5es+/6SL+1L1z2qWGaBgB4pXOGGACAaIIYAIBoghgAgGiCGACAaIIYAIBoghj4f+zdZ0AUV9828GspS+9NBLHEShEREFAC9l5jiyVYotFojBpbjCUxsUeNLcEWW8QSrIiKYEUEG0ZFEdCIoCJVRUA6837Iu3sveidPbh1Yda7fJx2R/9nd2ZlrzpxzhoiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0tTy6OaAgADk5ORAS+uv8t9//z1SU1MRGBiI0tJSDB8+HEOGDAEAREdHY9GiRSguLkaXLl0wefJkdTSZiIiIiN5T1R6IBUHAvXv3cObMGWUgzsjIwOTJk7F//37I5XJ8/PHH8PLygr29Pb755hv89ttvsLW1xZgxY3D27Fn4+/tXd7OJiIiI6D1V7YH43r17kMlkGD16NHJycjBgwAAYGBjA29sbpqamAIBOnTohLCwMLVq0QO3atVGrVi0AQI8ePRAWFsZATERERESiqfZA/Pz5c/j4+OC7775DUVERAgIC0KVLF1hZWSl/xtraGjdu3EBmZuYr2zMyMv6nehYWhqK1/d+ysjKq9pqs//bUJ/WS+ucv9ddP6sX9j95V1R6I3dzc4ObmBgDQ19dHv379sGjRIowdO7bSz8lkMgiC8Mr/l8lk/1O9nJx8VFS8+nv+yZt+obOy8t7o//9bf9fOqqj/v7wnb1r/XXn/Sb2qc/9/G0n99ZN6cf+jt5WGhuy1OkOrfZWJK1euICYmRvl3QRBgZ2eH7Oxs5bbMzExYW1vDxsbmv24nIiIiIhJLtQfivLw8LF26FMXFxcjPz8eBAwfw448/IiYmBk+ePEFhYSHCw8Ph5+cHV1dXJCcnIyUlBeXl5QgNDYWfn191N5mIiIiI3mPVPmSiTZs2uH79Onr37o2KigoMHjwY7u7umDx5MgICAlBaWop+/fqhadOmAIDFixdjwoQJKC4uhr+/Pzp37lzdTSYiIiKi95ha1iGeNGkSJk2aVGlbjx490KNHj1d+1sfHByEhIdXVNCIiIiKSGD6pjoiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkTUvdDSAieheZGOtBrlP5EFpSXIbc54VqahEREb0uBmIiotcg19HCwll7K237ZkE/NbWGiIjeBIdMEBEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaRpqbsB6mZmIoeWXEfdzSAiIiIiNZF8INaS6yB26ahK29ynb1JTa4iIiIiounHIBBERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSZrkn1RHRETvHhNjPch1Kp/CSorLkPu8UE0tIqJ3GQMxERG9c+Q6Wlg4a2+lbd8s6Kem1hDRu45DJoiIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjStNTdAHr7GJvoQEcuV3cziIiIiKoFAzG9Qkcux/AtEytt2zpilZpaQ0RERFS1OGSCiIiIiCSNgZiIiIiIJI1DJojotZgY60GuU/kQUlJchtznhWpqERER0ethICai1yLX0cLCWXsrbftmQT81tYaIiOj1ccgEEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRoDMRERERFJmpa6G0DSZmYih5ZcR93NICIiIgljICa10pLrIHbpqErb3KdvUlNriIiISIo4ZIKIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkTUvdDaB/x9RIDm1dHXU3g4iIiOi9w0D8jtDW1cHRgBGVtnXdvkVNrSEiIiJ6f3DIBBERERFJGgMxEREREUkaAzERERERSRoDMRERERFJGgMxEREREUkaAzERERERSRqXXSOitx7X4SYioqrEQExEbz2uw01ERFWJQyaIiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjSGIiJiIiISNIYiImIiIhI0hiIiYiIiEjS+KQ6IqK3HB9dTURUtRiIiYjecnx0NRFR1eKQCSIiIiKSNAZiIiIiIpI0BmIiIiIikjQGYiIiIiKSNE6qkzgzEzm05Jy9TkRERNLFQCxxWnIdxC4dVWmb+/RNamoNERERUfVjICYiIqK/xXWwSQoYiImIiOhvcR1skgLJBGIjY13o6miruxlERERE9JaRTCDW1dHG4OlBr2zfuXSIGlpDRERERG8LyQRiIno3GJvoQEcuV3czKuEYSiKi9xsDMRG9VXTkcgzfMrHStq0jVqmpNX/hGEoiovcbH8xBRERERJLGQExEREREksZATERERESSxkBMRERERJLGQExEREREksZVJoiIiP5HJsZ6kOtUPoWWFJch93mhmlpERG+CgZiIiOh/JNfRwsJZeytt+2ZBPzW1hojeFIdMEBEREZGksYeY6B3FW7ZERETiYCAmekfxli0REZE4GIipWhgZ60JXR1vdzSAiIiJ6BQMxVQtdHW0Mnh70yvadS4eooTVERERE/8FATERERO8UzqEgsTEQExER0TuFcyhIbFx2jYiIiIgkjYGYiIiIiCSNQyaI6P9kaiSHtq6OuptBRERUJRiIiej/pK2rg6MBIypt67p9i5paQ0REJC4OmSAiIiIiSWMgJiIiIiJJYyAmIiIiIknjGOK3kLGJDnTkcnU3g4iIiEgSGIjfQjpyOYZvmVhp29YRq9TUGiIiIqL3G4dMEBEREZGkMRATERERkaQxEBMRERGRpDEQExEREZGkMRATERERkaQxEBMRERGRpHHZNSIi+p+ZGOtBrlP5FFJSXIbc54VqahER0etjICYiov+ZXEcLC2ftrbTtmwX9qqSWqZEc2ro6VfK7iYgABmIiInrLaevq4GjAiErbum7foqbWENH7iGOIiYiIiEjS2EMsEUbGutDV0VZ3M4jo/2BsogMduVzdzSAikhQGYonQ1dHG4OlBr2zfuXSIGlpDRH9HRy7H8C0TK23bOmKVmlpDUsMLMpIqBmIiUhszEzm05JwsRfS24AUZSRUDMRGpjZZcB7FLR1Xa5j59k5pa8xf2kBERSQ8DMRGRCvaQERFJD1eZICIiIiJJYyAmIiIiIkljICYiIiIiSeMYYiIiIqL/gYmxHuQ6lSNUSXEZcp8XqqlF9KYYiImIiIj+B3IdLSyctbfStm8W9FNTa0gMDMREr6k6ewhMjeTQ1uV6vURERFWBgZjoNVVnD4G2rg6OBoyotK3r9i1VUouIiEhqOKmOiIiIiCSNPcRqxkfXEhEREakXA7GavY2PriVp46OLqw8viInodXCVC/ExEBNRJXx0cfXhBTERvQ6uciE+BmIiIqJ/wFVeiN5/DMRERET/gKu8qBcvSKg6MBATERHRW4sXJFQduOwaEREREUkaAzERERERSRoDMRERERFJGscQExHRW4PrYP87YqxDy3Wwif6DgZiIJIuB4O3DdbD/HTHWoeU62ET/wUBMRJLFQEBERAADMRFVAyNjXejqaKu7GURERP8VAzERVTldHW0Mnh70yvadS4eooTVERESVMRATEdE/ktKTwjipj0iaGIirCW8ZE9G7SkpPCuOkPvXiBQmpCwNxNeEtYyKiyrjKB72MFySvktIdGnViICYioir1T3fIuMoH0T+T0h0adWIgJqL3HocsqdfbeodMKj3U3P+J/m8MxET03ntbAxmpl1TWoeb+/2Y4ZEEaGIiJ3jKcVPL+YQ8dvQkGMvXikAVpYCAm+heq84RUnZNKpHLLWN3YQ0dvgoGMqOoxEBP9C1V1QqquQMpJTURERH+PgZhIjaprDCN7KIneDRwyRaQeDMREL+EJiYjURUrr8HLIFr1NGIiJXlIVJyROqqK30X8LJCVlJZBr8YKQqt7buMoHO0Ski4GYqBpwyAK9jf4ukEilh7K6/N0FcXlpCTS13//wpe4Ogb+rX1RcirznRZW2SamHnip7JwLx4cOHERgYiNLSUgwfPhxDhjBEEBH9W+oOJOqm7tf/TxfEb1sPaVVQd4fAP9XPQ9F/+R/qwx5q9XnrA3FGRgZ++ukn7N+/H3K5HB9//DG8vLxQv359dTeN3iFS76EhaVN3IFE3qb9++u8qykphZWWktvp/N4aaPdTq8dYH4ujoaHh7e8PU1BQA0KlTJ4SFheGLL774V/9fQ0Om/LOlmcF//Rm5scUr2ywNzV/Zpmf56s+ZmOr/Y81Kv5P11VZfV0cbXy46+Mr21TN7I27djErbXMYuee9eP+uzPuuzPuu/9LNa2tVy/Dc01IHO39yhqK7zj5S87nsgEwRBELktolq/fj1evHiByZMnAwCCg4Nx48YN/PDDD2puGRERERG9DzTU3YD/y3/L6zIZr4CIiIiISBxvfSC2sbFBdna28u+ZmZmwtrZWY4uIiIiI6H3y1gfili1bIiYmBk+ePEFhYSHCw8Ph5+en7mYRERER0XvirZ9UZ2Njg8mTJyMgIAClpaXo168fmjZtqu5mEREREdF74q2fVEdEREREVJXe+iETRERERERViYGYiIiIiCSNgZiIiIiIJI2BmIiIiIgkjYGYiIiIiCSNgZiIqJpVVFSouwmkRiUlJSgqKlJ3MySPi2yRKskG4vLycnU3Qe3UeTBQ9/svCMIrr7863w/V+qWlpdVW922hzkD43z7n6vzsy8vLoaHx16E3Jyen2uqqUncgV3d9dQehuXPnYtq0aWprh7rf/5eP/9X9PijqyWQyPHr0CAUFBWqpD0AtF0aK+m/L/qfu76OC5nffffeduhtR3SoqKqCpqQkA2LlzJ/T19aGvrw8trep5TokgCJDJZACAsrIy5cmxOqmelNVRW/H+x8TEoKysDGZmZtVWX/H+y2QyxMfH4/Lly6hdu3a1ff4AlPWjoqJw5MgRuLq6Vmv9iooK5T74T9uqgiAI0NDQQFZWFh48eAALC4tqqQv89RoV+/2VK1dw584d2NvbK/fH6qChoQFBEPDVV1/h8uXLuHDhAnR0dGBvb1/ltVX3/YyMDGRkZKCiogL6+vpVXvvl+teuXcP+/fshCAJq1qxZbfuA6vH30aNHMDY2rpa6wH+Ou+3bt8f69etRVFQENze3aquvIJPJkJCQgEuXLkEmk8Hc3Lzaaqse/2/fvg0rK6tq++wVFPUuXbqEiRMnon379jA1Na2W2qrHoISEBOzatQsODg7Vuh8qvn+nT59GWVkZbG1tq6226uefnJwMMzOzav/8/44kA7FMJkNmZibGjRuH7OxsAICLi0u1nBRVD8YRERHYvHkz9PX1YW1tXa2BXENDAxkZGVi4cCGePXsGW1tb6OnpVUt9DQ0N5Ofn48svv8StW7fw4sULNGnSBNra2tVSX/H+7969G6tWrUJJSQnKysrQoEGDaqmvcO7cOSxYsAATJ06ElZVVtdaWyWRITExEREQE7t69iyZNmlTbQUkmkyEyMhLz5s3DrVu3YGNjg5o1a1ZLbUX9DRs2YOvWrSgvL4dMJkPt2rWrrX5hYSG+/vprNG3aFAEBAZg7dy6cnJzQuHHjKr9IVQ0CkyZNQlxcHE6fPg1BENCoUaNKxyexqV6EHzx4EGvXrkX9+vVhamqKOnXqKH+uKtug+rs3btyI+fPnIzU1FWZmZrCxsamSmqoUr//q1atISEjAnj17YGNjA0dHxyqvrero0aP48ccfIZfLYWRkVOn9r2oaGhrIycnBxIkTce7cOQiCgCZNmlRLbdWL/sjISCxbtgy9evVC27Ztq6U+8J/vYGBgIPbu3Yu4uDhoaWmhXr160NXVrZY2hIaGYsmSJXByckJmZiZcXV2rpS7w1+dfUlKCadOmISwsDADQuHHjaqv/TyQTiF8+yO7atQtNmjTB9OnT8ezZM1y7dg03b96Ek5NTlbUhNzdXucNv3rwZ+/btQ9u2bWFiYoI6depUW2+xTCbDnTt3MGbMGPj7+0Mmk8HZ2RkymazK6j948ADR0dHK0Ll8+XLY2dlh3rx5EAQBKSkpePjwIRwcHKrshKj6e1NSUrB582bs2rULTk5OKCkpQUREBGxsbGBkZCR6baByIMjPz0dISAgSEhIwePBgGBoaVmuv/cmTJ7Fw4UL4+flhzZo1iI+PR7t27aolDGVnZ2Pp0qVYtmwZPDw8oKWlhZSUFMhksip57wVBwJ49e5T7eFJSErZv3449e/bAwcEBL168QEREBJydnavkovjl/bmsrAxxcXFo0aIFVq9eDT8/P/Ts2RMnT55E/fr1lT2oVSUsLAxBQUGYNWsWBg4cCAMDA6xZswaurq5VFgpjYmJw6tQpNGvWDKWlpTh8+DCGDx+Opk2b4u7duwgNDcWtW7fg7u5eLfvgxYsXcfnyZcyePRsXL15Eeno6LC0tYWFhUSW1VYWGhmLZsmX49ttvUa9ePWzduhVNmjSp0otC1SBYWFiIn3/+Gd988w08PDyQnZ2Ns2fP4vnz51V2YZieno6nT5/CxMQERUVF+Pbbb9GnTx+MGjUKurq6yM7OhoaGRpV2yqj2TAKApaUlLl68iNLSUjg7O8PAwKDKar/s4MGDOHbsGNatWwcDAwNkZ2fjwYMHcHZ2rpJzgGLfFwQBeXl5+PnnnzF79my0aNECOTk52L59OywtLavlorCkpASzZ8+Gh4cHxo0bBx0dHRQWFkJDQwM6OjpVXv+fSCIQq96iUJyczp8/j9u3b2PVqlXIy8tDfHw8UlJSYG5ujlq1aonehujoaJw/fx6NGjVCQUEBoqOjMWHCBOjo6OD27dv47bffcOPGDbRq1apKTggv3w4/d+4cHB0dMXDgQCQkJGDfvn24cuUKjI2Nq+RLkZ6ejk2bNiEtLQ25ubkAgLS0NGzevBl//vknrly5gvXr16Njx45Vcvvu5bCpp6eHI0eOYNeuXTh16hQePXqEiIgIvHjxAi1bthS9vmKYTmlpKajzWb4AACAASURBVM6cOQNzc3M0b94cmZmZuHTpEtzd3aGnp1dlwxZe/r3bt2/H9OnTIZPJEBMTg+HDh6O8vLxKhq4o7kgo6t67dw9xcXHQ0NDAnj17EB0djZMnTyI7Oxuenp6ih1KZTIZly5YhLCwM9+/fR/PmzRETE4OgoCBcunQJqampOHjwIIqLi9GiRQtRa6vud1FRUUhKSkLNmjURHh6OoKAg9OrVC6NHj8aNGzfw22+/oW/fvqJ//i/v+7///jvCwsIwdepUGBkZoV69ekhPT0dOTg6aN29eJRek+fn5OHHiBEJCQqChoYHU1FSEh4dj7969kMvlMDExQXR0NDw9PWFiYiJqbeA/+2BeXh5mzJiBa9euwdbWFt27d4e9vT3Onz+P58+fw8bGRvRb1y+//1evXoWtrS26deumHCr1008/wdfXt0pu26sGwfz8fOjq6iIqKgrnzp3DwYMHkZGRgadPnyIiIgIdOnQQvZeytLQU27dvR1ZWFvLz86GhoYHg4GDUqlULe/fuxdWrV/HLL7/gxYsXcHFxgVwuF33/U/38x48fj+vXr6O8vBxDhgzBnj17oKGhgXr16kEul4taV+Hl4+/Zs2dhaGiIdu3aKTPBqVOnoK2tjQ8++ED02orPPzc3FyYmJkhLS8PSpUsRFRWFsrIyZGVl4dSpU+jevbvox9+XX3thYSGCgoLg4eGB0NBQxMbG4qeffkJBQQEcHR2ho6OjtiEU730gVj0Zr1y5EufOnUNsbCz69u0LIyMjdOvWDQEBAejQoQNOnDgBHx+fKgmEWVlZOHv2LHbu3Ak7Ozvl7bLTp0+jdu3asLe3R1paGpydnWFoaChqbdUDYk5ODrS1tVFcXIzFixdjz549MDc3h4WFBYqKimBlZSXq7bPNmzfj2bNncHd3R3h4OA4ePIjOnTvDz89PGYD69++PQYMGITU1FW5ubqIHYtUhIt9++y0eP36MpKQkjB07Fjo6Ohg8eDD69++POnXq4MKFC2jTpo3ow1cUkzeGDRuG+/fvY+3atejWrRvq1auHxMREJCYmwsvLq8rCsOI7cO3aNdSoUQO7d+9GfHw8IiIiMGfOHDRr1gz9+vVDp06dRO+lVbymJUuW4NatW5g5cyYKCgqQlZWFjh07YuzYsTA1NcWDBw/Qpk0b0eoKgqAMI82bN8fatWuRnZ2NTz/9FLVr14aJiQkGDx6Mfv36wdbWFvfv30fLli1F7aFR/K6tW7di+/btCAoKQvv27aGlpYW8vDx4e3vDzMwMgYGB+OCDD9CyZUtR9wHFybC8vBw3btyAoaEhWrRogXv37uHq1ato06aNcjyhjo4O3NzcRK2vCNdaWlpYt24d7t+/j0GDBqF///5wdHTEoEGD0KVLF5ibmyM6Ohrdu3cXtZcwOzsb+vr6ymFys2fPRsOGDdG4cWOsWLECvXv3Rr169WBgYKC8fduoUSPRQoHi/S8qKsKDBw9gamqKpKQkxMXFoU2bNtDU1ISLiwuCgoLwxx9/oFOnTqIee1SD4JQpU3D06FHUqFEDvr6+sLW1Rf/+/TFw4EDUrl0bSUlJotcvKyuDtrY2LC0t8e2332L//v0YN24c7O3tcezYMbi7u2PkyJHw8vJCTEwMOnfuXCVDB2UyGR48eIDPP/8c3t7esLW1xZYtW+Dp6Ql/f39s3boVBgYGqFOnjqiBUHXM/IMHD5CQkABbW1sIgoArV67Azs4ONWrUwAcffIDdu3fjxYsXsLGxgaWlpWhtkMlkePbsGUaMGIGTJ0/i2bNn6NixI7y9vTFgwAB06dIF7u7uOH/+PHx8fETtKVc990RERCAnJweamppo0qQJNm7ciPr162PQoEHw8fHBpUuXquzz/7fe+0CsOLh/++23ePr0Kb788kuMHTsWdnZ26Nu3L2JiYnD58mWsXr0a9vb2GDBggGi1VWeympqaIigoCI8fP0aXLl0wZMgQtGvXDn379kWrVq2Ql5eHo0ePonv37qLfulHskFOnTsXFixexbt06+Pn5YdiwYRg2bBjatGkDDw8PbNy4EU2aNEH9+vVFq21qaopmzZopvwi2tra4ePEi/Pz84OPjg+fPn+Py5cvYsGEDKioqMHjwYNFqA/85IP35558YO3Ys+vTpg+LiYgQGBqJHjx5o1aoVfv75Z/zxxx/YsmULxo8fXyXj6Z48eYKpU6di+PDh+Oqrr7Bt2zZcu3YNffv2hZmZGSIjI/HkyRM0bdpU1LqKA1JJSQl27tyJZcuWwc/PD+bm5li1ahWCg4NRu3Zt3Lx5E3fu3MHAgQNFC4SqPY2xsbE4cOAAatSogXbt2qFZs2Zo1aoVYmNjUVxcjF9++QUffvihqEOWFEOAzpw5g5UrV2L06NHYs2cP6tatC19fXzg4OGDbtm04e/Ys9u/fj0mTJsHa2lq0+oqekZUrV+LSpUsIDAyEhoYGjhw5gk8++QTFxcU4ffo09u3bhxo1amDWrFmiXxApTsRjx47F48ePERERgfz8fAQEBOD333/HH3/8AQMDA2zevBkdO3ZEvXr1RKmrGgQKCgpgbGwMDw8PODg4IDw8HNbW1nB2dsbPP/+MiIgIbNmyBSNGjICLi4to9R8/fozvvvsObdu2RXFxMXbs2IFLly5hxYoVcHV1RWFhIRYsWIBhw4bBwcEBcrkc7u7uol6QK449n3zyCS5duoRjx45hxowZ2LJlC9LS0mBmZobbt28jPT0dH3/8sajHXkX9nJwczJs3D87OznB2dsb8+fPh7+8PX19fHDp0CGfOnMGvv/6K3r17i/r9U+2ZvHTpEmrUqIGCggI0bNgQrVq1Qvfu3ZGdnQ1jY2MEBgbC2toarVu3Fq2+Yh9UnIdPnTqF+vXrY9SoUQgJCYG1tTWOHDmCXr16wcLCAmFhYWjXrp2oc1kU3+fz589jypQpiImJUd6JLi0txfnz52FiYoLTp08jNTUVRUVFcHBwQN26dd+oruqxNzc3F/v27YOzszN8fHzwxx9/IC8vD35+fggJCcGmTZuwZcsWjBo1SrTvn4JqZ8jJkydhZWWFL774AqNGjcKoUaOgpaWlPA7LZDK0b99erRPs3ttArLpDlJSU4Nq1axg3bhw2btwIOzs7fPrpp7h9+zbMzc3x5MkTNGzYEOPHjxe9vkwmQ0pKCsrLy9G5c2dYWFggKioKmpqaaNy4MSZNmoSzZ8/iwIED+P7770U7Iam2QxAETJs2DQ4ODpg1a5ZyMlH79u1x8uRJzJ49G9u2bUPXrl3Rv39/UeoqeubMzMxw/vx5BAQEYOzYsejYsSNu3LiBiIgIdOrUCdHR0cjNzYWFhQXmzp0rSm3gr4NfTk4O7OzsAAA3btyAs7MzfH19sWzZMkyZMgUWFhaIj4+HhYUFnj59ikmTJokWSF++TVpRUYE7d+7Az88Phw4dQteuXZGYmIgzZ86gTZs2qFWrFnx9fUUfQ6cIRCNGjICpqSl0dHQQHh6OwYMHo6KiAoGBgbh//z62bduGfv36wdnZ+Y1rqu77jx8/Vh7kjY2Ncf36dVRUVKBRo0YoKyvD+vXrcefOHfTv3x89evQQ4RX/NWFILpfD1NQUu3fvxvHjxzFkyBB07NgRjo6OmDRpEnr27AkrKyvk5eXhyZMnmDdvHhwcHESpr/r6gb8m77Rs2RKurq7w9vbGrVu3sHfvXkyePBm9e/eGp6cnevXqBUD81V/y8vKwePFijBw5Ep07d8aaNWuUFySOjo5YuXIlbty4gRUrVsDd3V20uorXHhISglWrVilPhi4uLsjIyEBsbCz8/f2Rm5sLFxcX9OnTB15eXqLVLy8vh4mJCTp06IDY2Fhoa2vD2toaiYmJSE5Oho+PD1q1aoUrV65gxYoVGD58OD744APRh2vcvXsXO3bswIABAzBt2jSsW7cOKSkpWLBgAY4fP47o6GiEh4djypQpor1+1VvUOTk5CAwMxIMHD7BgwQI4OjqioqICmzZtwocffognT56grKwMQ4YMgZ+fnyj1FWQyGYqKirBw4ULEx8djzpw5KC0txe+//w57e3vUrFkTW7duxenTp1G/fn1MnTpVtNqq3yPFd/HevXvIy8tDSkoK5HI5unfvjtOnT2Pr1q2YOXMmOnXqJNrxNyYmBmZmZpDL5Th79iy2bt2KVatW4aOPPsKZM2egq6uLJk2awNDQECdPnsSjR4/wyy+/IC4uDrq6um90YaKafQ4dOoTNmzfj2rVrGD58ONzd3ZVDRLOysuDh4QE9PT2MGjUKHh4eorz2lyUkJODy5ctYs2YNTpw4AQDo0KEDKioqEBQUhD179sDU1BQ//PCD+lebEN4zFRUVyj8/efJEyMrKEgoKCoShQ4cKffr0EVauXCkIgiDcvXtX6Nq1q1BWViZ6G8rLy5V//vXXX4W2bdsKw4YNE7Zt2yaUl5cLK1asEBYvXixkZWUJJ06cEBISEoQnT56IVl/1PVBYsWKFkJiYKIwZM0ZYvHixUFBQIMyePVsoKysT4uLihPj4eNHqK15/ZmamkJOTIwiCIGzYsEEYOHCgkJmZKaSkpAiTJk0Sxo4dKyxcuFC0uqqio6MFJycnYc6cOUJsbKwQHh4utGjRQujevbtw5coVQRAEYc6cOcLRo0dFr626T8XGxgqJiYlCWVmZcO3aNeHmzZvC3LlzhbKyMuH8+fNCs2bNhJCQEFHrv/z57969W1izZo0gCIKQm5sr7Ny5U5gwYYJQXl4unD17VggODhauXbsmahsEQRCCg4OFPn36CF9++aXyc9i9e7cwZcoUITo6WhCEv94rMb+DmZmZQkREhFBRUSFUVFQIK1euFPz9/YW7d+8q98vAwEChRYsWwtChQ4X79++LVluhvLxcSE1NFUaOHCkIgiAsX75cWL58ufLfS0tLhZYtW76y76seN96ktqqioiJh7ty5wqZNm4SBAwcKe/fuFR4+fCgsWbJEKCkpEU6fPi20b99e+f0Xow0K+/btE4YOHSrk5uYKX3/9tdCnTx8hLy9PSExMFH744QehQ4cOwurVq0Wr9988fvxYmDt3rjBo0CAhJydHOHXqlDBjxgxhx44dyp9ZvHixaPUU+3JFRYWQl5cnTJkyRejbt68QGxsrCIIgFBcXC15eXsLWrVsFQRCEgoICIS8vT/T6giAozykRERFCQECAsHv3buW/zZkzR+jbt69QUFAgWm1BePXYs3nzZqFXr15CcHCwctvSpUuFKVOmCL/99psQExMj6ut/uQ1LliwRli5dKqxZs0Z5Lho9erSQlpYmCIIgfPPNN8LatWv/tv2vIy0tTdiwYYMgCIKQk5MjbN68WXBzcxNSUlIEQRCEixcvCtOnTxe2b98u5OXlCampqcKePXuE77//Xhg2bJhQWFj4xm0QBEHYv3+/MGzYMOHixYtCjx49hAULFgiC8Nc+uG3bNmHy5MnCrVu3RKmlSnEMUbyXly9fFkaPHi3MmDFDmDx5slBeXi7s27dP2Z6nT5+K3obX9d71EKv2TCxfvhyxsbFo164d8vPzcebMGfzyyy/Q1NREUFAQDAwM0KFDhyprw/HjxxEXF4clS5agSZMm+Omnn2Bvb4/OnTsjNjYWS5cuRc2aNUW9MlUds3P9+nVkZmbCxsYG27dvx9KlSzFlyhQEBASgvLwcO3bsQO/evVGjRg1Rlv3KyMiAoaEhZDIZLl++jDFjxuDatWtITEzEF198gWvXriE8PBy9evWCm5sbEhIS0LVrV1FnVysW/K5VqxYuXLiA8PBwTJw4EU2bNkVCQgIcHBwwZMgQ5OfnY/369fD19RV9iITi/Z84cSISExNx8OBB3Lx5Ez169MDhw4ehpaWFDz74AIGBgejduzcGDhwoWm3Vz1/hxIkTuHv3Lrp06QINDQ2Ym5vj4MGDuHfvHoYMGQJHR0fUqFHjjSdTPXjwQNnDFhUVhT179iAoKAhZWVmIiIjAyJEjUbt2bWRlZSEsLAxubm4wMTERrUe0oqIChoaGqFevHg4ePIjdu3fj66+/Rnx8PC5evIi2bdtCU1MTHh4eMDc3h729Pfz9/UWprfreyWQymJiYYPny5Xj06BHGjh2LpUuXQhAEGBoaIjQ0VDmxqX79+sqe6TftHVGdK3D16lUUFhZCS0sLCQkJCAkJwaRJk9C1a1ckJSUhLCwMvXv3Rt26dVFYWIgjR46gc+fOb/RZlJaWVhp/GR8fj/bt2yM6OhpXrlzBokWLEBkZCTc3N3h7e6NmzZoYOnToG73ml6n2DJ49exZTp07Fpk2bEBMTg0uXLqF///6oqKjAuXPnUFpaikaNGsHX11e02orXr1jf2MvLC9euXQMA2NjYwNTUFP7+/hg3bhx8fHxQu3Zt0SZyCSrzZWbOnIl9+/bhzJkzGDduHAoLC3H79m1UVFSgXr16aNOmDZ49ewZvb29RagOVjz0ZGRkwMDCAk5MTUlNTkZ2dDSsrK1hZWaFVq1ZISkpSfifFXl1DJpOhvLwc48ePh5GREdq1a4e1a9dCS0sLrq6u2LZtG9LT07FmzRo4Ozvjiy++eKX9r6u0tBQmJiZwc3PD+vXrkZeXB39/f+VKNu3bt4eDgwOKi4tx8uRJODo6wtzcHHfv3kV5eTkWLVr02mNoVe8MJCYmYsWKFWjWrBkGDhyIDz/8EGvXrkVFRQWaN2+OWrVqwcTERPRJxMBf7//Vq1exY8cO5Ofnw9vbGzt37kRBQQHWr18PmUyG3bt3o0GDBnBxcam2peb+jfcuEAN/3S7ftm0b1q1bhzp16sDExATu7u54+PAh9uzZg+DgYDx9+hTfffedqB+G6g55/fp1zJ07F3Z2dujevTvs7OxgY2ODH3/8ER9++CH69OkDZ2dndO/eXbT6wH9Oqhs2bMCvv/6KwsJC2NjYoFOnTjhy5AgaNWoEMzMzLFu2DACUIelNBQUF4ejRo2jbti1CQkIQExODzz//HC4uLrh58ybu3r2LadOmYd++fbhw4QL69euH1q1bK4c0iEFxQCssLFTeItXQ0MC2bdswcOBA2NvbIzQ0FPv27UNQUBB69OiBjz76SLT6qqFo7dq1MDY2xpw5cxAREQEzMzN06NAB+fn5CA0Nxa5du9CuXTuMHDlStPqqJ+Rly5YhMjISRUVFaNeuHcLCwlBSUqJcbzs2NhaPHz8G8NckIuDNAtmVK1dw8uRJ2NjYKG+V6ujoIDIyEpGRkVi5ciWOHTuG8vJyuLm5wcbGRrQHEhQVFUFLSwsymQypqalISUmBhoYGkpKSkJ6ejkmTJmHbtm1IS0uDj48PAMDR0RHNmjUTpT7wn/cuNjYWBQUFsLCwwMCBAzFjxgzY2dlh9OjROHnyJM6ePYuMjAysWLEC6enpcHZ2Fm0Sr+J7/PnnnyMxMRHHjx9HRkYGrKysoKmpqRzH/9NPP8HHx0d5MvTw8EDXrl1f+/PPysqCIAjQ09NDfn4+5s6dC09PT5w4cQLr1q2Dnp4efvzxR9jY2GDevHlwcnJC7dq1q2Tdbw0NDTx+/BgGBgaoW7cuUlNTkZmZifHjx2PdunUoKipCly5dkJeXhw8++EDUMKahoYGioiKMHz8eSUlJWL58ORo2bAhnZ2ecPXsWmpqasLGxgb29Pbp06SL6Ep8ymQxlZWWYMmUKDAwMMHfuXMyePRuFhYUYM2YMEhMTcfPmTejq6qJWrVqiDpFR1Af+mkC6evVqHDp0CGlpaejYsSNiY2ORn58Pa2tr5fm4a9euVTKBXRAEFBcX4+bNm5g1axZ+/PFHuLq64pNPPsHZs2fRr18/xMfHo0uXLsoLMtWLiTehqamJ5ORk7N+/H5mZmbhw4QIaNGiAFi1aID4+HhcuXIC/vz8aNmwIR0dHNGjQAHp6enB2dn6jITOqc0XKyspgbGwMHR0dHDp0CI6OjmjUqBEaN26M77//Hvb29nBxcXnjccqqHj58CH19fWhoaODo0aNYs2YNfH19YWBggAYNGqBu3bo4deoU/vzzT5w+fRoJCQkYNWpUlawo8ybei0D88ri71NRUPHr0CKWlpThx4gS2bt2KX3/9FTNnzkT37t3h6OiIzz77TLQwLPz/QfsaGhpIS0tDWloanJycYGdnh8jISDRo0ABWVlaoX78+CgoKsHLlSgwYMEDU5d1Uw/jFixfx+++/Y8+ePbC1tcXz58+RkpKCESNGYOvWrbh06RIMDQ2xbNky0Xrnjh07BltbWzRv3hxTpkxBeno6vvrqK9SoUQPa2tq4dOkSCgoKMG7cOFy7dg1+fn5VMoHo+vXrmDhxIm7evAlnZ2cEBATgwIEDOH/+PD755BN069YNLVu2RJs2bdCxY0fR66elpeHZs2fIzMyEqakpVqxYgfr16+Obb77BkCFDMGDAAHTt2hVdu3YVdQIJ8Nf+V15ejm3btiEhIQH29va4ePEi8vPz0alTJ8yfPx85OTlYvnw52rVrh5o1a8LS0lKUceslJSU4ffo0Nm7cCAsLC+jp6WHNmjXQ0dHB2rVrYW5ujuDgYFhYWMDHx0eUpYUEQUBycjLGjBmD1q1bw9DQEIsXL4aWlha6desGQRBw7tw5aGtr49NPP8XMmTNhaWkp6kMAVI89aWlpWL58OQRBgK2tLczMzODv74/PPvsMHTt2xNChQ2FnZwdnZ2cEBgbi4cOHGDZs2Bt9B1NTUxEfH49atWpBEAT88ssvsLCwwJw5cxAUFITatWtj5MiR0NfXR1paGmJjY+Hr64tRo0YBEOchGKtXr8aaNWvw8ccfIzc3F4cPH8aAAQPQtGlT7Nq1C23atIGzszMiIyNx5swZDB48WPSVTBSvo7S0FOPGjcO2bdvg7OwMuVyOJ0+ewNPTE56enpg5cyYaNmyIXr16iXb8VdQuLy/HtGnT4OXlhQkTJmDHjh0wMzND3759oaWlhcOHDyuXuRPr6Vwvf345OTm4evUqpk2bhqioKDg4OGDXrl0wNjZGz549lcdFMSePqgoODsbhw4exZs0atGnTBkFBQTA2NlZelBcWFqJBgwbQ1dUVbTUH1fc/OTkZ33zzDbp164YVK1YgMDAQXbp0wYQJE3Du3DmcO3cOAwcOhK+vr7IjQIyeYYW7d+9i9uzZ8Pb2xujRo/Hnn38iKioKbm5ucHZ2RmhoKLKzs+Hu7q5c4lKMpTZlMhlu3bqFuXPn4vjx48jKykK9evVQr1497Nq1C15eXmjYsCFMTExgZmYm6nrTMTExiIyMRIsWLSCTyRASEoIOHTrA1dUV8fHx+P333/Ho0SPMnTsX6enp0NDQwLx580RdSUMs73wgVsxkzc/Px5o1a5SPAU5OTsb169fRvn17TJs2Dffu3YMgCPD09BT9MYWKQfuRkZH48ssvkZaWhsOHD+Pzzz9HZmYmIiIi0KhRI5ibm8PDwwOtW7cWfWkxmUyG4uJi3LlzB/r6+nj48CGCgoJw9epV3LhxAz///DMcHR0xc+ZM0cJgSUlJpUdgN27cGE5OTujQoQPWrl0LU1NTuLq6wtLSEmVlZQgNDYWXl5dok6deFhMTg0WLFmHKlCm4fv06oqKi0KhRI4wePRoLFizAwYMHkZKSgu7du4v2ZVQNREVFRVixYoVy2MisWbMwefJkjBgxAgBw5MgRtGvXDra2tlW21ur48eNx8+ZNbN68GR4eHigrK8Ply5eVF4GGhoZwdXVFjx49sHz5cri6ur72zHbFhaBiFZWzZ8/i0aNHcHNzQ8eOHZGSkqK8bX/9+nXs378fQ4cOFe1krHiQy4kTJ3DlyhW0adMG4eHhcHV1Rb169WBpaYmKigocP34cderUweDBg9GgQQPRVnFRPfbcuHEDdnZ2aNSoEY4fP16pN/D27dvYuHEjhgwZgoyMDFy/fh1lZWVYvHjxG5+IY2JiMHv2bPj6+sLS0hLJycl4/vw5Nm3aBBcXF4wbNw7fffcd/Pz80K1bN/j6+ip7Bt80CCiCSPPmzREdHY2YmBh4e3vj6NGjaNWqFczNzeHt7Y0dO3YgMjISUVFR+Pbbb9GwYcM3es2qFA900dXVRVZWFoyMjFBRUYHr16/DwsICMTExiIuLg5OTExo0aIDmzZvD0dFRlECueNCS4mEvRUVFePz4MTw8PPD111+jU6dO+OSTTzB16lTlU1E9PT1FC8Oqx54XL15AU1MTZWVlePDgAfT19XHhwgWMGzcORkZGmD9/Plq0aIHevXuL+ojwlzujYmNj4ebmBg8PD5iYmMDZ2RkzZszA8OHDYWRkBCMjoyrpGQdQaTiYkZERGjZsiJycHIwePRrGxsbYsGED9PT04O/v/8oQp9el+vrLy8uVw+Pc3NzQuHFj+Pj4IDIyEklJScr3xdvbu9IQydetX1BQALlcjoqKCiQlJWHmzJn47LPP4OHhgeTkZNy4cQOenp7KFYZ69OgBZ2dn0R++UqtWLXh6euLAgQOQyWRIT09HWFgY9u3bBzMzMzg7O2Pfvn1o37492rZtCy8vr2p7Ku3/6p0PxIrZoxMnToSFhQUsLCzg7e2Ntm3bonv37khPT8eTJ08QHByMjz76SNQwfPToUeTm5qKgoAClpaWYPXs2VqxYAUtLS4SGhiIlJQVTp07FhQsXcO7cObi7u8PAwEC0MKT4UiuuMDdv3oyzZ8/CysoKjRs3hlwuR//+/TF06FDUrFkT2dnZaNasGbS1td/4gJyfn48ZM2ZAJpOhfv36OHz4sHIpKwMDA7i5uWHy5Mlo0aIF6tatC3Nzc3h6eoo6Xlf16TsymQy7du2Cp6cnunfvjsjISAiCgDt37sDd3R1DhgyBhoYGhg0bJuowGcXB8O7du7C2toYgCAgMDMSsWbOQlJSEu3fvorCwEL/++iuKiorw8ccfV8nT0FJSUpCXlwdnZ2esXbsWzZo1U661W1hYiP3798PJyQnm5uY4fPgwVq9ejaFDh772kB1FmJLJZLhy5QoyMjLQrVs3ODk54cyZMygpOm6BhgAAIABJREFUKcGnn36KlJQUJCYm4saNG5g3b55oj6idPn06QkJCcOjQIYwZMwbHjx/H48ePkZOTg8TEROjp6SnX1c3IyEBWVhbatm0r6pKGiiD06aef4tGjR9i4cSOGDRsGQ0NDnDhxAmVlZfjzzz+Rl5eHCRMmoF69eqhZsyZcXV3x4YcfvlHt4OBghIeHw9bWFhoaGti3bx/8/PyQmpqK4OBgdO3aFV988QW0tbWxYcMGtG7dGhYWFsqhJW96i1g1CMjlcnh6eiI4OBj79+9HXl4eoqKi8OLFC9SsWRNdu3ZF9+7d0bNnT1FPxtOnT8exY8ewb98+PH/+HPv378eLFy/QoUMHhIaGom3bttDX10dwcDByc3PRunVr5fHpTW3cuBH379+Hi4sLQkJCsHLlSvTo0QPBwcH46aefsGTJEvTs2ROFhYUIDQ1Fr1690KxZM1EfvKF4/3/77Tds2bIFx48fh4WFBfr27YuwsDDo6urCy8sLp0+fhoODA3r37i1afcWwNA0NDaSnpyMzMxMGBgZITExEUFAQBg0aBACwsLDA3bt3lUt+VcUwGQDYsmULTp06hZYtW6K4uBhFRUXw8fFBXl4evvvuO5w/fx4VFRVYvHixaE+CVB2iFhcXB5lMBm9vbzx79gwpKSkwNjaGra0tvLy8sHnzZtjZ2SlXEnrTXuGbN2/i999/h4+Pj3IloXv37inXeLayssKVK1dQq1Yt5fKmrq6uogbRl1/D8uXLkZubi44dO6Jbt24YPHgwfH19YW1tjfPnz6NLly5V9hRYsbyTgfjl20RhYWGoW7cuRo4ciXv37uHkyZMICwuDTCbD3r17cebMGcyYMQPNmzcXrQ0zZ87EqVOnkJqaip07dyI/Px9OTk5o2bIljhw5gi5duiA2NhaZmZmYMGECHjx4gA8//FDUYQKqS+vo6+vDwcEBV69exbNnz9CyZUu0bNkSx48fR3h4OA4cOIDPPvsMVlZWorUhJycHR44cgY+PD0JDQxEfH4+EhASUl5ejRYsWqF+/PsaOHYvBgwfD0tJS1F5x1TUu09PTYWRkpBwvHRkZiaZNm6JTp05YuXIlEhMT4eHhAT8/P9HCsOrB4MSJExg6dCi0tbXRunVrlJWVKT/35ORkZGdnw9TUFIsXLxYtDCsCSUVFBQoKCrBlyxZcuHABPXv2hIODA2bOnImBAwfC0tIS+vr6kMvlaN68OWrWrAl7e3v06tXrjcasqY5VX7lyJR48eICLFy9ixIgRyM7OxtWrV6Gnpwc7OzsMGjQIHTt2FGW8YGFhIT777DOYmppi7dq1qFOnDjw9PZUTZ/7880+4uLggLi4Oa9asQVJSEvz8/ES7K6H6uaelpWHatGn46quv0K1bNwQHByM6OhqTJ09GUVERLl++jGPHjmH8+PFwd3evNLTqTSxcuBDnzp1Ds2bNcO/ePXz11Ve4cuUKzp8/jwEDBuD27dvQ1NTE/fv3ERQUBEEQMHjwYGhqaorSK6YaphctWoTo6Gg8fPgQAQEB2Lt3Lzw8PDBw4EDcunULy5Ytg52dHdzc3ESbPKa6D6xevRouLi5o2rQpateujaVLl6J+/frQ1NTE48ePMX78eBQUFKBp06ZwdXUVpf7s2bORnJyMsWPH4tatW9i0aRPatGkDf39/eHt7IzQ0FNbW1pDL5ViyZIly+Tex7Nq1CwkJCXBycsKGDRsQHR2N5cuXIzw8HFFRUejbty/Cw8Nx5swZnDlzBs+fP8ePP/4oyoWAYpjS2LFj0aNHD1y/fh2ffvop7ty5gzNnzuDzzz/HpUuXEBoaCnd3dxw4cACnTp1C3759RX0CoGoGKC8vR1hYGHbs2IGaNWsiIyMD586dQ+/evfHhhx/Cz88PrVq1QkBAgHJohRhDJDQ0NFBWVoYJEybg7t272LRpE/Lz89GqVSskJyfjwYMHsLa2Ro0aNdC2bdtKS3q+yfdPce47d+4cNm/ejMLCQlhYWODmzZtwcnKCiYkJzM3NcerUKZiamsLX1xfe3t6i98rKZDLExcUpJ4p/8sknCAoKQl5eHlxdXXHw4EFs374d27Ztw4gRI0TNX1XlnQvEqjtzfn4+tLW1ERcXh6tXr2LJkiXQ19eHlpYW0tPTUadOHYwfPx5du3YVbY3Rp0+fYtq0aTA0NERgYCA8PT2RmZmJ7OxstG7dGjdv3oSBgQG6deuGy5cvIygoCF5eXujZs6doQVT1YJCYmIgpU6agcePGyh6o6OhoZGVlIS8vD0VFRUhNTcWSJUtEew+AvyYPNGjQAOnp6di6dStKS0vh4eGB/Px8hISEYMWKFQD+Cs39+/cX/el7MpkM+fn5+OKLL7Bnzx5oaWnB398flpaWWLp0KQYMGAADAwPExMRgyJAhop0Mgf8EghcvXkBbWxv16tXDrVu3kJ2djX379sHKygolJSVwd3eHp6cnWrVqpZzMJRbFd6CkpAT6+vrQ09NDSkoKbt68iREjRiA3NxeLFi3CsGHDYG5uDhcXF+VtOgsLC1HuUuzfvx/nzp3Dli1bkJeXh71796KgoACjRo3C/fv3sWnTJgBAy5YtRbsQSExMxMOHDzF//nwAf10MXb58GRcuXIC1tTWSk5Px2WefYeTIkWjZsiUaN24s2kxq1SEGFRUVyMvLw/3799G/f3/s2bMHPXv2xL179xAVFYUvvvgC7du3V96iVnxn3/QYsH//fty6dQvr169XDnfR19dXzp43MDBAv379UFhYiKSkJNSoUQPz58+HpqamaI9kVoSKr776Ci9evEC3bt1ga2sLFxcX2NnZYefOnejXrx8++ugjdOvWTfSZ7C/vAw8ePMCdO3eQkJAAOzs7JCcnIy8vD9HR0ejWrRvatm0ryvraz549w/jx43H8+HGsX78eNjY2eP78ObKysvDo0SPUqVMHDg4O8PLywuHDhxEXF4c6deqIur56fn6+8nxnZWWFsrIyfPzxx9i9ezceP36MJUuWICQkBGPHjoVcLkedOnUwZcoU0c49imFKEREROH78OFJTUzFx4kT06tULN2/eRFRUFH744QecPn0aV65cwa1bt7Bq1aoqWUni2bNnOHHiBIyNjdGwYUOcOnUKdevWhZaWFg4dOgSZTAYvLy/lk1iByr26YlCcV2fMmIGQkBBYWlrio48+go2NDaKiopCeno5mzZrBwMCg0t3c11FWVoaQkBA8fvwYjRo1wo4dO5CUlARfX1+0b98ex44dw5MnTwAAWlpa2LJlC9q2bSvq5DnV9icnJ2PSpEnKpzwaGBhg0KBB2Lp1K8zMzFC3bl24urqKvsZ4VVLfM/Jek2JnnjNnjvJKSbGM11dffYVatWqhtLQUEyZMgKmpKTQ0NERb0qy4uBjDhg2DtbU1vv/+ewCAubk59PT0lEM1OnXqhDlz5iA7OxtPnjzBli1bRJtND7z6hbazs1P2js2fPx8NGzZE27ZtsXDhQgQEBGD48OGi1X6ZgYEBBg4cqFxG65dffoG5uTlKSkoQHx+Px48fY8GCBaIuKwT8dTAsKSnBb7/9hlatWuH/tXfnATWm/ePH3+dUR7tUpBRaKFKWlIihlCxZsg3RGMtgbF/71mAY5hl7jJ1sY2xZYpQZZNeQfRljCSUtNJVK0Xr//vB0HvmNZ2YedwrX6z93da7rHOfc53Nf9+f6fGrXrk1wcDB6enrY2NhQo0YNtm3bxrlz5xgyZIhs7YBfDWru3LnDrFmzMDY2ZsGCBbi6umJpaUlhYSHBwcHExcXh6uoqayD+6hzgZd729u3b2b9/Py4uLmRnZ3PkyBG2bdvGpEmT1KVv5Cpt9XrOaWpqKl26dCE2NpaUlBQWLVrE9OnTsbS0ZMCAAbRp00bWfMViERERtGvXjl9++YV79+6pb91aWVlhYWHBmDFjWL16taxd/1793P3444/cuXOHmTNn4ufnx7Vr1ygqKqJFixbEx8ezYMEC6tevT+/evdHV1ZV1087jx4+pWbMmSqVSvSJ49uxZLCwsqFSpEmvXrkWhUNCnT583zv9/8Xow/eLFC4qKipg1a5b6Nvzt27fR0tLiyy+/ZPTo0ezbt6/UNs686T1gbGysvmvw66+/8vTpU/XmpbcRFxfHpEmT8PLywt3dnbFjx7JhwwYcHBzo0KED4eHhHD58GCMjI3XDE0D2VTl9fX3atm1LYWEhoaGh3Lhxg6VLl+Lj48PatWuBl80YfHx88Pf3l3XsiRMnqjdFDR06lNWrV/Pbb78xfvx4jIyMCAwMZPny5YSEhKifv1wXYVDy/JOUlMTOnTvJy8sjLCyMOXPm0KdPHzIzM+nWrRuhoaHcu3ePZ8+elViMkSsYfvToEVpaWlStWhVjY2M+//xzXF1dGTVqFIGBgQQHB9OuXTssLS2pUKGC+u/eNmdfU1OTsLAwDh8+TGBgIPn5+Rw6dAhLS0umTp3Khg0bWL9+Penp6QQEBMj23QclX/+IiAjS0tIYO3YsPj4+REZGsn79eiwtLRk7dixBQUHMnDmz1Jp9lJb3ZoX41XzZr776Cn19faZPn8748eOxsrKiTZs2HDp0iLCwMJYsWYKLiwvdu3eXdQ6ampoYGhoSGRlJgwYNqFKlCvfv32ft2rU0bdqUOnXqkJaWxvbt2wkLC2PIkCGyrgwWXwA8f/6cBQsWcOrUKQoKCrC3tycvL4/9+/fTtm1bDA0NOX/+PF5eXrIn0L9OT08POzs7YmJiSEpKokmTJmhoaFC1alVq1aolywlIkqQSOasRERGsXLmSmzdvMmDAAHW7yQMHDlCvXj0sLS0pKCigb9++b52r+apXO4/NmTOHUaNGcfXqVeLi4sjOziY8PJwRI0aoNw24u7vLunmu+DUorvdqY2PDxYsXOXr0KG3atKFGjRpcvXqV8PBw9PT0CAoKkjUoLC5rlpycTGFhIbVq1aJKlSocOXIEe3t7XFxcOH/+PDt27KBz586yrwoBVKlShfz8fLZu3UpKSgpDhgyhY8eO9OvXj+vXr9O9e3fq169P7dq1Zc1XKy6pFRQURGJiIjdu3ODFixd07NiRb7/9FhsbGxo2bMiGDRuYMGECnTp1Uv+tnGlSGhoaLFy4kMOHD7N7924aNGhA27Zt8fX1JT09nebNm7N582Z8fX3R1dWVPV84JSWF/Px8lEol69atw93dXV3D/Pr16xw9epQRI0bQqlUrTE1NZe26V+y/vQdu376NpaUlXl5e9OrVS7aA/Pnz59ja2tK1a1dcXFzUrcg7deqEubk5BQUF3Llzh9jYWJydnVGpVKWyVwBeBsUGBgZkZmaq6wv37NkTBwcHtm3bxrVr1+jatatsC0GvpynVqFEDV1dXPD092b17Nzo6Ojg7O2NkZISZmRlbt26levXqVKtWTda7osXvpcOHD7NlyxYKCwvp2bOnuoyoubk5KSkp+Pv74+npiaenpywXQ8XjF3+WsrKy1J/5x48fM2vWLIKCgujduzdKpZKwsDA6dOigLnH6topXZTU0NMjLy1O3gA8ICMDR0ZEnT55w7Ngx3NzccHR0pGfPnrRq1Ur2Vdni/8vFixcTGhrKzZs3KSgooEGDBuqNsmvXrqVr1640a9ZM1oXAd6XcB8SvrgqmpaWhq6vLrVu3aN26NUuWLKFOnTr079+f48ePU6dOHapWrYqLi4s6qV9utWvXJi8vjxUrVqCrq8uSJUvo2bMn3bp1A8DNzY3atWsTGBgoy206KFlntfgLwMbGBhMTE65fv87du3fx8vLi0qVLbN68mR9//JEBAwbg7e0ty/h/pWLFilSvXp2dO3eSmZkpa31XeLkyX7zKfPr0adasWUPv3r25f/8+hw4domvXrjg6OhIbG8u+ffvo1q0brVu3pmrVqrKM/3qKSnBwMPXq1aNv3760atWK1NRUsrOzOXjwILm5ufj7+/PJJ5/IuoEGXn4Grly5wvfff8+VK1fIzs6mZ8+ehIWFERsbS9OmTUlISECpVOLt7a3O2ZYrZ+7w4cPMmjWLChUqMGPGDBo1aoSZmRlTp04lKCiIn3/+mezsbObOnVsqK8PF3NzcaN++PX379qVmzZrqL51169Zhbm5O586dS6Ws16xZszAwMGDKlCnUqlWL8PBwDA0NqVu3LsuWLWPnzp107NhRHQzL3YYZwNzcHFdXV0xNTZk4cSLe3t44Ojpibm7O7t27GT58OIGBgSUqGbxtUFL8HDZt2sTcuXM5deoUDRs2xMjIiDlz5uDj44OBgQEbNmxQV5Yo7fqib3oPrF+/HltbW+rUqVMiZ/ptGRgYlFhcKK6mcenSJTw9PbG2tiYhIYFnz57RoEED2e6KvcmrKQBaWlpERkYSHR3N1atXmT9/vqybx9+UpnTu3DkqVarEnj17qFmzJtbW1piYmPDJJ5/g4OAg2/jwn/fwkiVLCAsLo2HDhjx48ICnT58SEBBAjRo1+O2339ixYwdNmzbFwcEBPT09WUqavTp+YWEhOjo63Lt3j8jISPVeouzsbLKzs1m5ciUKhQJ/f39ZPvuv3tnJzMzEwMAAHx8fcnNzuXbtGra2tjg5OREfH8+8efNITEzEy8tL1nxteBmUF+dLK5VK1q5di7m5Obdu3QLAxsYGOzs7EhMTKSwslK3ZzTsna9+7UhQVFSUFBARIJ06ckMaMGSO1adNG3R4xMTFR8vPzk3Jyct7ZfGbPni3Z29tLly5dkiTpZTtWOdueFouJiZH8/f2l5ORkSZIk6cSJE9KIESPUP7969ao0bdo06cSJE1JWVpa0d+9e6caNG7LP468UFhZKx48fly5fvizr4+7YsUOaP3++JEmSdOXKFemLL76QgoKCJEmSpOfPn0sDBw6UJk+eXOL35fR6W+H09HRp+fLlUq9evdTPNT8/X8rLy5NGjx4trV27VtbxXxUVFSW1bt1aCg8Pl5YtWyZNmjRJCg4OlmJiYqQmTZpIAwYMkNq3b69uEfq2Xn3uycnJ0ueffy49fvxYioyMlFq3bi1FR0dLkvSy/Wm3bt2kbt26SU+ePJFl7L+Sk5MjHT16VFqxYoV0+/Zt6dNPP5WmT59eauPl5+dLw4YNU7fgzc/Pl3755RepZ8+e0t27d6UnT55IDx48UP++HC1g/0pSUpK6zevUqVOlgQMHytqKd+vWrdLOnTslSZKk5cuXSwMGDJBiY2OlsWPHSn5+flJWVpYUHBwsDR48WPrss8+kcePGydqG+6+86/fA6x4+fCj5+/tLq1evliSp9L4D3qSgoECKjIyUFi5cKD148EBKTU2V8vLyZB/n+vXrkouLixQVFSXNmDFD6tu3r9S/f39p4MCB0tdffy0FBgZKTZo0ka5duyb72K9+jtLS0qQvv/xS3ZL66tWr0uLFi6VNmzapn/fRo0dLbfwtW7aoW7CnpqZKs2fPlp49eyalpaVJCxYskL755htpyZIlf/q3b2v27NnS0KFDpS5dukhRUVHSmTNnpG+++UbauHGjlJ6eLsXFxUkXLlyQdcw/e6zJkydL7u7u6n9v3LhRmjx5snT48GGpqKhIys3NlW38slBuV4iPHz/O06dPycjI4Pnz54wZM4YvvvgCHx8fcnJyuHbtGp999hk6OjqsWbMGAwMDfH19S+UW3Z9xc3Pjzp07PHr0iFatWqlv58vpwYMH2NjYEBUVxcmTJ/H29iYtLY3ffvsNJycnDA0NMTMzIywsDENDQ1xcXHBwcCi1ouv/jUKhoGbNmrKtyhbXWHR0dKRJkyasW7eODh06kJaWxu3btzE1NcXa2ho3NzfWrFlDYmIiHh4esta4lP59my4zM5Pp06dz+fJlCgsLcXJyQpIkzp49S7169TA0NERDQ4PWrVvj6uoq2/hQchNDREQETk5O9OrViwYNGmBubs4vv/yCl5cX3bp1w8LCgrFjx1K5cmVZVkaKP0v37t1DR0eH6OhocnJy2Lp1KwsXLgRg/vz5zJ07F0dHR0aMGCFrWbP/RlNTk4SEBA4ePMi1a9do2rQpY8eOLbXxlEolMTExhIeH4+fnh4aGBrm5uVy4cIGLFy+quy5Kr9zRKk3x8fGEhISwZs0a9u/fj46ODsuWLUNLS0uWvM1XN2/p6ury+PFjhg4dytOnT3n48CFmZmZs376dWbNm4evrS926denbt+87O//Cu38PvK5ixYrY2toyc+ZMWrRooe7SWNoKCwt59OgR+vr6aGhosG7dOvz8/DAzMyuVNI2/k6bk7OyMvb29rJunX08R09HRITw8nOzsbBo0aICZmRk3btzg6NGjqFQq7O3t1RvI5Lg7Uzx+bm4umpqaxMfHEx0dzaFDh9DW1iYqKoqqVatib29P06ZNadmypTpNQc67QzNnziQlJYUFCxagr6/P+vXrcXd3x9LSkrNnz7Jo0SKcnZ1p1qxZqWzcP3fuHOfOnQOgd+/eHDt2jJMnT9KuXTsaNGhAdHQ0ycnJNGrUqFy1Yf5flMuAeOLEiURGRnLnzh2WLVtGQUEBGhoa3L59Gz8/P3Wu7tGjR9m1axcaGhqylrT6OzQ1NWnSpAkrVqzg0aNHeHh4yPr448aNY//+/YSFhTFp0iQiIyO5desW7u7uHD9+HD09PfT19TE0NFTnEspZ8L6sLV68mCNHjtC8eXM0NTXp378/L168YOjQocTExHDnzh0qV66MtbU19erVQ19fX5buZ/CfhiMKhYKkpCTGjBlDmzZtMDQ0ZPbs2XTq1AkbGxvi4uI4c+YMzZo1Q1NTU7b3X/FuYgcHhxJlgu7evcvt27dp1KgR+vr66OrqEh4eTuPGjbGzs8PGxgaVSvXWG6hePZmfOnWKcePGMWDAAPbu3UtkZCTh4eGYmJhw5MgR9e2xd30RplAoqF69Oh07dqRVq1ayVTL4sy+y4mPW1tZcu3aN7du34+zsTHBwMPb29lSoUEHdCESuGqd/pWLFitSpUwd7e3tcXV3VLcDl+iJWqVRUrVqVjIwMzp07x8OHD2ndujX79+9X5weuX7+e06dP07NnzzK7CC+N98A/YWFhgY+PT6nV1/0zCoWCzZs3s3fvXtauXUvPnj1l3SvxZ/4qTalLly6lUkno0qVLLF++nNjYWFJTU6lSpQrJycnk5eVRs2ZN7t69S2xsLM+fP6dhw4bqgEyOz0BxjfVx48YRGxuLq6sr/fv3JzExkZSUFMLDw8nKyuKTTz4psXFOeouc/T9byDhx4gSDBg3CzMwMBwcHsrOzWb16NVOmTMHBwQFXV1fZUxReLau5bds2qlWrxrp161AqlUyaNIm5c+eSkZFB06ZNcXFxwc3N7Z0thpSmchUQF5c0MzAwYPny5bRr107d9KFfv37ExcXx+++/4+7ujpubGy1btqRp06b06NHjna5MFNPV1aVRo0aYm5tTrVo1WR7z6dOn6l70K1as4OLFiyQlJTF16lTmzZtH7dq1qV+/PpGRkRw8eJDt27djbW3N4MGDZRm/rBVfmTZv3pzx48cTHBxMQEAAAQEBjBs3DktLS7p160Z0dDQ3b97Ezs6OWrVqyRYMx8XFcfDgQfVmtAcPHqBUKvHw8GD16tX06dNH3X/e2NiYhIQE6tatK+vJ4NatWyxevBilUomjo2OJMoP3798nLy8PBwcHUlJS+Omnn/D09CyxeehtPwtKpZKkpCT09PSoWbMmiYmJxMfH4+fnp66tnZCQwJYtW/Dz83unwcDrFAoFmpryFMsp3rRaUFDAwYMHKSoqQlNTE11dXeDlZqbmzZtz9epV7t69i4ODA0OGDOGHH36gcePGstRZ/if09PSwsrJStyCWu6SUvr4++vr6pKenU1hYiKamJufPn2fo0KGcOXMGMzMzevfuXSqbJ/8JOd8D/wu5Nm79XQqFgvr162Nra4uXlxe+vr7vZNzCwkJOnz7Nzz//TMWKFRk+fDjW1tYMHTq0VMaLiopi5syZDB8+nIiICH7//XdatGhBbm4ua9eu5dKlS1y4cIFp06Zx7NgxmjVrJmtQfuLECRYsWMCoUaM4duwY169fp1atWnh7e+Pm5kZRURGZmZl4enqWyBl/mwvi4r9NSUlBW1sbpVLJ9u3bSUxMVF/0NGjQgJMnT6o3rpbWfo3k5GQiIiJYvXo1v//+O/fv36dPnz4YGBjQrl07/u///o9mzZpRvXr1Us+Zf1fKTUCcm5tLQEAAFSpUYPHixQDk5+dja2tLfn4+mzZton379ly6dInU1FR115XS3rzxV0xNTWULhp89e0bfvn2pWLGiuo7vxYsXOXPmDH/88QeSJLFv3z5116fiVpCltYHwXXu12YRCoeDGjRvExcXRpEkT6tatS4MGDRg1ahQ+Pj40bNiQ+Ph4mjRpIuuH8eHDh1y+fJkDBw6QkZGBUqlk37597N+/n88//5yuXbsye/Zsnjx5gr+/P25ubrJvYDAxMcHU1JSdO3dSuXJl9WYeKysr0tLSOH/+PDt37mTbtm0MHDiQZs2ayTJu8cVIfn4+w4YNY9OmTdSrVw+VSkVubi5eXl44OjoSHR1NYmIiX3zxBa1atZJl7PJAoVDw6NEjhg8fTlFREdHR0Rw8eJD27durV+pVKhWenp7k5+eTl5fHzJkz8fDw+J87/smpNBYFTExMUKlUJCUlcf78eY4fP05cXBwRERF89dVXsm+eEv4eTU1NTE1NZUtR+7tjlnaKyqsrpJGRkfj4+GBsbMzevXuZNGkSJiYmWFtb4+/vj7a2Nv369ePMmTM8ePCAjh07vvV3wavjb9y4kXbt2tGmTRtOnTpFfn4+SUlJ6hKHrq6u7Ny5E2NjY2xsbN5q3B9//JFdu3bh6elJaGgoc+bM4cqVK1y7do0JEyYwZ84clEoltra2BAcHk5ycTOfOnWW9ECwoKChxDnn+/Dm7du3i0KFDxMXFsXDhQu7du8e6devw9/fH19dXtsIB5UW5CYhfLWnWqFEjdR6khoYGTk5O6qCwXbt2HDhwgFatWr33+So7EO4jAAAXBklEQVSvUygUFBQU8OjRIxo1asSGDRs4c+YM06ZNIyEhAUmS0NDQYNOmTbRs2ZK6devKupu4LL3aeW7//v1oamrSp08fmjRpQv/+/WnRogWNGzemYsWKjBgxgjFjxtC8eXPZguHiYNzMzIw9e/bw008/4evri6+vL+Hh4Tg7O9O7d280NTX54Ycf8PLywtbWtlTSdBQKhbqm8Y4dO6hXr556V3ndunWJjY2lbt26jBo1St39523yRuPi4lAoFGhra5OSkoKBgQFFRUVcvXoVExMToqKiuHr1KvXr18fR0REvLy88PT1LtZJEWZAkiZCQEDp27Ei7du3YtGkTPj4+VK9eHR0dnRJfFoaGhty6dYtWrVrx6aefluGsS5+pqSkvXrygsLCQgQMHYmhoyIwZM975yqhQtkorReVNKWIPHjxg2bJlnDt3juXLl2NnZ8fAgQNp3rw5tra2JCYmsnLlSpKTk1m6dGmJtIW3Gb84MMzIyKBKlSrqltBubm4sWrSI1NRU7O3tqVixIhs2bKBx48ZvfYfSzMyMyZMnk5ycTHJyMuPGjcPV1ZV169bx9OlTJkyYwKpVqzh37hyZmZl8//33//Pzfd29e/eIjY2lWrVq3Lt3j6VLl5Kfn0/lypX5448/ePjwIYsWLaJSpUocPHiQZ8+e4eXlVaKSzYei3ATE8J+SZkuWLKFdu3bo6+vz7NkzVCoVjx8/xsjIiG7dupVKWZHyoLj7W0JCAlOmTEGlUrF582aqVq1KgwYNOHz4MD169MDFxUX2OqtlqThHvKioiEmTJnH8+HEOHz5MTk4O7du3R19fn2nTptGsWTMaNWqEh4eHbCkSxYo3T2zZsoWAgADMzMy4d+8epqamtGnThp9++omff/5Z3Wjls88+k3X812loaGBtbU1KSgp79+5Vtx4eOHAgN2/eZPz48RgbG791B7SJEydy8OBBdu/eTWZmJnv27CEnJwcfHx8OHDiAl5cXurq6hIaGkpGRgaenp7oE4Pvu9XxbSZIIDw8nMTGRjRs30rNnT3r06MH06dOxtrbG2NhYXYtUV1cXJycn2d+H5cnrm7dWr15N//79cXV1LdMUBaFsyZ2i8qYUsezsbO7du0eHDh1wd3cnPj6ew4cP4+vrq27A4uDgQEBAwFvdHXnT+HZ2dmhqahIcHEz//v3R0tLi4sWL9O7dGycnJ7KysqhQoQKdO3d+q+efl5dHxYoV8fDwYNasWTg5OdG9e3eMjY3x9fVl0aJFdOnShYCAAFq1akWXLl1kXYg5cOAA69evx8jISB34RkZGolKpMDExQUtLi9WrVxMfH8+BAwcYPXr0O9tA+q6Vq4AYoGHDhty/f58tW7bg7++vvgpatWoVtra2ODs7y3ZlVB6pVCrs7Ox48uQJ2tratG7dGngZIBUHxz169PggguHbt29TVFSEgYEBGRkZfPvtt1SqVImlS5diYmLC2bNnycnJoVevXmRkZLBkyRIqV64sa87cqyur6enpBAUFqTsPXbhwgWvXruHp6Ymrqyu+vr64ubnJ3gHqTVQqFTY2NsTExBAaGsqqVaswMjJi8+bNqFSqt1oVfrXY/tKlS3FycsLZ2ZkaNWowb9487Ozs0NDQICkpieHDh5OdnY2zs7OsjT7K0uud5xITEzE0NERPT4+QkBCGDh1K165dSUxMZO/evfTu3Vt93pGrvm959/rmrR49esi+eVgQ3pQiZmFhQW5uLpcuXeKHH34gNDSUQYMG4e7urv7b4lrrco9fVFQEvMyjjYuLIyYmhjVr1jB48GB19zeVSqVuCvVPpaSkEBcXR+XKldHQ0KCwsBALCwssLS1Zs2YNgYGBqFQqdHR0uH79Oi4uLpiamsoa++zdu5d79+6RmZlJzZo1CQkJoV+/fgwbNgwDAwPOnz+PlZUVPj4+VKlSBQ0NDcaOHYudnZ1scyhvyl1ADC+Lnhf3Qnd1deXLL7+kRo0ajBgxoqyn9k7o6Ohga2vLqVOniI2Nxd7enmHDhlGzZk1GjhxZ1tOTTUxMDL169WLo0KHk5OSwZ88eHj58SPfu3bGxsSE1NZXz58+jUCgIDAykU6dOuLi4yDoHhUJBamoqRUVFVKpUiUaNGjF//nyqV6+Or68v58+fZ+PGjcTExODr6/vOU1R0dXWxsrJi//79NGrUiHnz5gH/WVX/X71ebD8+Pp67d+9y69YtqlWrxoMHD8jKyiIqKooOHTrg5eX1QeSLFV9EFK8CzZ07lxMnThAbG0tMTAzm5uZYW1sTGhqq3s/QqFEjWVugvi9e37zVtm3bsp6S8AH6Oylizs7OsqWI/Z3xi7ssmpqasmPHDtLT0/nqq6/UaSJvuxgREhLClStXqF69ujr1SKFQ4ODgQEZGBvPmzVN3/gwPD6dDhw6yNnqaOnUqly5dUqd++fj4qKs3derUCTs7O1JSUoiOjkZXVxc/Pz/q16//QSzE/TflMiDW1NTE3d2dFStWMHfuXPr16/fRBMPFjIyMsLCwYNOmTSxcuJA+ffowatSosp6WrCwtLdHT02PBggX06dOH+vXrExUVRXx8PG5ubjg5OXH58mX++OMPXFxcZNtBvHPnTq5cucLFixeJi4tj1apVmJiYULVqVapVq4aFhQX/+te/aNu2LW3atEFTU5Pu3buX2QbOSpUq4e3tTZs2bQDUO/7fxpMnT5g/fz5OTk6EhIQQERHB7du3SUpKQpIkzM3Nad26NT///LP6FuX77vHjxxw5ckRdlWHixIkUFhaycuVKHBwcuH//Pqmpqfj6+lKlShUeP35MgwYNGDJkCCDvl/D7oiw2bwkfn3eVIvZPxx86dChpaWmsXLkSCwsL9aa7/3V8SZLQ0tJCS0uL2NhY4uLisLW1RU9PT5273Lx5c86dO8e3336LhYUFEyZMoGbNmm/9HCVJQpIk/vWvf5Gfn8/SpUtp3LgxXl5eWFlZUbduXS5fvszt27dxd3enXr16XLt2jcqVK5dpJaF3SSEVV5Ivh27fvk1aWhpNmzYt66mUiaKiIk6ePIlKpZKtkkB5NH78eLS1tZk9ezZnz55lzZo1tGvXjh49epCXl4dSqZQtZ23KlCmkpKTg6enJqVOn1NUEunbtSqdOnahbty4aGhp88cUX3L17l4MHD8peX/NtFBeLl8Py5cs5duwYCoWC4cOHU716dWxsbFi2bBlWVlZ07tyZ3NzcDyJF6fHjx6SmpqKlpaW+FRsSEsKePXs4deoUmpqaREVFcfToUfT19Rk0aFCJ/3c5X3dBEP7cH3/8werVq9WpChYWFqxatQp4Nxek/238tz0HvF4W8ezZsxw4cAA7Ozt1SlZ+fj5aWlrAy6ZHI0eOlL14wJQpU+jXrx8ODg7qO40KhYLY2FgeP37MDz/8QJMmTQgMDPzoznvlcoW4mKmpqXo152NU3P3tQ38NfHx8WLJkCXl5efj5+aFQKNiwYQPu7u7qW1dvo/jK+Ntvv+X58+d8//33ODs74+3tTbVq1Th27Bj3799HqVRibm7OixcvSE1NVZd3K0/k/EJ4U7H99evXY2trS506ddQny/fZxo0bWbRoEYGBgVSoUIFZs2ZRVFREr169uHv3LhEREbRv315d1i49PZ2GDRuWuBB4318DQXgflFaKWFmP/2owHBwczPXr1+nQoQOamppER0eTnZ2tPt8WN4by8PCQffPq06dPWbx4Ma1atcLCwqLEaveBAwfYvn077du359ChQ7Rs2ZIKFSp8VOe+ch0QCx8HhUJB165dGTlyJLa2tvj6+sraea/4Qx8eHk5AQABVq1aloKAAlUpFtWrVMDAwIDU1Vd2Kt7jKSffu3WUZvzz7s2L7NWrU4MsvvwTe/0Bw7ty5XLhwgXnz5lGlShV0dXVJSUnhypUrGBgY4O/vT0REBDdv3qRFixbqzk86OjplPXVB+CiVRopYWY+vVCpJT09n9OjR6q67YWFhjB49mrS0NK5du0ZeXp56M3Np0dbWJiEhgbNnz9KsWTO0tbXJyckpkcYxaNAgfH19MTQ0fO/P///Ux7MWLpRrFSpUYM+ePYwbN44XL15Qt25dWR8/MzOTK1euUFhYCJRsYmBmZkZRUREzZsxgwoQJbNy4kW7dusk6fnmlra2tLie0cuVKfHx8mDlzZllP660VFBTQt29fMjMz2bhxI1ZWVly+fJnjx4/Tt29fzMzMiIyMJCkpiWnTphEREcHZs2eBl3mz5TiTTBA+eMUbyF6tT/++jf/6OWTLli04Ojoya9YsHBwcuHHjBlOmTKFHjx4YGxtz/vx50tPT33ruf8XPzw+lUsnKlSsB1J04V65cScWKFdHW1i5XaYLvUrnOIRY+Pi9evCi1hivfffcdycnJzJo1C0NDQ3JyctDV1SU5OZk5c+awaNEidf7Wx0aSJHJzcz+YZjd//PEHXbt2ZeDAgfTr14/du3ezYcMGpk+fjpubG8+ePWPJkiVIkkTv3r3R09MTG8cEQZDdzZs3qVu3LsuXL6devXpkZGTw/PlzPvnkE3x8fBg5ciS+vr7o6+tjamr6TuZ08uRJ1q9fz4sXL/Dw8CAqKgpHR0e++uqrdzJ+eSVSJoRypTRvjRkaGnLlyhV+++03WrRooQ5+p06dSpUqVT7K0lrF5C62X9Z0dXVp3LgxCxcu5NSpU9y5c4eZM2dSv359CgsL0dbWpkaNGvz666+4uLio8/Rfbd0qCILwT726+S8zM5PPPvsMlUrFgAEDMDc3Z8eOHXh7e2NqasqNGzd4+vQp3bt3f6clzWrUqIGvry+5ubmYmJjg4uLCoEGD3tn45ZVYIRY+KidOnGDt2rXk5uZSv359bty4QZ06dZgxY0ZZT00oBUeOHGH8+PGsWrUKd3d38vLySjQ1ef78ucgXFgRBdsUVI86dO8f06dOZOHEiTk5OdO7cmS1btjBv3jzq16/PsGHDynqqwr+JgFj46Lx48YLw8HAqVqyIUqnEy8urrKcklKKQkBD27dtHSEgIlStXpqCg4INaDRcEofwoKChg7ty5eHh44OHhgZaWFj/99BNLly5l9+7dbNmyhevXr1O7dm3GjBlT1tMVXiECYkEQPnhz5szhxo0bbNu2raynIgjCB+TVFIniO1Br1qzh0qVLjBw5ktq1a6OlpcXw4cN59OgRO3bsQKlUolKpynjmwutElQlBED5448ePR6VSceTIkbKeiiAIH5DiYPjIkSMEBQUREhLC4MGDqVatGps3byYpKQlAvVfh8ePHIhgup8QKsSAIH4UPpeueIAjly549ewgNDWXChAlkZ2fTokUL4GVXuIKCApKTkzEyMmLevHliz0I5JhLpBEH4KBQHwx9bO1JBEOT1ehvmpKQk2rZtiyRJXL58mVWrVlGtWjWmTZvGpUuXiImJYeDAgWU4Y+HvEN8KgiB8VEQwLAjC/6q4WUdubi5Hjx4lKysLa2trjh07xnfffYe+vj6TJk0iJiaGBw8e0LJlSxEMvyfECrEgCIIgCMLfoFQqefDgAaNHj1bvS+jWrRtr1qxBS0uLlJQUsrKyUKlUGBoalvV0hX9ABMSCIAiCIAh/Q0JCAt988w1BQUFUr16dTz/9FF1dXVQqFb/99hsRERFkZmYyadIkatasWdbTFf4BERALgiAIgiD8ib1796JUKlEqlXTs2BFNTU309fUxMzNj//799OnTh6tXrxIZGUnjxo2ZP38+WlpaGBsbl/XUhX9IJNMJgiAIgiC8ZurUqRw4cIDExESOHz9OUlISFSpUYMiQIaSlpZGVlcXgwYNxc3Pj2LFjJCQkYGZmJoLh95RYIRYEQRAEQeBlow1Jkvjuu+9QKBSEhIQA/2m6AWBkZMSKFSvIyckhMTGRU6dO0bNnTz799NOynLrwlsQKsSAIgiAIAi8bbSiVSrKysggMDARetmMuDobv379PdHQ0FStWJDs7mz59+uDt7U2fPn3KctqCDMQKsSAIgiAIwr89ffqU6OhounfvDlCi5vCvv/7K6dOnWbp0KT4+PmRkZFCrVq2ymqogI7FCLAiCIAiC8G9GRka0bt2aLVu2kJmZiUKhICcnB3jZgllPTw8tLS2qVKkiguEPiAiIBUEQBEEQXuHn54dSqWTlypUA6OrqArBixQoqV65cllMTSolCkiSprCchCIIgCIJQnpw8eZL169fz4sULPDw8iIqKwtHRka+++qqspyaUAhEQC4IgCIIg/Innz5+zY8cO9PX10dHRoUOHDmU9JaGUiIBYEARBEARB+KiJHGJBEARBEAThoyYCYkEQBEEQBOGjJgJiQRAEQRAE4aMmAmJBEARBEAThoyYCYkEQBEEQBOGjJgJiQRCEUlJYWMiGDRvo2rUrnTt3pn379syfP5+8vDwAJk+eTEhISKnOwcvLi/Hjx5c4dv36dby8vEp1XEEQhPeJCIgFQRBKyddff83ly5fZtGkT+/btY9euXTx48ICgoKB3Oo9ffvmFffv2vdMxBUEQ3ieaZT0BQRCED1F8fDw//fQTp0+fRl9fH3jZ/nXmzJlcvnz5//v9Xbt2sWPHDvLz88nIyOCLL74gICCAlJQUJk2aRHp6OgAtW7Zk9OjRbzz+Z8aMGcPs2bNp1KgRVlZWJX6Wk5PD119/TWxsLBkZGejp6bFgwQJsbGwIDAzE0dGRs2fPkpqaymeffUZqairR0dE8f/6c4OBg7O3tycrKYs6cOdy5c4f8/HyaNm3KxIkT0dQUXzGCILwfxAqxIAhCKbh58yZ2dnbqYLhY5cqVadOmTYlj2dnZhIaGsmbNGsLCwli8eDHz588HYOfOnVhaWrJ3715+/PFH4uLiyMrKeuPxP+Pq6kpAQADjx4+noKCgxM9OnjyJoaEhO3fu5JdffqFevXr8+OOP6p8nJCQQFhbGsmXLWLBgAW5ubuzZs4cWLVqwZcsWAL799lscHR3Zs2cPYWFhpKens2HDhrd+DQVBEN4VcfkuCIJQCpRKJUVFRX/rd/X09Fi1ahUnTpwgNjaWW7dukZOTA0CLFi0YPHgwSUlJNGvWjHHjxmFgYPDG428ycuRIfv31V77//nu8vb3Vx9u2bYuVlRU//PADcXFxREdH07BhQ/XPfXx8ANQryy1atACgevXqREdHA3D8+HGuX7/Orl27AHjx4sXffZkEQRDKBbFCLAiCUAqcnZ25f/8+z549K3H88ePHDB48uETQmJycTJcuXUhISMDFxaVE6oOzszORkZF8+umnJCQk0KNHDy5duvTG42+iqanJwoUL2bp1KxcuXFAf37p1K0FBQWhra9OxY0f8/PyQJEn9c5VKVeJxtLS0/r/HLioqYsmSJezbt499+/YRGhrK9OnT//6LJQiCUMZEQCwIglAKzMzM6NixI1OnTlUHxc+ePePrr7/GyMgIbW1t9e/euHEDY2Njhg0bRosWLTh27BjwskrFggULWLFiBd7e3gQFBWFnZ0dsbOwbj/83VlZWBAUFsWjRIvWx06dP4+/vT48ePbC2tubo0aMUFhb+o+favHlzNm7ciCRJ5OXl8eWXX6rTKQRBEN4HImVCEAShlMyYMYMVK1bQq1cvNDQ0yMvLw9vbm5EjR5b4PQ8PD3bt2kXbtm3R0dHB2dkZY2Nj4uLi6NevH5MnT8bPzw+VSoW9vT1+fn5kZGT86fG/0qVLF06fPq1eTR4wYADTp09nz549aGho4OjoyJ07d/7R8wwKCmLOnDl07NiR/Px8mjVrxqBBg/7RYwiCIJQlhfTqvTFBEARBEARB+MiIlAlBEARBEAThoyYCYkEQBEEQBOGjJgJiQRAEQRAE4aMmAmJBEARBEAThoyYCYkEQBEEQBOGjJgJiQRAEQRAE4aMmAmJBEARBEAThoyYCYkEQBEEQBOGj9v8AGExTHtA0uqkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Class Name\",hue='Rating',data=df)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='upper right')\n",
    "sns.set(rc={'figure.figsize':(10,10)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The top 6 categories are dresses, knits, blouses, pants, jeans, and sweaters. The ratings by class follow the pattern shown above with the majority of ratings being 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.violinplot(x='Division Name',y='Positive Feedback Count',hue='Recommended IND',data=df,scale='count')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='upper right')\n",
    "ax.set(ylim=(0, 130))\n",
    "sns.set(rc={'figure.figsize':(10,10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.violinplot(x='Department Name',y='Positive Feedback Count',hue='Recommended IND',data=df,scale='count')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='upper right')\n",
    "ax.set(ylim=(0, 130))\n",
    "sns.set(rc={'figure.figsize':(10,10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.violinplot(x='Class Name',y='Positive Feedback Count',hue='Recommended IND',data=df,scale='count')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='upper right')\n",
    "ax.set(ylim=(0, 130))\n",
    "sns.set(rc={'figure.figsize':(10,10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive Feed Count by Recommendation\n",
      "                   count      mean       std  min  25%  50%  75%    max\n",
      "Recommended IND                                                        \n",
      "0                 3575.0  3.388252  6.613545  0.0  0.0  1.0  4.0  108.0\n",
      "1                16087.0  2.488966  5.633717  0.0  0.0  1.0  3.0  122.0\n"
     ]
    }
   ],
   "source": [
    "print('Positive Feed Count by Recommendation')\n",
    "print(df['Positive Feedback Count'].groupby(df['Recommended IND']).describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dresses, Jackets, and outerwear had the most positive feedback based on reviews. A very high majority(~80%) of the reviews were recommended, however this isn't suprising based on the ratio of 3 to 5 ratings to 1 to 2 ratings. In the case of women finding a review a positive and helpful, the range of values is not to far off. Visually, most of the categories show more counts in recommended reviews due to the dataset having such a positive reaction to the overall inventory. \n",
    "\n",
    "The dataset provides a good amount of information present in order to go along with the text analysis,but however there are some drawbacks in terms of likely false age entries and a potential for misentered information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEXT ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reviewing the comments, they all are similiar in nature. Most of them mention colors, clothing sizes, and fit.here are misspelled words, however the overall style and nature of the reviews is consistent to move forward with needing to clean much. One noticeable thing was that reviews had a character limit with some being cutoff."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Original Text "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tokenized_sents'] = df.apply(lambda row: nltk.word_tokenize(row['Review Text']), axis=1)\n",
    "df['sents_length'] = df.apply(lambda row: len(row['tokenized_sents']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recommended IND</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3575.0</td>\n",
       "      <td>73.220420</td>\n",
       "      <td>30.504122</td>\n",
       "      <td>4.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16087.0</td>\n",
       "      <td>71.333437</td>\n",
       "      <td>32.571720</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count       mean        std  min   25%   50%    75%    max\n",
       "Recommended IND                                                              \n",
       "0                 3575.0  73.220420  30.504122  4.0  48.0  74.0  103.0  139.0\n",
       "1                16087.0  71.333437  32.571720  2.0  44.0  71.0  103.0  133.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cc6mf9+7di+uvvx5r1qzBmjVr8O1vfxsA4PV6ce+992LFihVYv349XC7XRD4bERHrLyIqG1lDW09PD5555hm8+uqr6OjowGuvvYbjx48PO2fTpk147LHH8Pbbb0PTNLz++usAAJ/Ph4cffhgvvfTSsPMPHTqEr33ta+jo6EBHRwe+973vAQB+8IMfoL29HW+99RbuvPNOPPnkk/n6nERUgVh/EVE5yRradu/ejcWLF6Ourg52ux3Lly/H9u3bU8e7uroQDoexcOFCAMC6detSx3fs2IHp06fj7rvvHnbNQ4cO4d1338XatWtx33334ezZswCAnTt3YvXq1QCAVatWYdeuXYjFYvn5pERUcVh/EVE5yRraent74XQ6Uz83Nzejp6cn43Gn05k6vnbtWtx7772QJGnYNaurq7FhwwZs3boVy5Ytw4MPPjjiWrIsw+FwYGBgYAIfj6i0KHEgEFFG/U+J613K8sH6i4jKiZztBE3TRrwmCELOx9N54oknUv/7K1/5Cp5++mn4fL6054pi7nMlGhsdOZ9bCE5nta73LyV8FgkXP4fegSA+PtE/6nuuuaIZzgZ7IYulCz1+J4xUfwH61WH8ex0dn09m2Z5NWAshWm0Z8brdboa1Ap5rvn93soa2lpYWdHZ2pn7u7e1Fc3PzsON9fX2pn10u17DjF4vH43j++edHfIOVZRnNzc3o6+vDpEmToCgK/H4/6urqcv4w/f1+xOMjK+FicDqr4XKlr7grDZ9FQrrnEIwo8PnDo74vGIzApaqFLFrRFeJ3QhSFrCHHSPUXoE8dxr/X0fH5ZJbLs5GCUfh8kRGv20JRBD87k/Y9gtUKRRoZ9IxmtOeTS/2V9n3ZTliyZAn27NmDgYEBhEIhvPPOO1i6dGnqeFtbGywWC/bt2wcA2Lp167DjIwsq4le/+hXefvvt1PkLFiyAzWbDsmXLsHXrVgDAL3/5S7S3t8NkMo35QxERAay/iEpVPBpBf+f+tP9p4dG/2FaynFraHnzwQWzYsAGxWAx33HEH5s+fj40bN+L+++/HvHnzsHnzZjz66KMIBAKYO3cuNmzYMOo1v//97+Oxxx7Dj370IzQ0NOCpp54CADzwwAN46KGHsHLlSlRXV2Pz5s35+ZREVJFYfxFRORG0dIM6DIrdo6WBzyIh3XMIRBTsPdyT4R0Ji+a0oMqS9fuUoejVPWo07B4tPXw+meXUPRrwoL9z/4jXmxbMRd/Bj9K+p7H9aqhVtXkpo5506R4lIiIiIv0xtBEREREZAEMbERERkQEwtBEREREZAEMbERERkQEwtBEREREZAEMbERERkQGU12JQRGVAEAUEIkrW8ywmGTK/dhERVQyGNqISE4mpOHjUlfW8RXNaIJfZIrxERJQZv6cTERERGQBDGxEREZEBMLQRERERGQBDGxEREZEBMLQRERERGQBDGxEREZEBMLQRERERGQBDGxEREZEBMLQRERERGQBDGxEREZEBcA8cIiIiKop4KITAnw7A0Vyvd1EMiS1tREREVBSh48cQOnoEJ37wQwQ/+lDv4hgOQxsREREVReT0Scj19XDMmgX/vr2Ih0J6F8lQGNqIiIio4NRAAEp/PyzTZ6Dxhi8AAGL9ffoWymAY2oiIiKjgIqdPAQAsl0yDbUobIAgMbWPE0EZEREQFFzl9ClJtLeTaWogWC6TaOih9DG1jwdBGREREBRWPRhDrOQfLJVNTr5mamhDr64OmaTqWzFgY2oiIiKiglMFBQNNgam5JvWZqbIIWjUD1+3QsmbEwtBEREVFBqV4vAECqqUm9Jjc1AQC7SMeAoY2IiIgKSvV6AFGEVOVIvSbX1QOSxMkIY8DQRkRERAWleDyQqmsgiOdjhyCKMDU0sqVtDBjaiIiIqKBUrwdybe2I16XaWqg+rw4lMiaGNiIiIioYLR6H6vNBqkkT2qqqEA+HoamqDiUzHoY2IiIiKhjV5wM0bdgkhCTRXgUAiAeDxS6WITG0ERERUcGoXg8ApO8erUqENjXgL2qZjIqhjYiIiApGGQpt6bpHxWRoY0tbThjaiIiIqGBUjweizQbRbB5xTEp2jwYCxS6WITG0EZWAcFSFyx3SuxhERHmner1px7MBgCDLECwWxIMMbblgaCMqAe/+6Szeeu8UuvtYcRFReVG8HkjVI7tGkyR7FVS2tOWEoY1IZ/2eMLr6AhBFAb8/eBZuX0TvIhER5UU8GoUWiUByODKeI1YxtOWKoY1IZx+c6IdJFvGl6y9BPK7h57/7RO8iERHlhTI4CAAQ7faM50hVVewezRFDG5GOPP4oTvb4MXtqHZpqbZjRWo0TXR5omqZ30YiIJizmGZo5OjThIB3RXgUtGkU8FitWsQyLoY1IR6ddibWJrphaDwBoqLEiFFHhD7HyIiLjS7W0VY3e0gZwBmkuGNqIdNTvCaPKKsNulQEAjbVWAECfJ6xnsYiI8iI2FNqytbQBgMou0qwY2oh0NOANp4IaANQ5LJBEAQNehjYiMj5lcBCC2QJBljOeIw21wrGlLTuGNiKdRGIqfMEYGmvOhzZJFNDqrEK/hzNIicj4YoODqVCWCVvacsfQRqSTZGvahS1tAHBJswP93jAnIxCR4SmDg6lQlokgihBtdra05YChjUgn/UPj1hpqhoe2Kc0OxJQ4fEFORiAiY4sNDo663EeSaLMhHuawkGwY2oh00u+NwGEzwWqWhr1+SXNiEcp+TkYgIgOLx2JQ/f5RJyEkiRYLQ1sOGNqIdNLvCaOhxjLi9ZYGO0RRQD8nIxCRgSmDbgCjL6ybJFitDG05YGgj0kEklliL7eLxbAAgSSKqbSau1UZEhqa4E6Ett5Y2K7QIQ1s2DG1EOhgc2l+0oXpkSxsA2K0yAmGlmEUiIsorxT0AYPSFdZNEqwWaokBTWe+NhqGNSAfJSQbVdnPa41U2EwJsaSMiA1MGkt2jubW0AUA8zOWORsPQRqQDXzAKQQAcNlPa4w6rjHBUhRqPF7lkRET5obgHINpsEE3p67kLidah0MYu0lExtBHpwBeMocpqgigKaY/brYlKLsguUiIyKMXthqm2NqdzhVRLG0PbaBjaiHTgC8ZQbc/87bPKltjyJRBiaCMiY1I8bsg5hjbRmhjfqzG0jYqhjUgHvlA043g2AKgaamkLhDmujYiMSfF4INfU5HTu+e5RjmkbDUMbUZFFoiqisThqRmtpsw61tLF7lIgMSNM0qF4vpOrqnM4XzBZAENg9mgVDG1GR+YJRAIBjlNAmSSKsZokzSInIkOKhELRYDHKuoU0QIJgtnIiQBUMbUZEll/uoGaV7FEh0kbJ7lIiMSPV6ASDn0AYkukg1do+OKqfQtm3bNtx666245ZZbsGXLlhHHDx8+jNtvvx3Lly/HI488AkUZ3qXz7LPP4rnnnkv9/Mknn+Cuu+7CmjVr8Bd/8Rc4fPgwAKC7uxtXX3011qxZgzVr1uCee+6ZyGcjKkm5tLQBickInIgwcay/iIpP8XoAIOcxbUBiMgK7R0eXNbT19PTgmWeewauvvoqOjg689tprOH78+LBzNm3ahMceewxvv/02NE3D66+/DgDw+Xx4+OGH8dJLLw07/9FHH8XGjRvR0dGBv/u7v8O3vvUtAMChQ4ewevVqdHR0oKOjAy+++GK+PidRyfAFY7BbZMjS6H9+yZY2TdOKVLLyw/qLSB/qUGjLdUwbkFhgl6FtdFlD2+7du7F48WLU1dXBbrdj+fLl2L59e+p4V1cXwuEwFi5cCABYt25d6viOHTswffp03H333cOueeedd2Lp0qUAgCuuuAJnz54FkKj0jh49inXr1mHDhg04cuRIfj4lUQnxZlnuI6nKKkNRNUQVLrA7Xqy/iPSheMbT0mblmLYssoa23t5eOJ3O1M/Nzc3o6enJeNzpdKaOr127Fvfeey8kSRp2zXXr1qVe++EPf4ibb74ZAGCxWLB27Vq8+eabuOeee/CNb3wD0Wh0Ah+PqPT4syz3kVQ1tFsCJyOMH+svIn2oXi8gipDs2fcdTRIsiTFtGneCyUjOdkK6rhlBEHI+Ptp1n3rqKRw8eBAvv/wyAOCb3/xm6viyZcvw9NNP48SJE5g9e3bW6wFAY6Mjp/MKxenMvRm43PFZJFz8HCI9PoQiKhrrbKh2WNO+x2SSUe2wwtmQqLjiENKea7db4GzIvULUmx6/E0aqvwD96jD+vY6OzyezTM9mMBKEqbYWVQ4rYtWWEcdNJgnVF72u1lYhCMCiKXCUyTPP9+9O1tDW0tKCzs7O1M+9vb1obm4edryvry/1s8vlGnY8HUVR8K1vfQs9PT14+eWXUT3U5/3KK69g1apVqK+vB5CoGGU5axFT+vv9iMf1Gf/jdFbD5fLpcu9Sw2eRkO45dPcOzagSAZ8/fTdALKYkjg192+wfDKKpZmSlFwxG4FLVPJe6MArxOyGKQtaQY6T6C9CnDuPf6+j4fDIb7dkEevsgVtcgGIzC5xs5I9QSU0e8HhuKJD7XAEI1zhHvMZrRnk8u9Vfa92U7YcmSJdizZw8GBgYQCoXwzjvvpMZzAEBbWxssFgv27dsHANi6deuw4+l8//vfh9/vx0svvZSq8ABg7969eOONNwAA77//PuLxOGbOnDnmD0VUqga8iUqqKsNG8ReymhNdcOGoMYJZKWL9RaQPxeuFNIbxbMD5XRFUv78QRSoLObW0Pfjgg9iwYQNisRjuuOMOzJ8/Hxs3bsT999+PefPmYfPmzXj00UcRCAQwd+5cbNiwIeP1BgYGsGXLFkyZMgV33nln6vWOjg488sgjeOihh9DR0QGLxYKnn34aosil5Kh8DPqHQps1ewuMKAowm0SGtglg/UWkD9XrgaW1bUzvESyJHoV4MAAp4Bl53GqFIo3sdagkglZG6wmwe7Q08FkkpHsOP9v5Cd76w0ms/+IsSBn+QV9wuRMHj7oAAFt3nUB9jRXLFraOOG/RnBZUWcbW/aYXvbpHjYbdo6WHzyezTM9G0zQc//pG1N38RUxasRz9nftHnNO0YC76Dn407DU1GED///szTL59HVT7yFa6xvaroVbltgF9KdCle5SI8mfQH4HNImUMbBezmGVE2NJGRAYSDwahKQrk2rEFLNGS6B5VAoFCFKssMLQRFZHbG4bdmn08W5LNIiEc5a4IRGQcyTXapJqxhTZBkgBJQjwUKkSxygJDG1ERuf2RnMazJVlMEse0EZGhJHdDGGtLGwCIZgtUhraMGNqIikTTNLi9EVSNoaXNakl0j5bR0FMiKnPJfUfHOnsUAASzmaFtFAxtREUSCCuIKnFU2XJvabOaJGgAIjG2thGRMaipLazG09JmhhriVlaZMLQRFcmAN1ERjamljWu1EZHBKF4vIEkQq6rG/F7BbEY8FCxAqcoDQxtRkaQW1h3DmDarhaGNiIxF9Xkh19TktCXcxQSzGWqQ3aOZMLQRFcmAL9HSNpbZo8mWNi77QURGoXq9kKrHPp4NGJqIEGb3aCYMbURF0u8NQxIF2IZaz3JhMSVa5bjsBxEZher3Qaoe30bpgtmMeDjMyVcZMLQRFYnbG0GdwzKmLgOOaSMio1G94w9totkMaBq0aDTPpSoPDG1ERdLvDaO+emz75nH/USIyGsU3/u5RwWIGAIa2DBjaiIrE7YugboyhDQCsZpmhjYgMIR6JQItEII+3pc2UCG1xhra0GNqIiiCuaRj0R1DnMI/5vVYzt7IiImNQ/YkN0sc9ps2S+GKrRSN5K1M5YWgjKgJ/MAZF1VDrGE9Lm8TZo0RkCKrXCwATmD3KlrbRMLQRFYHbl/jWOP6WNoY2Iip9im+opW0cW1gBidmjAMe0ZcLQRlQEbn8ytI29pc1iTuw/GucUeCIqcaov2dI23iU/EnUkW9rSY2gjKoLBoZa28XaPagCi3H+UiEqcOtTSJo939qgsA6LIMW0ZMLQRFYHbF4EgADVV4+seBbhWGxGVPtXnhWAypSYUjJUgCJCsVnaPZsDQRlQEbn8ENVVmSOLY9+KzmLiVFREZQ2Jh3fHtO5ok2W3sHs2AoY2oCNy+COrH0TUKXBDa2D1KRCVO8Y1/N4Qk0WZn92gGDG1ERTDoi4x5N4QksynxZxqNxfNZJCKmCO0oAAAgAElEQVSivFMnsBtCkmSzsqUtA4Y2oiJwTyC0JVvaOBGBiEqd6vNCrplYS5tks3FMWwYMbUQFFompCEaUcYc2k5z4M40obGkjotKlaRrUvHSP2hCPMLSlw9BGVGCDvvGv0QYkZlOZTSJb2oiopGmRMLRYLA/dozZosSg0rk05AkMbUYEld0MYb0sbkOgi5UQEIiplqd0QJtjSJtlsQDwOKNxz+WIMbUQFlo/QZjZJnIhARCVtovuOJkk2GwDuipAOQxtRgU1kC6sks8zuUSIqbandEMa572iSOBTaOBlhJIY2ogJz+yKwWSTYLPK4r8HuUSIqdRPddzRJsrOlLROGNqICG/RFJtTKBrB7lIhKX7KlTXJMMLRZ2dKWyfi/+hMBUOJAJDZ8sKg2EEQwMvw1i0mGXKFfEQZ8ETRMYDwbAFhMIqKKCk3TJrQ9DBFRoSheLwSLFeI49x1NEq2J92sxhraLMbTRhERiCvYe7hn2WrXDCp8/POy1RXNaIE+ge9DIBv0RtDU1TOgaZpMETQNiahxmWcpTyYiI8icfC+sC51va4rHYhK9Vbiq07YOoONR4HIP+COom2NJmTu2KwC5SIipN+VhYFwBEmxUAu0fTYWgjKiCPPwpNQ166RwFuGk9EpSsf+44CgCDLgCBAY0vbCAxtRAWUWu4jby1tDG1EVJoUry8/oU0QIJjNHNOWBkMbUQG5vYnQlq+WNnaPElEp0uJxqH7fhNdoSxJMZsSjbGm7GEMbUQHlYzcEAKnJB+weJaJSFA8GAVWd8HIfSaLJxJa2NBjaiArI7Y9AlkQ4bKYJXYfdo0RUylT/0BpteZg9CgCC2cQxbWkwtBEVkNsXQX21ecJrq8mSAFFg9ygRlSYlT/uOJgkmjmlLh6GNqIASoc064esIggAzt7IiohKV3MIqf2PaTBzTlgZDG1EBuX3hCY9nS7KYJEQVtrQRUelRvUPdo3lqaRM5ezQthjaiAtE0DW5fNG+hzWwS2dJGRCUpNabN4cjL9RLdozFompaX65ULhjaiAvEGolDUOOonuFl8UmLTeIY2Iio9itcL0V6VWBg3DwSTCdA0QFGyn1xBKnMzSDK0dJvUX6wUNqjv9yT2X81n96jHz+4CIio9qs+bt5mjACCaEzPu47EYJNPEZt+XE4Y2Mpx0m9RfrBQ2qO/zhAAA9TV5ammT2T1KRKVJ9Xoh52k8G5DoHgUwNK7NnrfrGh27R4kKJNXSlsfu0ZgSR5xjPIioxORrs/gkYah1TeMM0mEY2ogKpH8wBEEAah3mvFzPPLSVVYwzSImoxCRCWx5b2syJejPOGaTDMLQRFUifJ4Q6hwWSmJ8/s+RWVjEusEtEJURTVagBP6Q8rdEGJLaxAsBdES7C0EZUIP2eMOry1DUKAKahmRVRhePaiKh0qH4/oGmQ89o9OjSmLcqWtgsxtBEVSL8nhIY8zRwFznePcoFdIiolyd0Q8tnSJpjZ0pYOQxtRgfR7wqjLZ2hLdo8ytBFRCVF9yYV189jSJieX/GBL24W45AflTSiiQBIFrmCNxLMIhpW8trSluke57AdR0clqBFo4POJ1wWqFIuXv79yIlEK0tIkiBFnm7NGLMLTRhMWUOHYfOouTPX4AidmSyxa25nU8l9G4fREA+VtYF2D3KFG+jCeAaeEw+jv3j3i9sf1qoKpy6zrg/L6j+VynDUjMIOX+o8MxtNGE+IJRvPWHk/D4o7hyRgOsZgkffebGW384hRuuacOkhspcFNHtz39oM7F7lCgvGMDyS/V5AUGAWFWV1+sKJhPHtF2EY9poQrb+/lN4A1Hc1D4F117hxJUzGnDHjbNgt8jYdaAb4WhlduW5vfkPbZIoQBIFdo8SUUlRfV5IjmoIeVreKEkwmRHn7NFhGNpo3I6eHsT7H/Vg7vQGtDad/4ZVU2XG0oWTEYmp6Py4V8cS6sfty+++o0lmk8iWNiIqKYrPl9fxbEmimS1tF2Noo3GJxzW88s4R1FdbMO/SxhHH66utuGpmI050e9HlCuhQQn25/VFU282pLs18MckSx7QRUUlRvd68bmGVJJg4pu1iDG00Ln/6pB9drgDWfH5GalbjxeZf2gCHzYQDx1wVN6PU7Q2jqc6a9+uaZZHdo0QXkNUIpIAn7X+yGtG7eBVB9fkgF6CljWPaRuJEBBqX3+w/gzqHGQtnOfHHI+m7QCVRxLxLG7Dngx589Jkb181uLnIp9eP2RdDcmN9BuUBi2Q92jxKdl2lSAcCJBcWi+rx53Xc0STCbEeeSH8OwpY3GrNcdxAcnBvCFhW2QRGHUc2e21qLKKuPt905VVGub2x9BY20BWtpM7B4lotIRj8UQD4UK0j0qmkyAqkCLs85Lyim0bdu2DbfeeituueUWbNmyZcTxw4cP4/bbb8fy5cvxyCOPQFGUYcefffZZPPfcc6mfvV4v7r33XqxYsQLr16+Hy+UCAESjUWzatAkrVqzAbbfdhk8++WQin40K5Lf7uyCJAj6/oDXruZIo4MqZDfj0rBdHTg0WoXT6iylx+IIxNNXZ8n7tREsbu0fHgvUXUeGkdkMoREtbcv9RjmtLyRraenp68Mwzz+DVV19FR0cHXnvtNRw/fnzYOZs2bcJjjz2Gt99+G5qm4fXXXwcA+Hw+PPzww3jppZeGnf+DH/wA7e3teOutt3DnnXfiySefBAC88sorsNlseOutt/Dwww/joYceytfnpDxR43G8e+gcFs5qynlm5Ky2RGvbrzpPF7h0pWFwaI22xppCjWnjt85csf4iKqzkvqNyTSEmIgztP8ou0pSsoW337t1YvHgx6urqYLfbsXz5cmzfvj11vKurC+FwGAsXLgQArFu3LnV8x44dmD59Ou6+++5h19y5cydWr14NAFi1ahV27dqFWCyGnTt34stf/jIAYNGiRXC73eju7s7PJ6W8OHJqEP5QDIvntuT8HkkSsWTeZBw43oc+T6iApSsNyd0QGmvz39JmNklQ4xrUeOV0NU8E6y/SU6ZJEuU0QSK1WXxBWtqGQpvC0JaUdSJCb28vnE5n6ufm5mb86U9/ynjc6XSip6cHALB27VoAGNa1cPF7ZFmGw+HAwMBA2mudO3cOra3Zu+EAoLHRkdN5heJ05v+bRqn5cNcJWMwSvnDdNFjNMrSBIKodI1uULn7tqsuc2NF5Gu997MJXV105oTJkuueF7HYLnDrtxvDBUDews9424ncil7KbTHLGc6qrEt0FFotJ1884Hnr8fRip/gL0q8OMXHeFtRCiGVr97XYzrGk+W6b3ZDrf6awe83sAINwbwuCRj0a8Xnf1wozvMRq7lghUzmmTYBvDszaZJFRn+P8teUyqscMLwGoCbNWWUZ91qcr331bW0JZu8LggCDkfz5WYYSXlTK+n09/vR1ynFginsxoul0+XexdLPK7h3YPdmDejAT5PCD4AwYgCn3/4Hn7VDuuI12ymWlw9y4ntez7DLde0wWwa//pl6e454pxgBC5Vn7Ffn545H9ou/p3IpeyxWOZz4mqia3RgMKjrZxyrQvx9iKKQNeQYqf4C9KnDjF53ScEofL70LVfmYBS+NJ8t03vSnZ98PmN5z3juY0ROZzXc3YnVAzwxCf4xPGtLTM34/1vyWGxoeGnAE4TiiBjuuY32t5VL/ZX2fdlOaGlpQV9fX+rn3t5eNDc3ZzzucrmGHU+nubk59R5FUeD3+1FXV4fm5ubUoN5cr0XFc7zLA28givZxLt1x47VTEAgreO9wT55LVlr6PSHU2E2wmvO/ok5yTTwu+5Eb1l9EhaX6fBBkGaIt/8NBzk9EYPdoUtbQtmTJEuzZswcDAwMIhUJ45513sHTp0tTxtrY2WCwW7Nu3DwCwdevWYcfTWbZsGbZu3QoA+OUvf4n29naYTCYsW7YMHR0dAIDOzk5YLJYxdS1QYXUe6YVJFjFv5sgdEHIxe2od2pxV2LHvTFkv/9HvCRdkuQ8AqRbKKGeQ5oT1F1FhJXZDqBlXC3U2qTFtDG0pObW0Pfjgg9iwYQPWrl2LVatWYf78+di4cSMOHToEANi8eTO+973vYcWKFQiFQtiwYcOo13zggQdw4MABrFy5Eq+++iq+853vAAD++q//GtFoFCtXrsSTTz6Jp556Kg8fkcZLiQOBiJL67+DxPlx+SR3iOP/6WHpyBEHATddMwakeP453eQpWbr31ecIFmYQAJGaPAuAM0hyx/iIqrMTCuoUZZ8bQNlJO/TerV69OzZZKeuGFF1L/e/bs2XjjjTcyvv+b3/zmsJ/r6urwL//yLyPOs1gs+P73v59LkagIIjEFe4e6Mn3BKFyDYcyYXJN6DQAWXO7M9Pa0PnflJPxs5yfYse8MZk2py2t5S0Fc09DvjeDqWWN7Lrli9+jYsf4iKhzF5ytcaJMTESXOddpSuCMC5aSrL7Hpe2vTxLZmspglfH7+ZOw74kotjVFOfIEoFDVeuO5Rmd2jRFQ6VJ8XUgH2HQUAQRQhyDJb2i7A0EY56e4LwmEzodpumvC1brymDfG4ht8d6MpDyUpLnycx67OpQKHNxO5RIioRmqZB9flgclSlXY9OCnggxif2BZObxg/HDeMpKzWu4Vx/ADNba/My2LS53o55lzZi54FurFoyHbJUPt8d+r2J0FaoljZRFCBLArtHiUh3aigMLRqFbLOhv3N/2nOaFsyd0D0Y2oYrn38tqWBc7hAUVUNrU/4Wcr352inwBqLY+3Fv3q5ZCpItbYXYwirJLEvsHiUi3cXcbgCAXKDuUYCh7WIMbZRVd38AggBMasxfaJs7owEtDXa88/7pslr+o98TRpVVhs1SuEZsk0lkSxsR6S46mAhthZqIACTWamNoO4+hjbLqGQiiqdaaGgSfD6Ig4Nbrp+Jkjw8HjvVlf4NB9HnCaCrQch9J3DSeiEpBdCCx+0uhW9riDG0pDG00qpgSR58njJYC7HG5ZN4kNNfb8PPff4p4mbS29XsLt7BuklmWEGP3KBHpLDZY+O5R0WSCxiU/UhjaaFSuwRA0DZhUgNAmiSLW/LcZOOPyo7MMxrZpmoZ+T7hgM0eTTLKIKLtHicqSrEbSzsKU1dJbIinqHgQkCZI9//8+JCXGtCkFu77RcPYojercQBCCADjrCtPld/2cFrz1h1P46Y5juGpGA+zWiS8pohdfKIZITC3oJAQgEdo4po2o+DRNK8h2TcPuEQ6nnYnZ2H41UGUp6L3HKuZ2Q66phSAWrv1HGGppK6exzxPB0EajSo5nS64Plm+iKODuW2fjyZf34ac7juNrK+cU5D7F0OsOAQCa6ws8po0TEYgKKh6JIPjRhwge/hDdZ04h2H0W8UAA0DQIZgskhwOm5maYW9ugXbNA7+LqJuoehFRbW9B7CLIJ0DRA5ZAQgKGNRhGJqujzhHHljIaC3mfG5BqsWDwVv9hzElfNbMB1c1oKer9C6XUHARQ+tJlkCWpcg6IyuBHlU+TcOfS/+3P49r6HeCgEwWxG9eWzUH1tO0xWC4Jnz0ELh6F4vQgdO4rQx4cRPPBH1N28HHU33pTadqlSRN1uyLX1Bb2HYOb+oxeqrN8wGpMTZz15G88miAICkczjEm5qvwQfnxrEC9s+gkkWJ7x3Z7b7AYDFJCOfDYg9A6GCdiUnmYYWIw5HVdQWbigJUcVQPB4EDvwRvadPQTCZUH3tItQs+TPYZl2O5sn1cLl8kAKeYd2Wmqog0tUFpes0XK//BwZ/91u0/NUG2OdMbDFZI4m5B2GfOqOg9xBMZgDgDNIhDG2U0SddXgjITwiJxFQcPOoa9Zy/XXMlfvzzD/DPP/8Ay6+bipWfmzZsvTM1Hkd3XxAfn3Jj3xEXAuEYIlEVZpMEh01Ga1MVWpuqIEtiTvdbNKcFch7XU+txJ7qSC73DQ7KrOpwllBLR6OKRCAIH9yN09AgEWUbjLbegdsUqyNXZZ0MKkgzr1GloXLcW3hMn0fsfW3Dm//o/0bDqy2hcvaag47xKgaaqiHm9Be8eFYdaL9nSlsDQRhmd6PagocZSsPFsF7NZZPz9XyzAll8dxS//cBK/3ncabU0OOGwmeINRnOsPIhJLjGswySKq7SZYTBKiMRWfDoZw9LQHNouEhbOaMO+ypqKU+UK97hCa6wvf9GU2nW9pI6Kx0+JxhI4fg/+PndCiUdhmXYGq+Qvg/PwSqFVjX76iat58TLv8CvRueRkD2zoQOX0Kk+/9OvK3smXpUX1eQNMgF3pM21BLm6YwtAEMbZSBosbx2VkfLm0r3Po76ditJmxcfSVuWXQJdh86hzMuP7yBKKrtJlw2fzJmttZgUmMVPjvrGTaLS41r6BkI4uDxPuz5oAf9ngiuubwJZlNxqk1N09DjDuFzrYV/XqmWtihb2ojGKtJ1Br3/9iJCn34K2elE9XWLYWponPB1RYsFLXf/b7BMnwHXf2xB1w+exiX3fC0PJS5NyqAHAAof2pJj2qIMbQBDG2VwqsePmBIvSstROtMn1WD6pPQBKBBRcPKcd9hrkiigtakKkxvtOHbGg/cP96LXHcRN7VNQVYRlRHyhGEIRBS1FeF7J0BaKsKWNKFeaomDgrV+g///7T0hWK6o/92ewXnpZXpfwEAQB9TfeDKnKgXMvvYAzL7yAqusWQ5DK759axZvYDUGqrSvofQQ5ORGBC+wCDG2UwbEziT/I5gIPqs83QRBw+SV1WHi5Ey/+50f4zb4ufOn6qQXv4u0dKM5yHwBS24mxpY0oN+HPPsO5f/0Jol1nUH39Ykz68mp4Pj5WsPvVXL8Y0OI495P/G2okipqlXyi7MW6qp0gtbaah0MbuUQDcEYEyOH7Gg6ZaK+xWY+b6WZfUYenCVgz6I/j9we6Cb5PVM7TcRyG2+7rY+e5RtrQRjUbTNAy8/RZO/R9PQPX70frfH8DkjfdBdjgKfu+axUvQctttiJw+hcCBPxb8fsWmDIU2qabAExGGQhtnjyYwtNEImqbh2JlBzCzC+KxCanNWYdGcZpxxBfDxZ+6C3qvHHYIoCAXfwgpgaCPKhaYq6Pq3f0Pfz16DY+HVmP6PT8Kx8OqilqHhC1+AddblCH74AcKffVrUexea4vFAdjhSoapgZBkQBI5pG2LMZhQqqN7BELzBGGa2FvYbVDFccUkduvuC2H+sD1OaHaipMhfkPr3uIBprLQVf7gNIjN8TBCDEJT+I0orHYvDs/A1i586i6Y4/R/3yFQXffiqT6kXXQx0chHf3u5AbGiAXuGWqWFTPIEz1hR3PBiSGvAiyzO7RIWxpoxFOdCUG+c8weEsbkPiDXzy3BZIo4N1D5wq2f12PO1SUSQhA4jOZZJEtbURpaKoKz293INZzDpPXr0fDl27VLbABgCBJqFm6DIIkwfv7XdDKZDsmxeOBub6wuyEkCSYz12kbwtBGI3zS7YHFLOVlJ4RSYLfKaJ/dDNdgCJ+d8+X9+nFNw7mBYFHGsyWZZYkTEYguomkafO//AbGec6hZ8t9Qd911ehcJACDZq1DzuSVQBvoRODhyM3gjUgbdRQxtJoa2IQxtNMIn3V7MnFwDUdTv22m+zWyrQX21BfuP9kGN53fPzj5PGJGoCme9DYGIkvqvdyA47OdAREE8Tw19JllEmEt+EA0TOvoxwsePwX7VfFhnXqp3cYaxTJ0G62WzEPzoQ4ROn9a7OBOixeNQBgdhbpr4+na5EEwmTkQYwjFtNEwkpuJMrx9fun6q3kXJK1EQcO0VTvy68wyOnvJgzvT8fUPs6vUDADz+CPYe7km9Xu2wwucPDzt3weUT21M1KdE9ypY2oiTF64V/XyfMbVNQVeQJB7lyXNuOaNcZnP3pTzH1scchSMbcM0H1eQFVhaWxOKFNNJkQ5zptANjSRhc5ec4HNa7h0jKYhHCx5OK7h070Q1Hz19p2xpUIbXUOS96umQ3HtBGdp2kafHvehSCKqF78OV3HsI1GNFvgWHQ9ImfOYHDHr/QuzrgpAwMAAHNTcbYLZPfoeQxtNMyJ7sQkBKMv95HJ/EsbEY6qOH7Gk7drnnEF0FRrLdoerQBb2oguFD5+DLHeHjjar4Nkr9K7OKOyTJ0Gx5VXom/rm4j1ufQuzrjE3IkllCxF7B5laEtgaKNhTnQnFtUt1NIYemtpsMNZZ8OHnw5AzVNr2xmXH5ObivsPhVkWuY0VEYB4NIrAwQOQnU5YL71M7+JkJQgCJt1xByAI6Pl/Xi7YjPZCUtxDLW1F6h5laDuPoY2G+aTbi0vbyq9r9ELzZjYgEFbQeWTi33JjioqegRBaixzaTJw9SgQAGNi1C/FQEI6r20u2W/RipoYGNN12O4IfHIJv73t6F2fMlIEBCLIMU21xemSSoc2IATffGNooZcAbhtsXKduu0aQ2ZxXqHGb89o9nJlwJdPcFEdc0tDYWO7SJUFQNMSW/M2GJjEQNBND/61/D3DYF5pYWvYszJnU33gzL1Glwvf5TxMPh7G8oIYrbDbm+vmghWTAlen60KCcjMLRRSnI8WzlOQriQIAiYPa0eXa4Ajk1wbFtyEkLxW9qSW1mxtY0q1+DO3yAeCqFq4TV6F2XMBFFE811/BXVwEP2/2KZ3ccZEcQ9Arm8o2v2SW2WpBgu3hcDQRiknznohSwIuaS78Zsp6mzG5BnaLjF/vOzOh65xx+SFLIpz1tjyVLDfmodDGrayonMhqBFLAk/Y/WY0MOzcei2Fwx69QNXs2TA3FCxD5ZLtsFmo+92dwv7Md0Z5zehcnZ7EihzYhuWk8QxtDG513osuDaS3VRZ0FqReTLOJzV03CH4+4MOAdf0VwqseP1iY7pCIvRGxKhTZORqDyoYXD6O/cn/Y/7aJ/sH3v7YHq9aLxxht1Km1+NN1xJ0STCb3/8aohxmxp8Xiqe7RYGNrOK/9/nSknihrHZ+d8ZbFJfK4+v6AVGjT8dn/XuN6vxuM40e3FZTpM3GD3KFUyTdPgfmc7LJdcAvvll+tdnAmRa+vQsHoNgh/8CYGDB/QuTlbJhXXlIrZuMrSdx9BGAIAuVwBRJY5L28p7EsKFGmutWHhZE353oBsxZewtVqd7/YjEVMyaUleA0o3OJCdWUmdLG1Wi0JGPEe3uRt3Nyw0zY3Q09TfdAvOkyXC99mrJb9ekDK3RZtKjezQSyXJm+WNoIwCJTeIBfRfVFURhxF6d6f7L1/6dAHDTtVPgD8Xw3ke9Y37vsdOJZzZrSvFb2jimjSqZZ9fvINrtqF5UGhvCT5Qgy3B+ZT1iLhcGfvtbvYszquQabXqMaeNEBO49SkM+6fKipsqMxhqrbmWIxFQcPJp97bR87d8JAHOm1aO1qQq/3ncafzZv0pi+tR87M4jGGisaaqwIFDk8pca0sXuUKozq88H/x07ULv0CRLMZiIX0LlJeVF15FRzXXIu+X/0KDavWQKoqzZ0dYgPJ0Fa8MW3i0JIf7B5lSxsNOXHWi0tba8qiq2EsBEHATddOwakeP4535b78h6ZpONbl0aWVDbhwIgJDG1UW757d0BQFtUuX6V2UvHP++V8Cmgb/vr16FyUjxe2GIMuQqquLdk/BlGhfYvcoQxsB8Idi6BkIlv2iupksuXISbBYZO8aw/IfLE4bHH9UttEmiAFEUuGk8VRRN0+D5r9/BOvNSWKZcondxAACSgIzLlIjxsf19mpqcaLzpJkROfoboubMFKvHEKAP9kOvqIYhFjA+iBIgiW9rA7lFC5Syqm4nFLOHz8ydjx74zcPsiqK+2ZH3PsdODAKDLJAQg0UJoNUsIsqWNKkjk5ElEu7vR/Ndf1bsoKfFoBP0HP0p7rGnB3DFfr/GmmzDw+/+Cb+97aFj55ZzCkaxGRiyJAgCC1QpFyl6fjUXM5YLJmb8hKrkQBAGCycTQBra0ERKbxAsCMH1y8Zq7S82N105BPJ778h+HTvTDYTOh1anfuBObWUaYoY0qiPcPuyHIMqrbF+ldlIIRzWZUty+COjiI0JGPc3pPpvXt0gW5iUqEtua8XzcbQTaxexQMbYTEJvFtTQ5YzZXb8NpcZ8OCy5rwuwNdWZf/iERVHDjeh/bZzRB1HANotUhc8oMqhqaq8L3/B1TNX1Cyg/TzxXzJVJgmtyJwcD/iodKZaKGGQlD9vqK3tAGAaDZx9igY2ipeXNPwabe3otZny+SLiy6BLxjDzv3do5538JM+RGNxXD+n+N82L2Q1S1xclypG4OhRqF4vaj63pGD3SDc+LdzbO67xaRMhCAKqF10HTVHg3//Hot03m5grsTSSLi1t7B4FwDFtFa9nIIhgRKnYSQgXmj2tHrOn1uEXfziJpQtbYTFJac9776Me1DnMuo1nS7KZZXiDUV3LQFQsns5OiPYq2K+aX7B7pBufFq22wOeLjGt82kTItXWwz5mL4EcfwlYiuz6kQluzTt2jDG1saat0n3QlJiFU0vZVo1n7+ZnwBqL47R/Tj20LhmM4dKIf181pgVjk/UYvZrVICLN7lCqApqrwf/ABHNdcA3FoodVKYJ+3AKLNBt/770GLx/UuDmK9iXU0TU3F7x4VTBzTBjC0VSQljtTuAkdOD8JqllDjMBd05wGjuPySOlw5owG/2PMZ3L6RFcTOA91QVA3XzWkpfuEuYjXLXFyXKkL03FnEw2E4rmnXuyhFJZrNqLqmHUp/Hzzvv693cRDr64XocECy24t+b8Fk5pg2MLRVpEhMwd7DPdh7uAeHTw6grtqCfR/3pl5L/qeUwDe7Qsq0bda6ZTMRU+J4/j8/hC8cgzL0GFyDIfznf32Kq2c1lUR3stXMiQhUGSInT0K0WmGfU9wuylJgnTETJmczerdtgxoM6FqWWK8LZh3GswEc05bE0FbBVDUOty+CJh23rtJTJKaOCKp7D/fg5Dkfrp3txNHTg3h+6wcIhKOIKXG88vYRCKKA9beUxvgSq0WGosYRU8o7XFNl0+JxRM6cguPKKyuqazRJEAQ4rtZHIP8AACAASURBVLseaiAA189e07UsMVcPzPV1ukzSEE0maNFoSXQT64kTESrYgC8CTQOa6ioztI3msrZauAbD+OgzN578905EYip8wRjW33I5Gkok5FrNiYkSoagCk2zWuTREhRHrOQctEkH1ggV6F0U3poZGNN54I/p37ED1tYtQddW8opdBUxTEBgZgmtSK/s79AIo7SSO5aXw8HIJkL+8lX0bDlrYK1udJNDU31pZGCCklgiBgyVWTcNO1bXDYTLisrRZ//xcLceM1bXoXLSW5rh4X2KVyFjl1EpBkOObM0bsoumpasQLmya3o+fd/hRoMFv3+sYEBIB4v6p6jFzof2iq7i5ShrYL1e8KwWSTYLWxwzaTN6cDff+VqfPP2+bhyRgMEHRfTvViqpY3j2qhMaZqGyOlTsLS1QTRXdmuyaDKh5e57oAy64frZT4t+/+RyH7qHthBDG1WoPk8YjbW2kgoilDvbUNgOsaWNylTM1Yt4KATL1Gnjen+mzdyLuVBuPtlmXor65Svg/f0uBD44VNR7p0KbQ++WttLZIUIPbGKpUNGYCm8gWhKzIGl8LhzTRlSOIidPAqIIc9uUcb0/02buxV4oN58a16xF4OAB9Pz7v2La/3oCksNRlPtGu7shWiwQdVjuA2D3aBJb2ipUv3doPFuJDKqnsTs/ps2YrQZEo0l0jZ6EeXJrxXeNXkg0mTHpnnuh+rw4+5PnizabMtJ1BpbJk3XrmRFNid+BUtqLVQ8MbRWKkxCML9nSFmT3KJUhpb8f8UAAlmnj6xotZ9bp0+H8ynoEPziEgV9sK/j9NE1D5MxpWCZPLvi9MmH3aAK7RytUvyeMarsp9Q8/GU9yTBs3jadyFOk6DQCwtF2ic0lKU+3SLyB0/Bj6/3Mr7K2TCnov1TOYCNCtrdBroxx2jyawpa1C9XnC7Bo1OFkSIIkCZ49SWYp2dUFuckK0sp5KRxAEtPzV38Dc2obul1+GGijcbgmRM2cAAJbW1oLdI5vzs0cru6WNoa0CeQNRBMMKmtg1amiCIMBm4f6jVH7ioRCU/j5YxjkBoVKIFgtav/7foSkKPDt/A00pTF0Q6UqENque3aOiyK2swNBWkU6e8wEAGrkTguHZLBKX/KCyEznbDQAwt5XOYtalyjxpElo3bIAy0A/vu7+HpuW/AzN65gykujpIVfruRCBaLBU/po2hrQKd7PFBANBQzdBmdDazzNmjVHaiXWcgWK2QGxr1LoohVF91FRzXLkLk1EkEDuzP+/UjZ07DMkX/sYWi1Vrxi+vmNBFh27Zt+PGPf4xYLIavfvWrWL9+/bDjhw8fxqOPPgq/34/29nY8/vjjkGUZ3d3d2LRpE/r7+zFjxgxs3rwZVVVVWLduHVQ18Q9NOBzG6dOnsWvXLkSjUaxcuRJTp04FADQ1NeHFF1/M80emU+d8qHWYYZKZ2Y3OapHZ0pYF6y9j0eJxRLu7YJlySdku/C0JAAKetMfGu/Cvbc5cKJ5BBD/4E+TaWlhnXjrq+bIagZamq1GwWqFIltTPmqoierYbdXOvHFe58kmyWiu+pS1raOvp6cEzzzyDN998E2azGX/5l3+J66+/HpdddlnqnE2bNuG73/0uFi5ciIcffhivv/467rrrLjz++OO46667sHLlSvzoRz/CP//zP2PTpk148803U+/9n//zf+K2225DU1MT3n77baxevRpPPPFEYT4tQdM0nOzxobWpcjfcLSc2swS3P6J3MUoW6y/jUfr6oEWjIxbUzRR0jLi7QaZFf4HxL/wrCAKqr1sM1eeDd8+7WXcu0MLh1MbvF2psvxqoOh/aoj090BQFlin6jy8UrVaOact2wu7du7F48WLU1dXBbrdj+fLl+P/bu/MoKas7f/zvZ6m1q3qvaqDZXFBEFIzEoHEgxgUFOjAu34k4IcYMWSeJzMSjDhqio+OYoBjyVSdjlvOTwRHHfIWQg6AZB50AUSAKGNnXpmm6qqt6qb2equf+/ujugra3oruqnlrer3M4h36ep6o+VV11+1P33s+9mzZtSp1vampCNBrF9OnTAQC33347Nm3aBE3TsGPHDsyZM6fX8XNt374d+/fvx5IlSwAAe/fuxcGDB3H77bdj8eLFOHDgQMaeKHXxdkS7ihBYOVoUbFb2tA2G7VfhiZ0+BUgSzKN7Vyrq8Rh8Oz/s808k+f7vISkKKmbfAKWsDO1b3kHc5xvxfUaPHQWAYW8llkldw6PsaRuUx+OBy+VK/ex2u7Fnz54Bz7tcLrS0tKCtrQ0OhwOqqvY6fq5Vq1Zh6dKlUJSutcIsFgsWLlyIL3/5y3j33Xfx3e9+Fxs3boQ5zdWwa2pys53HQFwuY/ZkOx/7TnV9Ux0/uhxOx+CJm8mkDvuaTx8byX1lMq7hPJ7dboGrevCtW4Q/nPPXwW63oKrChtjxtoJ47xkRYyG1X4BxbVi+vH+iIoLEmdOw1tWhorb3FnsmkwKn09LnNgMdH85tBjrudFpy8viDnbPbzbAO8HuKigjiPbdxWmCbPxdN/+8NNP3yJUx/9qdQbLbBbzPI47SfOAK1vBz1V16KWGtrn9sM9toM9nyGc5tEmR2J1ta8eb+mI9OxDpm09VeJcu48g4HOD3W7Q4cOoa2tDTfccEPq2Pe+973U/2fPno1nnnkGR48exeTJk4cKEwDg8wWh68Ys/edyOeH1Bgx57POx+4AHqiLBrEgIBAfvZta0xLCucTqsfY4N974yGddwHy8cjsGbHHwIJhzL/esQDscAXUc4quX9ey8bnw9ZloZMcgqp/QKMacPyqe0SZ1oR97aibPpVCAR6D/tbtGSfY4MdH85t+jvudFoQCMRy8viDnTOH4wgM8HtSwvHet1FtKL/+C2h/52385WcvYNR9S/rMD+xzmwEex79nL6wXT0KrL9TnNkO9NoM9n+HcRldN0EKhvHm/DmWwz1Y67Ve/txvqgrq6OrS2tqZ+9ng8cLvdA573er1wu92orq5GMBhMTdjtOd7jD3/4A+bOndvrsVavXo22trbUz0KI1DddyozjzZ0Y63ZAlotzgm+psZlVJJICWiI3+w8WGrZfhSW4bx8ADHuDeDrLPGYMaufMQWD7NnRu/eOw7kNr9SLR2grbpel/8cgmhcOjQydt1113HbZv3w6/349IJIK33noLs2bNSp2vr6+HxWLBrl27AADr1q3DrFmzYDKZMGPGDGzcuLHX8R4fffQRZsyY0euxduzYgddffx0A8MEHH0DXdVx44YUjf5YEAEjqOo63BDChrnC6lmlwPVtZcV5b/9h+FZbgvn2QbTaoVdVGh1IUaufMgW3yZfC8sjq1QO75CHfPy7TnSdImW60QiUTWFhEuBGn1tC1duhSLFy/GwoULMX/+fFx55ZVYsmQJ9u7dCwBYsWIFnnrqKdx2222IRCJYvHgxAGD58uV47bXXMHfuXOzcuRP3339/6n4bGxtRV1fX67GWLVuGbdu2Yf78+Xj66afxzDPPQJa5LEWmNLeGEdd0TBjFpK1Y2Cxd86m4K0L/2H4VDqHrCO3fD/OY+qJd6iPXJFnG6CXfhGy1ovnF58+78jJyYD9khwPmMfmxyLFs6ZrnVsoVpGn13Tc0NKChoaHXsZdeein1/8mTJ6e+YZ6rvr4eq1ev7vc+e77Bnquurg6/+c1v0gmJhuFYcycAYHydEydbCmNOAA3OZu7eNJ4L7A6I7VdhiJ04Dj0S6VM1SiOjVlRi9JJv4dSzP0XLmpcx+uvfSPu24YP7Yb/kUkh58uWjZx9aPRKB4jC28NAo+fGboJw41twJm0WBq6pvJREVJiuHR6lIhPd1rVtmGjXK4EiKj/2yKaie/yUEtm9DR5rz22JNTV3z2SZfluXo0pdK2kq4p41JWwk51hzAxFHlkDn0UDTsTNqoSIT374Nl1CgotsGX16HhqWlYANulk+FZ8zJip08PeX3H/26BpKpwfvaaHESXnrPDo6VbjMCkrURoiSROeYO4YHT50BdTwbByThsVAV3TEDl8CPZLLjE6lKKVmt9msaD5Fy9Aj8cHvFaPx9G5bRscn7kaqjN//mYo3T1tyRKuIGXSViIaPSEkdYGJLEIoKj1z2iKc00YFLHrkMEQ8jjImbVmlVlZh1Ne/gXjTKbS88caA1wV37YAeDqFi1hdyF1waeoZH+9sztVQwaSsRJ7oLD1g5Wlx6lvyIsqeNClh4/z5AkmC/aPBNzmnkyqZegarb5qF92zZEDu7vc17oOtrf+W+Y6uryZn22Hj1JW5LDo1TsTpzpRJlVRW0F9xwtJiZVhqpICHNOGxWw8P59sEyYCMXO+Wy5ULvgr+GYMgWB9/+EyOFDvc55NmxA9NhRVM9tyLulV86tHi1VTNpKxPEzAUwY5cy7DyGNnNWscskPKlh6PI7osaN5s4BrKZBUFfX33Qfz6DEIbN+Kjve2INZ0CsEPd8H/zjuo+MIXUfH5640Osw+5ex/fUq4e5R4rJUBL6GjyhnDLNeOMDoWywGZRWIhABSt69AiQTMJ2yaVGh1JSZJMJFV/4IkJ79yC87xPEThwHADimTIH7y4uMDW4AkixDsliZtFFxa2oNIqkLbl9VpGwW9rRR4YocOghIEmwXTwLALx+5JKkqHFd9BrbJk5Fsa4daVQXXX12HZB7vmSvbSnv/0fz9zVDGHD/TVYTAytHiZDOrnNNGBSty8AAsY8dCKSsDQh1Gh1OSFJu9YNbHU6w29rRRcTtxJgC7RYWrkjshDIckSwgNkRTpIkfB9MNmUeHvLN1GjAqXSCQQOXIYFdfPMjoUKhCS1VrSi+syaSsBLEIYmZiWxO6D3kGvmXaJK0fR9GXlnDYqUNGTJyDicc5no7QpNltJD4+yerTIJZI6mrxBrs9WxGwWlYvrUkGKHDwAALBN4qK6lB65xIdHmbQVuSZvCIkkixCKmc2sIhJLQAgDx2iJhiFy+BBMdXVQKyqMDoUKhFziw6NM2opcz04ILEIoXjaLgqQukEjqRodClDYhBKJHjsB24cVGh0IFpKt6tHR72jinrcgkdCCmnZ3fdLipA1azArvdlJpMb+Skeco86zn7j5pUxeBoiNKjeb1IBjph5dZVaVEkDFhdK+ulMz1Cttqgx6IQQpTkPG0mbUUmpiWwY19L6ud9J9pQ4TBj135P6piRk+Yp8+yWnqQtgfIys8HREKUneuQwAMB2EXva0qHHY/Dt/qTfc7XTpuQ4GuPIViuQTEJoGiRz6bV3HB4tYrou0BaIoaac+40WM6ulq3eNFaRUSCJHD0OyWGGuH2t0KFRAZFvX0lWlWozApK2ItQdj0HWBaiZtRc12zvAoUaGIHjkC6wUXQJL5Z4jSJ1u7k7YSXfaDn5Yi5uuMAQB72oqcrXt4NMpdEahA6LEYYqcaOTRK5022dv09K9UKUiZtRczfGYWqSCgvMxkdCmWRrXt4lFtZUaGIHjsK6DqLEOi8lfrwKAsRipivI4rqcmtJVtiUEmtPT1ucw6OUP9RkDGKAP6zxIwcBALYLmLRlUzFWnJb68CiTtiLVU4RwybhKo0OhLDs7p409bZQ/RDQK384P+z0XOXYMploXFCfXj8ymYqw45fAoFaWOUBxJXaCmwmJ0KJRlJlWGqsisHqWCET15EpaJFxgdBhUg2daTtJXm8CiTtiLl6+h6Q7NytDTYLAqrR6kg6NEoNL8f1okTjQ6FCtDZ4VEmbVREfKkihNJbfLAU2cwqq0epIGh+HwDAyp42GgbJbAYkicOjVFz8nVFUOa2QWYRQEqwWhXPaqCAkfK0AAMv4CQZHQoVIkiTINlvJFiIwaStCuujeCYHz2UqG3aIiwupRKgCazwez2w3Fbjc6FCpQTNqoqHQG40gkBRfVLSFWs8qeNioICV8rrOPGGR0GFTDZZkcyEjY6DEMwaStCvk4WIZQaG4dHqQAkI2Ho4TBs48cbHQoVMIU9bVRM/J0xKLKEChYhlAybReXiupT3En4/AMA6lpvE0/BxeJSKij8QRZXTAllmEUKpsFm6hkeFEEaHQjSgnqTNUl9vcCRUyJi0UdEQ3UUIVU4WIZQSq1lBUhfQErrRoRANKNHmh+xwQOneP5JoOGSbnUkbFYe2QAxxTUdVOZO2UmLr3n+UFaSUzxJtfqhV1UaHQQVOtlqRjIRLcmSBSVuRafKGAADVThYhlJKepI0L7FK+EpqGZGcnTEzaaIQUux1IJiESmtGh5ByTtiLT5A0CAIdHS0xq03juP0p5KtHeBgBQq5m00cjI3cPrerj0hkhVowOgzDrlDcFpN8GkMh8vJTaLAgCIRJm0UX5KtHUnbVVVUCQAoY4+18g6h/dpaKmkLRIBKioMjia3mLQVmSZvENXsZSs5PcOjYW4aT3kq0eaHZDJBLnNAj8fg2/1Jn2tqp00xIDI610AJNZA/SfXZTeNLb4FdJm1FJBJLoLUjirHuWqNDoRxLFSJwThvlKa27CEHifsh5baCEGsifpFru3gItWYIVpBxDKyKNnq75bOxpKz12a09PG5M2yj9CCCTb2jifjTJCOXd4tMQwaSsiPUkbl/soPalCBCZtlIf0YBAikYBaWWV0KFQEZCZtVAwaPQHYrSrsFo56lxpZlmA1KwizEIHyUKpytLLS4EioGJTynDYmbUXkZEsQY10OzhkpUT1bWRHlm0R7OwBAqWDSRiPHnjYqeEldR1NrCPWuMqNDIYPYrSrntFFeSnS0Q7aXQTabjQ6FioCkKJAsFiZtVLjO+CPQEjrqXQ6jQyGDsKeN8lWyvZ1Do5RRss3G6lEqXI0tAQDAWPa0lSy7ReWcNso7QteR6OyAwqSNMkix2jinjQpXoycIVZFQV203OhQyiJ09bZSHksEgkExC5Xw2yiDZbuPwKBWuk54gxtSUQVX4Ky1VNs5pozyUZOUoZYFsszNpo8LV2BLAuDrOZytlPT1tQgijQyFKSXSwcpQyT7axp40KVEcwhs6whvFup9GhkIHsFhVJXSCu6UaHQpSSaG+HXFYG2WQyOhQqIrKVhQhUoE5274Qwzs2etlJ2dtN4DpFS/kh2tHMnBMo4xcZCBCpQPdtXcXi0tHH/Uco3QggkOjuhVFQYHQoVGdluh4jHIRKl1d4xaSsCJ1sCqCm3oszK4YdS1tPTxgpSyhd6qLtytJxJG2VWaleEaNTgSHKLSVsRaPQEOTRKqT1nuVYb5YtERwcAQGHSRhl2dv/R0prXxqStwMW0JM74wxjPodGSx542yjfJzk4AgFpebnAkVGx6etqSJTavjUlbgWvyhiAEMI6VoyWPc9oo3yQ7OyCZzZCsVqNDoSKj2LsWkmdPGxWUk56u7atYhEDsaaN8k+jogFJeAUmSjA6FikxqThuTNiokjZ4gbBYFtRX8JlvqzKoMRZY4p43yRrKzk0OjlBVnkzYOj1IBaWwJYpzLAZnfZEueJEmwcf9RyhPJaBR6JMwiBMoKuXt4tNQW2E0raduwYQPmzp2Lm2++GWvWrOlzft++fbjjjjswZ84cLFu2DInudVNOnz6Ne+65B7feeiu+/e1vIxQKAQB27NiBz33uc1iwYAEWLFiAhx9+GADQ2dmJb3zjG7jttttwzz33wOv1Zup5FiVdiO7KUc5noy527j/aB9svY8Q9HgCAyjXaKAsUW/ectu7PZakYMmlraWnBypUr8corr2D9+vVYu3YtDh8+3OuaBx54AI8++ig2b94MIQRee+01AMBjjz2GRYsWYdOmTZg6dSpeeOEFAMDevXtx3333Yf369Vi/fj2eeuopAMBzzz2HGTNm4M0338Rdd92FJ598MtPPt6h42yOIaUnOZ6MU9rT1xvbLOD1JG3vaKBskVYVkNkMPc3i0l23btmHmzJmorKyE3W7HnDlzsGnTptT5pqYmRKNRTJ8+HQBw++23Y9OmTdA0DTt27MCcOXN6HQe6Gr2tW7di4cKF+Na3voXm5mYAwJYtW9DQ0AAAmD9/Pt577z1ompbZZ1xEGlu6dkLgch/Uw25ROaftHGy/jBP3eABJguLkSABlh1JWVnJLfqhDXeDxeOByuVI/u91u7NmzZ8DzLpcLLS0taGtrg8PhgKqqvY4DgNPpxLx583DTTTfhP//zP7F06VK8+uqrve5LVVU4HA74/X7U1dWl9WRqaoxNXlyu3DZOrTtPQZYlTJs8CmaTAgAQ/jCcjsGLEkwmdchr0r1uoGs+fWwk95XJuHL9eNl8Hex2C1zV9l7HKsutaPIGc/5eTIcRMRVS+wUY14Zl43fj9bVCdTpRXmnvc85kUuB0WtI+nqvbDHTc6bTkbcxG32aw1ybTsdntZljPea+ecjqgJuJ52d71yHRsQyZtQog+x84t3x7o/GC3e/zxx1PH7r77bjzzzDMIBAL9Pr4sp18r4fMFoet9HzcXXC4nvN7+n0O2HDjmw+hqOzraz37TCMcSCAQH39ZD04a+Jt3r+rvG6bD2OTbc+8pkXLl+vGy/DuFwDN5kstcxRQICoXjO34tDycbnQ5alIZOcQmq/AGPasGy1XZEWD+QyBwKBWJ9zFi15XsdzdZv+jjudFgQCsbyN2cjbDPXaZDo2cziOwDnvVd1sRaS9M+/aux6DfbbSab/6vd1QF9TV1aG1tTX1s8fjgdvtHvC81+uF2+1GdXU1gsEgkt1/VHqO67qOF198MXW8h6qqcLvdqftKJBIIBoOorKw87ydVKk5y+yr6FLuFhQjnYvtlHM3v59AoZZVit3NO26ddd9112L59O/x+PyKRCN566y3MmjUrdb6+vh4WiwW7du0CAKxbtw6zZs2CyWTCjBkzsHHjxl7HZVnG22+/jc2bN6eOT5s2DTabDbNnz8a6desAABs3bsSMGTNgMnET9P4EIxraAjEWIVAvdouKWDyJpK4bHUpeYPtljGQ4jGQoBNnBpI2yR7bbkQyzerSXuro6LF26FIsXL8bChQsxf/58XHnllViyZAn27t0LAFixYgWeeuop3HbbbYhEIli8eDEAYPny5Xjttdcwd+5c7Ny5E/fffz8A4Omnn8bLL7+MefPm4be//S2eeOIJAMAPfvADfPTRR5g3bx5eeeUV/OhHP8rW8y54jS1dXa7judwHnePsrgjJIa4sDWy/jKG1di13ojj4pZKypxR72oac0wYADQ0NqaqoHi+99FLq/5MnT8brr7/e53b19fVYvXp1n+OTJk3Cq6++2ud4ZWUl/u3f/i2dkEreSU9X5SiHR+lcqf1HoxocttLs5fk0tl+5p3WvUcfhUcom2V4GPRKB0HVI5zl/tFCVxrMsQo2eICocZpSXmY0OhfJIT9IW4rIfZKCzPW1M2ih7FLsdEAJ6tHR2RWDSVqBOtgQ5NEp9lFm7ete4VhsZSfN6odjtkM38UknZI9vLAKCkhkiZtBUgLaGj2Rfi0Cj1UZbqaSvdRV3JeJrXA1NNjdFhUJFL7T/KpI3yWbMvhKQuuBMC9WHv7mnj8CgZSWv1wlRba3QYVOSU7qSNPW2U1062sAiB+ld2TiECkRGErkNrbYWZPW2UZexpo4Jw0hOAWZVRV9V3exgqbWaTAlWR2dNGhkm0tQHJJIdHKevY00YF4ZQniLFuB2RZGvpiKjllVpU9bWQYzesBAPa0UdbJqaStdBbYZdJWYIQQ3ZWjHBql/pXZTOxpI8P0LPfBnjbKNtlqAySJw6OUv3ydUYRjCc5nowHZrSpCEfa0kTE0rxeQZZiqqowOhYqcJMuQbbaSGh5Na0cEyh+NPTsh1HGNNupfmUVFWyBmdBhUorRWL0zVNZAUxehQqMgoEoBQR+9jViv0znaoyRgSisWYwHKISVuBaWwJQgIw1lVmdCiUp+xWE055S2eOB+UXzeuFyeUyOgwqQno8Bt/uT3odEwCizWcgolGgrPiTNg6PFphGTxDuKhusZubb1L8yq4pwjMOjZAwmbZRLkskMocWNDiNnmLQVmJOeAIdGaVBlNhMisSSSum50KFRi9GgUyUAnTLVM2ig3ZLMZeoxJG+WhSCwBb3uUlaMEAJBkCaFYos8/Ve36WLd2xpBg3kY5lKocdbkNjoRKhWS2lFRPG8fYCkRCBw41dU3AdFXZEIr1v6SDLnIZFRkppiWx+6C3z/Ezvq75bO//5QxunDEOqoUfc8oNzduTtLGnjXJDMpsh4kzaKM/EtAS27W0GAPg6Itixr6Xf66Zdwsay1FlMXVV7cS1pcCRUalJJW60LAN9/lH2y2QyRSEAkS+P9xuHRAuIPxGAxKbCx54QGYTZ1faxjGsdGKbe0Vg9kmw1yGavbKTcksxkAkIxEDI4kN5i0FZC2zhiqyi2QJG5fRQMzs6eNDNJVOepmG0U5I/ckbSWywC6TtgKR1AXagjFUO4t/HRoamZ7h0ViCSRtln5qMQQl1QAl1QPO0wFxVCSXUAVnn+4+yr6enrVR2RWDSViA8/jB0XaC6nEkbDa5neDTO4VHKARGNwrfzQ7Tu+DPiXi+SWgK+nR9CJLn/LWWfbOn6m5gMlcaC4kzaCkRTa9cbssppNTgSyneKLEORJQ6PUk7pkTCg65AdXEeSckeydP1N5PAo5ZVTniBkSUJFmdnoUKgAmE0KYkzaKIeSga59kRUnkzbKHdnSPaeNPW2UT5q8QVQ6zZBlTvCloVlMModHKaeSwQAAQGFPG+WQZGIhAuUZIQROeUOoYhECpclsUjg8SjmVStq43AflkCTLkMwW9rRR/ugIxRGMaKjmfDZKk9mkIM49rCiH9GAQclkZJEUxOhQqMbLFwp42yh+Nnq65IqwcpXRZVJlz2iinkoEAh0bJEJLFzJ42yh8nW7qGHTg8SumymDk8SrmVDDJpI2PIFiuTNsofjZ4gqsstqZXuiYZiMSlIJAUTN8oJkUhAj0SgOB1Gh0IliMOjlFcaPUGMdbExpPRZzF0JfiiqGRwJlYJksHu5D/a0kQEkM4dHKU/E4kmc8YVR72JFFqWvZyurEr9Z6gAAIABJREFUUISr0lP2cbkPMpJssUKPxSASxd/eMWnLc6dagxAA6tnTRufB2t3TFoywp42yLxnoTtq4sC4ZQEptZRU0OJLsY9KW53oqRzk8SueDw6OUS8lgAJKqpv54EuWSbC6dXRGYtOW5xpYgbBaVy33QeekZHmVPG+VCMhiE4nRCkrhjC+We3LP/aJA9bWSwk54AxrkdbAzpvJyd08akjbIvGQhALuNoABmjp4dX5/AoGUkXAqc8IYx3szGk8yPLEsyqjFC0+CfmkrGEEKmeNiIjyKk5bRweJQN52yKIaUmMY9JGw2AxKxwepaxLBgJAMsHKUTJMqhCBw6NkpJPdRQjj69gY0vmzmBQOj1LWxX0+AKwcJeNIqgooSkkkbarRARCQ0IGY1ncY6+jpDsiyhAqnBbowIDAqaFYzkzbKPq0naXNwRICMIUkSlLKykljyg0lbHohpCezY19Ln+MfH/Ci3m/DRIS+mXeIyIDIqZBazAl9H1OgwqMjFmbRRHlDsduic00ZGagvEUF1uNToMKlAWk8JCBMo6rbUVst0OSWEfABlHKSsrieFRJm15KhpPIBxNoMrJ9dloeCxmBVpCR4ybxlMWaT4fixDIcIrdzupRMk5bIAYAXFSXhs3as8BumPPaKHviPh+HRslwXUkbe9rIIP7OrqSNPW00XBbuP0pZpmtxJDo6WDlKhlPKyqAHgxCiuKv2mLTlqbZADHarCquZ80RoeHqStkAkbnAkVKwSPh8gBIdHyXCqwwGRSEDEirv4iklbnvJ3RlHNXjYaASv3H6Us07xeAGDSRobr6e1NdAYMjiS7mLTloWRSR0cojipWjtIIpIZHOaeNskTzegAAspNz2shYave8ymSg0+BIsotJWx5qD8YhBNjTRiNiVhVIYE8bZY/m9UIymSBbbUaHQiWup6ct2cmkjXLM39k1Js/KURoJWZZgs6oIMGmjLNFaW2GqqYEkSUaHQiVOTQ2PMmmjHPMHYjApMhw2k9GhUIFz2EwcHqWs0Vo9MNfUGB0GUWrZGQ6PUs75O2OoKrfw2yuNWJnVxOFRygohBDSvFyYmbZQHZFWFbLNxeJRySwiBtkCU67NRRjjLTOgMcckPyjw9GIQejbKnjfKGUl7OnjbKrUBYQyIpOJ+NMqLcbkYHkzbKgnj3ch+m2lqDIyHqojjLkQhwyQ/KodT2VU4u90Ej57SbEYxoSCR1o0OhIqO1di33wZ42yheqs5zDo5Rb/s4oJAmodJiNDoWKQHlZVzFLgMUIlGE9C+tyThvlC6XcyeFRyi1/IIaKMjMUhb8aGrnysq7kvyMUMzgSKjaa1wulogKymV8wKT8oznIkg0EIvXhHFpgZ5Jm2zhiquRMCZYjT3vUHlcUIlGlaqxemWpfRYRClKOXlgBBIBoNGh5I1TNrySDSeQDiWYOUoZUx5d9LWEWTSRpmleT0wuZi0Uf5QneUAgGRnh8GRZA+Ttjzi7+wuQmDlKGWIs3tOGytIKZNEIoGE3w+Ty210KEQpSnl30lbEFaRM2vJIT+VoFStHKUPMqgKbReHwKGWU5vMBQnB4lPKK0t3TVsxbWaWVtG3YsAFz587FzTffjDVr1vQ5v2/fPtxxxx2YM2cOli1bhkQiAQA4ffo07rnnHtx666349re/jVAoBAA4cuQIFi1ahAULFuBv/uZvsG/fvtT1V111FRYsWIAFCxbg61//eqaeZ0Hwd0Zht6qwmhWjQ6EiUl5mKemeNrZfmae1dleOcniU8oia6mkr4aStpaUFK1euxCuvvIL169dj7dq1OHz4cK9rHnjgATz66KPYvHkzhBB47bXXAACPPfYYFi1ahE2bNmHq1Kl44YUXAACPPPIIlixZgvXr1+P+++/Hgw8+CADYu3cvGhoasH79eqxfvx6/+tWvMv1881pbIIZqzmejDKsoK90Fdtl+ZUcqaWNPG+UR2W4HZLmo12obMmnbtm0bZs6cicrKStjtdsyZMwebNm1KnW9qakI0GsX06dMBALfffjs2bdoETdOwY8cOzJkzp9dxALjrrrswa9YsAMCll16K5uZmAF2N3sGDB3H77bdj8eLFOHDgQGafbR5LJHV0hOKsHKWMKy8zl+zwKNuv7NC8XkiqCrWy0uhQiFIkWYbidCJRxD1t6lAXeDweuM7pAne73dizZ8+A510uF1paWtDW1gaHwwFVVXsdB7oawB6rVq3CTTfdBACwWCxYuHAhvvzlL+Pdd9/Fd7/7XWzcuBHmNNcBqqlxpHVdtrhczmHdTvjD0JKAEMAYtwNOR9/EzWRS+z0+nOtycV+fPpYvceX68Yx+Hex2C0bVlmHfcf+w35+ZYsTjF1L7BRjXhp3v78bX2QZrnRvuugpEPTHE+xkhMJkUOAcYORjonNG3Gei402nJ25iNvs1gr00uY7PbzbC6nDhVVQk1FjG8veuR6TiGTNqEEH2OSZI05Pl0bveTn/wEu3fvxssvvwwA+N73vpc6P3v2bDzzzDM4evQoJk+ePFSYAACfLwhd7/u4ueByOeH1Dq9iJRxL4FRL1zcDm0lGIBjtc42mJfo9Ppzrsn1fToe1z7F8iCvXj5cPr0M4HINJlhCKJnC6uR0m1Zj5kiP5fAxElqUhk5xCar8AY9qw4fxugo1NUKpq4fUGoITjCAT6Lt5s0ZL9Hh/snNG36e+402lBIBDL25iNvM1Qr00uYzOH4wh4A4DNgXCrL+PtzXAM9tlKp/3q93ZDXVBXV4fW1tbUzx6PB263e8DzXq8Xbrcb1dXVCAaDSCaTvY4DQCKRwA9/+EPs3bsXL7/8MpzOrkx09erVaGtrS92XECL1TbfY+QMxmFQZDpvJ6FCoyFSkdkUovSFStl+ZJ4RA3OOBua7O6FCI+lAqK5BoL+F12q677jps374dfr8fkUgEb731Vmo+BwDU19fDYrFg165dAIB169Zh1qxZMJlMmDFjBjZu3NjrOAA8/fTTCAaD+PWvf51q8ABgx44deP311wEAH3zwAXRdx4UXXpi5Z5vH/J1RVDktvb7NE2VCKSdtbL8yL9nZARGLwsSkjfKQWlmFREd70W5lNeTXwLq6OixduhSLFy+Gpmm48847ceWVV2LJkiX4/ve/jyuuuAIrVqzAI488glAohClTpmDx4sUAgOXLl+Ohhx7Ciy++iNGjR+PZZ5+F3+/HmjVrMHbsWNx1112px1m/fj2WLVuGhx56COvXr4fFYsEzzzwDWS7+peR0IdAWiOHi+gqjQ6Ei1LP/aCkWI7D9yjzN4wEAmN1cWJfyj1pVBSSTSAYCUCuK729qWn33DQ0NaGho6HXspZdeSv1/8uTJqW+Y56qvr8fq1av7HP/kk0/6fZy6ujr85je/SSekotLaHkUiKVDFylHKglLuaQPYfmVa3NNVkGFysaeN8o9aWQUASLS3FWXSVnxfAwtQk7drc1uu0UbZkOpp4/6jlAFaSwugKDDV1hodClEfqaTtnPmlxYRJWx5o9AQhSUClI/2lAYjSpSpdBS7twf6rrojOR9zjgammFpLCnVso/6hVZ3vaihGTtjzQ2BJAldMCReGvg7Kj2mmBf4BSeaLzoXlaYOJ8NspTank5IElM2ig7hBA42RJEDeezURZVl1vh7xx67TeiwQghoHlaWIRAeUtSFCjlFUi0tRsdSlYwaTOYtyOKcCyBmgombZQ9VeUW+DvZ00YjkwwEoEejMLlHGR0K0YDUqir2tFF2HG/u2gmBPW2UTdVOC8KxBKLxhNGhUAHTeipH2dNGeUytrGQhAmXH8TMBqIqESlaOUhZVd38pYG8bjUS8e/9Vs5vLfVD+Yk8bZc3x5k6MqXVAkbkTAmVPz3Iy/gDntdHwad4WQJK43AflNbWyCno4DD1WfF9SmbQZSBcCJ1oCGF93/pvGEp0P9rRRJsSbm2FyuSEV4Z6qVDzOLrBbfMUITNoM5GmLIBJLYnydc+iLiUagymmBBLCClEYkfuYMzKNHGx0G0aCKea02Jm0G6ilCYE8bZZuqyCgvM3OtNho2kUxCazkD8ygmbZTfzt3KqtgwaTPQ8TMBmFQZo2rKjA6FSkB1uQVt7GmjYdJaWyESCZhHjzE6FKJBpXrairCClEmbgY43d2J8HYsQKDeqnVb2tNGwxZtPAwCHRynvKTYbJIuVPW2UOboucKIliIl15UaHQiWiZ4FdIYTRoVABip9pBgAOj1JBMNVUQ/P5jA4j45i0GaTZH0ZMS2LiaBYhUG5UO62IaUmEY1xgl85fvLkZSnk5lDJO56D8Z6p1IdHqNTqMjGPSZpATZ7qKECaOYtJGuVFd3r1WG5f9oGGIn2nmfDYqGCaXG5rXW3QjC0zaDHK8OQCzScZoFiFQjpxdq43FCHR+hBCIN5/m0CgVDFNtLfRoFHowaHQoGcWkzSDHzwQwoc4JmUUIlCO1FV1JW2sHkzY6P8nOTujhMIsQqGCYXF3748a9xTVEyqTNAEldx8mWACaOYhEC5U5FmRkWk4KWtrDRoVCBOVs5yuFRKgwmlwsAoLV6DI4ks5i0GaDJG0I8oeMCFiFQDkmSBHeVDZ62iNGhUIGJn24CwOU+qHCYaruTtiLraeMGcgY43NQBALi4vsLgSKjU1FXZ0OgprjkelH2xU42Q7WVQq6qNDoWoX4oEINRx9mcAitOJpLfFsJiygUmbAQ43daDCYUZN9xwjolypq7bjw0OtSOo6FJkd7ZSeWGMjLOPGQZI4B5fykx6Pwbf7k17HJKsV8ZbiStrYahvg8KkOXFxfwQaQcs5daUNSF/CxGIHSJHQdsVONsI6qgxLq6PNP1pNGh0jUL8XhLLoFdtnTlmPtwRhaO6K48eqxRodCJaiu2g4AaGmLwF1lNzgaKgSapwVC05CIJ+Db+WGf87XTphgQFdHQFIcTsePHIBIJSGpxpDvsacuxI5zPRgaqq7IBAFr8rCCl9MQaGwGA89mo4CgOByAENL/f6FAyhklbjh1u6oCqyBhfx8pRyr3yMjMsZgUtrCClNMVONQKyDLWSXzSpsCjOrr+zmrd4lv1g0pZjh5s6MHG0EyaVLz3lniRJqKuyca02Slus8SQsbjckpTiGl6h0KA4mbTQCMS2J480BDo2Soeqq7PD42dNG6YmdaoSlvt7oMIjOm2y3QzKbEW9uNjqUjGHSlkOHmzqQ1AUmj68yOhQqYXXVNrR2RJFI6kaHQnkuGQwi4ffDMoY7IVDhkSQJltGjEWs6ZXQoGcOkLYf2n2iDLEmYNJY9bWScuio7dCG4BykNKXriOADAOpbV7lSYLKNGId7UZHQYGcOkLYcOnGzHxNFO2CycG0LGGVNbBgA4xZ0RaAjRo0cASYJtwgSjQyEaFsvo0UgGOpHo7DQ6lIxg0pYj0XgCx5o7OTRKhhvrKoMsSTjJpI2GEDlyBObRo6HYbEaHQjQslu79cnv2zy10TNpy5PCpnvlslUaHQiXOpCoYXWvHyZaA0aFQHhNCIHr0CKwXXmx0KETD1pO0Fcu8NiZtObL/ZDsUWcLFnM9GeWC828GN42lQWksL9HAItgsvMjoUomFTy8shl5UVzbw2Jm058slxPy4YXQ6rmfPZyHjj3E60BWLoDMeNDoXyVOTIYQCA9SL2tFHhkiQJlvqx7Gmj9HWE4jh+JoArLuQ2MJQfxtc5AIC9bTSg6NEjkG02mLuHl4gKlbm+HvHTTRBCGB3KiDFpy4G9R3wAgCsvqjU4EqIuPduoNbYwaaP+RY8ehvWCCyHJ/DNBhc0yZiz0SASJtsLfg5SfxhzYc9SHCoc51btBZDSHzYTqcguLEahfyXAIsVOnODRKRcHSvc5g7ORJgyMZOSZtWZZI6vjLMT+uuLAGkiQZHQ5Ryni3k8t+UL/C+/YBQqBsyuVGh0I0YpYJEwFFQeTwIaNDGTEmbVl2pKkDkVgC0y6qMToUol7G1znQ7AshEksYHQrlmfAnH0O2WmG94EKjQyEaMdlshnXCBES7i2sKGZO2LPvocCsUWcKUiSxCoPxy6bhKCAEcOtVudCiUR4QQCP3lY9gmXwZJZbU7FQfbRZMQPXYUuqYZHcqIMGnLIl0I7NzvweUXVHPrKso7F9VXQFUk7DvRZnQolEc0jweJ1laUXT7V6FCIMsZ68SSIRAKxkyeMDmVEmLRl0dGmTvg6Y7jmMrfRoRD1YTYpuGhMBfafYE8bnRX+5GMAgH0KkzYqHraLJwFAwc9rY9KWRe/va4FJlXHVJJfRoRD1a/KEKpxsCSAYKewhA8qc0F8+hqnWBZObXzapeKgVFTC53EzaqH+6LrBjvwdXXlTDoVHKW5dNqIIAcLCRvW0EJCMRhP/yMcqunMZqdyo6tosnIXr4UEEvssukLUv2nWxDZyiOaRfXIhRLDPpPL9z3DxW4C0aXw6zK2M95bQQg9OGfITQNzs/NNDoUooyzXXIJkoEA4qcajQ5l2NgFlCVbPmyC3aoiGk9gx76WQa+ddgmHT8kYJlXGpLEV2HvMDyEEe1dKXOf726HW1sLKTeKpCJVdOR2QJAT+vAuWceONDmdY2NOWBf7OKD482IprLx8FVeFLTPnt6kvdaPGHcZJbWpW0REcHwvs+Qfk1M5m8U1FSKypgu3gSgh/+2ehQho0ZRRZs+eg0hBC4fho3Wqb8N2OyG4os4U+fnDE6FDJQYNcOQNfh/Ny1RodClDWOq65G/FQj4h6P0aEMC5O2DNMSOt77qAnTLq5FbYXN6HCIhuSwmXDFhTV4/5MW6JxgWZKErqNjyzuwjJ8AS3290eEQZY3jM58BAAQ/3GVwJMPDpC3D3tt9Gp1hDTfOGGt0KERpm3l5HdqDcRxgFWlJCu3Zjfjp06i96UYooY4+/2Q9aXSIRBlhqnXBMm48grt2Gh3KsLAQIYOi8QQ2bD2GyeMrMWVCFcJxNnRUGKZfXAurWcH/7jmNyyZUGR0O5Vjb5jehVtfAOXUqfDs/7HO+dtoUA6Iiyg7nzGvR+l9rET1+HNaJE40O57ywpy2D3trRiM6whjtmX8SJvFRQzCYFs6ePwfuftKDZFzI6HMqhzn37ETl0EFW33ApJUYwOhyjrKv5qNmSrFW1vbTI6lPPGpC1DPP4w3nz/JK6aVIuL6iuMDofovN02cwLMqoL1fzxmdCiUI0LXceyXv4ZSXo6K6//K6HCIckKx21Ex6wsI7PwAmq/V6HDOC5O2DNCFwMpX/wwJwJdvnGR0OETDUm4348arx2LHPg9Oebj8Rylo/5//RvDwEbi/fA9kq9XocIhypvKmmwFJQtvmN40O5bwwacuAzR+cxMdHfFh00yVwVbJilPKHJEtD7sgRiiWQ0Luuv/Vz42G3qvjl7z+BluCczGKmtXrhe+O3qPzMVXB89hqjwyHKKVN1DSqu/yu0/887iBw9anQ4aWMhwgh9sK8Fr//PEVx35Wh8/opRRodD1EtMS2L3Qe+Q1332sjqoFhUOmwlfnz8Fq17fg1f/+zC+MufSHERJuZYMh9D0s5WALOOiby1BgHNwqQTV3vF/ENqzBy2/+SXG/+jHkE1mo0MaEnvaRuDPB714acMnuHhsBZbe/RkWH1BRmH5xLW793Hj8z4dN+N0fjxX05srUlx6N4PQL/xdxTwvGfOd7sNbVGR0SkSEUux11X/0a4s2n4Vn9/0HoutEhDYk9bcOQ1HWs/+Mx/H7bCUwc5cQP7pwGq1lFwOjAiDLkjtkXIhCKY90fj8HbHsHdN10Cu5XNRaGLez04/X9XIX66CaPuWwL75MuMDokoqxQJQKij33OS1YqyqVegZsFfw7f+DUAAdffel9dV1Gm1whs2bMCLL74ITdNw77334p577ul1ft++fXjkkUcQDAYxY8YMPPbYY1BVFadPn8YDDzwAn8+HCy64ACtWrEBZWRk6Ozvxwx/+EI2NjaiursZzzz0Hl8uFeDyOZcuW4eOPP4bVasWKFStw0UX5s3Gxrgt8eKgV/++9I2j2hTFr2mgsuukSmE35+wsmGg5FlnHfvMtQU2HFhq3HsfuID/OunYBrp45CuT3/hxDOxfYL0KNRtL29Gf5NGyEpKuqX/hBlUy43OiyirNPjMfh2f9LvuZoZVwFlFtQ0LAAA+Na/gVjTKdQt/lrert825PBoS0sLVq5ciVdeeQXr16/H2rVrcfjw4V7XPPDAA3j00UexefNmCCHw2muvAQAee+wxLFq0CJs2bcLUqVPxwgsvAACee+45zJgxA2+++SbuuusuPPnkkwCA1atXw2az4c0338Q//dM/4aGHHsr08z0vQgi0B2P48KAX//mHQ/jhC1vx/Bt7IQTw97dfgXtvu4wJGxUtSZKw8K8uxI/u/SzGusqw9p3D+Iefb8W/rvkzfvvuEfzpL2dw+FQH2gIx6Hk6hFqq7ZcQAon2NgR27sCZX/8SR/7xfvjWv4GyqVdgwo9+zISN6FNqGhZg9Le+g0RHO04+8WM0rngaHX/8X8Q9nryaIjJkT9u2bdswc+ZMVFZWAgDmzJmDTZs24e///u8BAE1NTYhGo5g+fToA4Pbbb8eqVatw1113YceOHXj++edTx//2b/8WDzzwALZs2YI1a9YAAObPn4/HH38cmqZhy5Yt+MEPfgAA+OxnP4u2tjacPn0aY8aMSevJyHJ6c8pOnAngk+NtSOo6kkJA6AJJXSCZ1BGOJRGKJRCJaugIxhHTuiroFEXC9EkufOYSF6ZeUAVZ7pvvfvrxVUWG3WoaMp50riuk+7JZVCQTpiGvy3VcuX68QnkdAEA1KYgl+p/PMaq2DD/4P9Ph64xh7+FWHDrVjl0HvNix/+yGy4oswWJWYTHJsJlVWCwKrCYFqiJDUWTYbSYktAQUWYYsS1BkCbIkQZIkpKaCSoDNrOLay0fBbBp6um06n/dCar/SfU56OIzO7VuhRyIQySREMgGRSEIkNCRDISQ62pHs6IDQtK77tFhQfeONcF4zE9bx4wd9XFlVoNr7VsDLqtrv8cHOFeJt+juuWM1Qk3LexmzkbYZ6bfL/+SgQ53zmKq75HJxXXInO7VsR2LED7Rt/h/aNgGQ2Q3GWQy0vh+J0QjJbICkyJFmFpMiAqkKSZEACyqZNh7nubFHiQJ/pdPOVTxsyafN4PHC5XKmf3W439uzZM+B5l8uFlpYWtLW1weFwQFXVXsc/fRtVVeFwOOD3+/u9rzNnzqTd6FVVlaV1XU2NA5+5fHRa156PmhpHn2NjR6e30O6FY4feOiida3hf+f14+XxfQxk7Gph2qTsj95UrhdR+AWm2YTUOuMb9ddr3mY6zbZcDlWP7bxsrLpgw4O0HOleIt+nvuGuQc5l+/EK7zVCvjZGxpXObvhxw3bUQuGvhedxmYP3lBSMx5NfZ/roFz62SHOj8ULfrE0g/PVeDHSciGgrbLyIqJkO2KHV1dWhtPbvNg8fjgdvtHvC81+uF2+1GdXU1gsEgkslkr+NA17fdntskEgkEg0FUVlbC7XbD6/X2uS8iouFg+0VExWTIpO26667D9u3b4ff7EYlE8NZbb2HWrFmp8/X19bBYLNi1axcAYN26dZg1axZMJhNmzJiBjRs39joOALNnz8a6desAABs3bsSMGTNgMpkwe/ZsrF+/HgCwc+dOWCyW8xpaICI6F9svIiomkkijLGLDhg34xS9+AU3TcOedd2LJkiVYsmQJvv/97+OKK67A/v378cgjjyAUCmHKlCl46qmnYDab0dTUhIceegg+nw+jR4/Gs88+i4qKCrS3t+Ohhx5CY2MjnE4nVqxYgbFjxyIWi+FHP/oRPv74Y5jNZjzxxBO4/HJWORHR8LH9IqJikVbSRkRERETG4ixZIiIiogLApI2IiIioADBpIyIiIioATNqIiIiICgCTthHasGED5s6di5tvvjm1tU0pWbx4MebNm4cFCxZgwYIF2L17d0m9JsFgEPPnz8epU6cAdG2b1NDQgFtuuQUrV65MXbdv3z7ccccdmDNnDpYtW4ZEImFUyFnz6dfi4Ycfxi233JJ6b7z99tsABn6NKLdK6XOarlJvz/rDNm5wOW/3BA3bmTNnxA033CDa2tpEKBQSDQ0N4tChQ0aHlTO6rovPf/7zQtO01LFSek0++ugjMX/+fHH55ZeLxsZGEYlExOzZs8XJkyeFpmnivvvuE1u2bBFCCDFv3jzx4YcfCiGEePjhh8WaNWuMDD3jPv1aCCHE/PnzRUtLS6/rBnuNKHdK6XOarlJvz/rDNm5wRrR77GkbgXM3o7bb7anNqEvF0aNHIUkSlixZgi996Uv4j//4j5J6TV577TUsX748ter9nj17MGHCBIwbNw6qqqKhoQGbNm3qd1PyYntNPv1ahMNhnD59Go8++igaGhqwatUq6Lo+4GtEuVVKn9N0lXp71h+2cYMzot0bcsN4GthQm1EXu87OTlx77bX48Y9/jGg0isWLF+O2224rmdfkySef7PVzf++HlpaWATclLyaffi18Ph9mzpyJxx9/HHa7Hd/85jfx+uuvw2639/saUW6VetvVn1Jvz/rDNm5wRrR77GkbAXGem0oXm6uuugo/+clPYLfbUV1djTvvvBOrVq3qc12pvCYDvR9K8X0ybtw4PP/886ipqYHNZsNXvvIVvPvuuyX5WuQj/h76Yns2NLZxg8tFu8ekbQSG2oy62O3cuRPbt29P/SyEQH19fcm+JgO9HwbalLyYHThwAJs3b079LISAqqol/5nJF/w99MX2bGhs4waXi3aPSdsIDLUZdbELBAL4yU9+glgshmAwiDfeeAM//elPS/Y1mTZtGo4dO4YTJ04gmUzi97//PWbNmjXgpuTFTAiBf/mXf0FHRwc0TcPatWtx8803D/gaUW6VetvVH7ZnQ2MbN7hctHuc0zYCdXV1WLp0KRYvXpzajPrKK680OqycueGGG7B7924sXLgQuq5j0aJFuPrqq0v2NbFYLPjXf/1XfO9730MsFsPs2bNx6623AgA+TvKzAAAEzElEQVRWrFjRa1PyxYsXGxxtdk2ePBnf+MY3cPfddyORSOCWW27B/PnzAWDA14hyp9Tbrv6wPRsa27jB5aLd44bxRERERAWAw6NEREREBYBJGxEREVEBYNJGREREVACYtBEREREVACZtRERERAWASRvljf/6r//CmjVrhn37L37xi9i7d28GIzrrvvvug9/vz/rjEFFhYvtFucCkjfLGrl27EI1GjQ6jX1u3bjU6BCLKY2y/KBe4uC6NSCgUwsMPP4wTJ05AlmVcfvnlePzxx7Flyxa8+OKL0DQNVqsVDz74IK666ir8/Oc/R1NTE7xeL5qamlBdXY2VK1diz549eOedd7B161ZYrVbMnDkTy5YtQzwehxACd955J+65556043rnnXfO6/Hr6uqwZ88e/PjHP4amaRg/fjxOnz6Nhx56COvWrQMAfPWrX8W///u/AwDWrl2L5cuXw+/3Y8GCBVi6dGlWXl8iyh62X2y/Co4gGoE33nhD3HfffUIIIRKJhFi2bJk4duyYmD9/vvD7/UIIIQ4ePCg+//nPi1AoJFatWiVuvPFGEQgEhBBCfPOb3xQ/+9nPhBBCPPjgg+KXv/ylEEKIhx9+WPziF78QQgjh8XjE/fffL5LJ5KCx3HDDDWLPnj3DenxN08SsWbPEli1bhBBCbN++XVx66aXiT3/6kxBCiEsuuUT4fL7U4zz++OOp2KZOnSpOnz6dgVeTiHKJ7Rfbr0LDnjYakauvvhorV67EV77yFVx33XX46le/iq1bt8Lj8eDee+9NXSdJEk6ePAkAuOaaa+BwOAAAU6ZMQUdHR5/7vfnmm/Hggw9iz549uPbaa/HII49AltMbzR/O4x88eBAAMHv2bADAzJkzMWnSpAEfo2drEpfLhdraWvh8PowePTqt+IgoP7D9YvtVaJi00YiMGzcOb7/9Nt5//3386U9/wte+9jXcfffduPbaa/Hcc8+lrmtubobb7cbbb78Nq9WaOi5JEkQ/O6ndcMMN2Lx5M7Zt24bt27fj+eefx6uvvorx48cPGZOu6+f9+Iqi9IlDUZQBH0NVz350BnoORJTf2H6x/So0LESgEXnllVfw8MMP4/rrr8cDDzyA66+/HgcOHMDWrVtx5MgRAMC7776LL33pS4jFYoPel6IoSCQSAIB//Md/xMaNGzFv3jwsX74cDocDzc3NacU0c+bM8378iy66CGazGe+99x4AYM+ePTh48CAkSeoTGxEVB7ZfVGjY00YjsnDhQnzwwQeYO3cubDYbxowZgyeffBLbtm3DP/zDP0AIAVVV8eKLL8Jutw96X7NmzcI///M/AwC+853vYNmyZVi7di0URcFNN92Ea665Jq2YJk2ahMcff/y8Hl9VVfz85z/H8uXL8eyzz2LixImora1Nfau9+eabsWjRIrzwwgtpvjJElO/YflGhkQT7RYkAAE8//TS+/vWvo7a2Fs3NzViwYAH+8Ic/oLy83OjQiIgGxfarNLCnjQrG7373O/zqV7/q91xDQwP+7u/+bkT3X19fj3vvvReqqkIIgSeeeIINHhFlBNsvygT2tBEREREVABYiEBERERUAJm1EREREBYBJGxEREVEBYNJGREREVACYtBEREREVACZtRERERAXg/wc2Wq0u/lR7WQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "ax=sns.distplot(df[df['Recommended IND']==0]['sents_length'])\n",
    "plt.legend(loc='upper right',labels=[0])\n",
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "ax.set_ylim(0,0.02)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "ax=sns.distplot(df[df['Recommended IND']==1]['sents_length'],color='r')\n",
    "plt.legend(loc='upper right',labels=[1])\n",
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "ax.set_ylim(0,0.02)\n",
    "\n",
    "df['sents_length'].groupby(df['Recommended IND']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>691.0</td>\n",
       "      <td>69.694645</td>\n",
       "      <td>30.554835</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1360.0</td>\n",
       "      <td>73.198529</td>\n",
       "      <td>30.652583</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>102.25</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2464.0</td>\n",
       "      <td>76.056006</td>\n",
       "      <td>30.679120</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>106.00</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4289.0</td>\n",
       "      <td>75.034973</td>\n",
       "      <td>32.360178</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>107.00</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10858.0</td>\n",
       "      <td>69.291582</td>\n",
       "      <td>32.558596</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>101.00</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count       mean        std  min   25%   50%     75%    max\n",
       "Rating                                                               \n",
       "1         691.0  69.694645  30.554835  4.0  46.0  69.0   99.00  124.0\n",
       "2        1360.0  73.198529  30.652583  9.0  47.0  74.0  102.25  139.0\n",
       "3        2464.0  76.056006  30.679120  5.0  51.0  77.0  106.00  133.0\n",
       "4        4289.0  75.034973  32.360178  3.0  48.0  76.0  107.00  129.0\n",
       "5       10858.0  69.291582  32.558596  2.0  41.0  67.0  101.00  131.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "ax=sns.distplot(df[df['Rating']==1]['sents_length'])\n",
    "ax=sns.distplot(df[df['Rating']==2]['sents_length'])\n",
    "plt.legend(loc='upper right',labels=[1,2])\n",
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "ax.set_ylim(0,0.02)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "ax=sns.distplot(df[df['Rating']==3]['sents_length'])\n",
    "ax=sns.distplot(df[df['Rating']==4]['sents_length'])\n",
    "ax=sns.distplot(df[df['Rating']==5]['sents_length'])\n",
    "plt.legend(loc='upper right',labels=[3,4,5])\n",
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "ax.set_ylim(0,0.02)\n",
    "\n",
    "df['sents_length'].groupby(df['Rating']).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reviews lengths for recommended and non recommended values have a simiilar distribution with a slight increase in density around the 100-150 range.  Rating is relatively similiar as well despite the recommended count being higher in 100-150 for 3-5 compared to 1-2. Class imbalance is present going forward. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import stopwords with nltk.\n",
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "import string \n",
    "#apply lower case, stop words, and remove punctuation\n",
    "df['Review Text']=df['Review Text'].str.lower()\n",
    "df['Review Text']=df['Review Text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "df['Review Text']=df['Review Text'].str.replace('[{}]'.format(string.punctuation), '')\n",
    "\n",
    "#Tokenize words by sentence\n",
    "df['tokenized_sents'] = df.apply(lambda row: nltk.word_tokenize(row['Review Text']), axis=1)\n",
    "#Sentence Length\n",
    "df['sents_length'] = df.apply(lambda row: len(row['tokenized_sents']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x72 with 0 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x72 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nltk.probability import FreqDist\n",
    "#Create list of token words\n",
    "lists =  df['tokenized_sents']\n",
    "words = []\n",
    "for wordList in lists:\n",
    "    words += wordList\n",
    "#remove 50 most common words based on Brown corpus\n",
    "fdist = FreqDist(words)\n",
    "mostcommon = fdist.most_common(50)\n",
    "mclist = []\n",
    "for i in range(len(mostcommon)):\n",
    "    mclist.append(mostcommon[i][0])\n",
    "# mclist contains second-most common set of words\n",
    "words = [w for w in words if w in mclist]\n",
    "# this will keep ALL occurrences of the words in mclist\n",
    "\n",
    "# Frequency Distribution Plot\n",
    "import matplotlib.pyplot as plt\n",
    "fdist.plot(50,cumulative=False)\n",
    "plt.show()\n",
    "plt.figure(figsize=(40,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nltk text cleaning and frequency seemed to work okay, but there still seems to some words like \"got\", \"even\",\"work\", and \"also\" present with high counts that don't really add much as potentially features in the top 100 words. The analysis gave 18346 different words, and 50 words only covers 0.27% with counts from 9266 to about 2000. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction-Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16462, 267)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(lowercase=True,stop_words='english',ngram_range = (1,3),max_features=250)\n",
    "text_count= cv.fit_transform(df['Review Text'])\n",
    "columns=cv.get_feature_names()\n",
    "\n",
    "#combine countvectorizer features with original dataframe for supervised learning \n",
    "df_n=pd.DataFrame(text_count.toarray(), columns=columns)\n",
    "df_feat= pd.concat([df, df_n], axis=1)\n",
    "df_feat=df_feat.dropna()\n",
    "df_feat=pd.get_dummies(df_feat,columns=['Department Name','Division Name'])\n",
    "df_feat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Target Recommended Review or not\n",
    "Y = df_feat['Class Name']\n",
    "X = df_feat.drop(['Class Name','Review Text','Title'],1)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.6493601166369675\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.56133979 0.5689446  0.57067639 0.58661258 0.57375051]\n",
      "0.57 (+/- 0.02)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Dresses</th>\n",
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       "    <tr>\n",
       "      <th>Class Name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blouses</th>\n",
       "      <td>574</td>\n",
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       "      <td>0</td>\n",
       "      <td>44</td>\n",
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       "    <tr>\n",
       "      <th>Casual bottoms</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>Chemises</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>3391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>352</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>29</td>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1923</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>14</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>37</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>57</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>22</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Dresses  Fine gauge  Intimates  Jackets  Jeans  \\\n",
       "Class Name                                                                \n",
       "Blouses             574        0          27          0        0      0   \n",
       "Casual bottoms        0        0           0          0        0      0   \n",
       "Chemises              0        0           0          0        0      0   \n",
       "Dresses               0     3391           0          0        0      0   \n",
       "Fine gauge          116        0          44          0        0      0   \n",
       "Intimates             0        0           0         38        0      0   \n",
       "Jackets               0        0           0          0      352      0   \n",
       "Jeans                 0        0           0          0        0    293   \n",
       "Knits               445        0          34          0        0      0   \n",
       "Layering              0        0           0          0        0      0   \n",
       "Legwear               0        0           0          0        0      0   \n",
       "Lounge                0        0           0         13        0      0   \n",
       "Outerwear             0        0           0          0       74      0   \n",
       "Pants                 0        0           0          0        0    148   \n",
       "Shorts                0        0           0          0        0     42   \n",
       "Skirts                0        0           0          0        0    103   \n",
       "Sleep                 0        0           0          6        0      0   \n",
       "Sweaters            198        0          18          0        0      0   \n",
       "Swim                  0        0           0          8        0      0   \n",
       "Trend                 0        0           0          0        0      0   \n",
       "\n",
       "col_0           Knits  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  \\\n",
       "Class Name                                                                   \n",
       "Blouses          1024         0        0       0          0      0       0   \n",
       "Casual bottoms      0         0        0       0          0      1       0   \n",
       "Chemises            0         0        0       1          0      0       0   \n",
       "Dresses             0         0        0       0          0      0       0   \n",
       "Fine gauge        420         0        0       0          0      0       0   \n",
       "Intimates           0         1        8      16          0      0       0   \n",
       "Jackets             0         0        0       0         48      0       0   \n",
       "Jeans               0         0        0       0          0    186      29   \n",
       "Knits            1923         0        0       0          0      0       0   \n",
       "Layering            0        14        0      21          0      0       0   \n",
       "Legwear             0         0       43      14          0      0       0   \n",
       "Lounge              0         4        6     285          0      0       0   \n",
       "Outerwear           0         0        0       0         91      0       0   \n",
       "Pants               0         0        0       0          0    426      37   \n",
       "Shorts              0         0        0       0          0     39      57   \n",
       "Skirts              0         0        0       0          0    156      22   \n",
       "Sleep               0         0        8      25          0      0       0   \n",
       "Sweaters          542         0        0       0          0      0       0   \n",
       "Swim                0         0        6      42          0      0       0   \n",
       "Trend               0         0        0       0          0      0       0   \n",
       "\n",
       "col_0           Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                            \n",
       "Blouses              0      0        44     0      0  \n",
       "Casual bottoms       0      0         0     0      0  \n",
       "Chemises             0      0         0     0      0  \n",
       "Dresses              0      0         0     0      0  \n",
       "Fine gauge           0      0        13     0      0  \n",
       "Intimates            0      6         0    13      0  \n",
       "Jackets              0      0         0     0      0  \n",
       "Jeans               94      0         0     0      0  \n",
       "Knits                0      0        51     0      0  \n",
       "Layering             0      1         0     4      0  \n",
       "Legwear              0      4         0    11      0  \n",
       "Lounge               0     21         0    42      0  \n",
       "Outerwear            0      0         0     0      0  \n",
       "Pants               98      0         0     0      0  \n",
       "Shorts              16      0         0     0      0  \n",
       "Skirts             200      0         0     0      0  \n",
       "Sleep                0     62         0    13      0  \n",
       "Sweaters             0      0        55     0      0  \n",
       "Swim                 0     10         0    97      0  \n",
       "Trend                0      0         0     0     72  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "bnb = BernoulliNB()\n",
    "bnb = bnb.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy',bnb.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "bnbscore = cross_val_score(bnb,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',bnbscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (bnbscore.mean(),bnbscore.std() * 2))\n",
    "\n",
    "bnb_predicted = bnb.predict(X_train)\n",
    "pd.crosstab(Y_train, bnb_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9990280252713429\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.87207425 0.89203397 0.89226407 0.90223124 0.90004063]\n",
      "0.89 (+/- 0.02)\n"
     ]
    },
    {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Casual bottoms  Chemises  Dresses  Fine gauge  \\\n",
       "Class Name                                                               \n",
       "Blouses            1669               0         0        0           0   \n",
       "Casual bottoms        0               1         0        0           0   \n",
       "Chemises              0               0         1        0           0   \n",
       "Dresses               0               0         0     3391           0   \n",
       "Fine gauge            0               0         0        0         592   \n",
       "Intimates             0               0         0        0           0   \n",
       "Jackets               0               0         0        0           0   \n",
       "Jeans                 0               0         0        0           0   \n",
       "Knits                 0               0         0        0           0   \n",
       "Layering              0               0         0        0           0   \n",
       "Legwear               0               0         0        0           0   \n",
       "Lounge                0               0         0        0           0   \n",
       "Outerwear             0               0         0        0           0   \n",
       "Pants                 0               0         0        0           0   \n",
       "Shorts                0               0         0        0           0   \n",
       "Skirts                0               0         0        0           0   \n",
       "Sleep                 0               0         0        0           0   \n",
       "Sweaters              0               0         0        0           1   \n",
       "Swim                  0               0         0        0           0   \n",
       "Trend                 0               0         0        0           0   \n",
       "\n",
       "col_0           Intimates  Jackets  Jeans  Knits  Layering  Legwear  Lounge  \\\n",
       "Class Name                                                                    \n",
       "Blouses                 0        0      0      0         0        0       0   \n",
       "Casual bottoms          0        0      0      0         0        0       0   \n",
       "Chemises                0        0      0      0         0        0       0   \n",
       "Dresses                 0        0      0      0         0        0       0   \n",
       "Fine gauge              0        0      0      1         0        0       0   \n",
       "Intimates              81        0      0      0         0        0       1   \n",
       "Jackets                 0      400      0      0         0        0       0   \n",
       "Jeans                   0        0    602      0         0        0       0   \n",
       "Knits                   0        0      0   2453         0        0       0   \n",
       "Layering                0        0      0      0        40        0       0   \n",
       "Legwear                 0        0      0      0         0       71       0   \n",
       "Lounge                  1        0      0      0         0        0     370   \n",
       "Outerwear               0        1      0      0         0        0       0   \n",
       "Pants                   0        0      0      0         0        0       0   \n",
       "Shorts                  0        0      0      0         0        0       0   \n",
       "Skirts                  0        0      1      0         0        0       0   \n",
       "Sleep                   0        0      0      0         0        0       1   \n",
       "Sweaters                0        0      0      2         0        0       0   \n",
       "Swim                    0        0      0      0         0        0       2   \n",
       "Trend                   0        0      0      0         0        0       0   \n",
       "\n",
       "col_0           Outerwear  Pants  Shorts  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                                                      \n",
       "Blouses                 0      0       0       0      0         0     0      0  \n",
       "Casual bottoms          0      0       0       0      0         0     0      0  \n",
       "Chemises                0      0       0       0      0         0     0      0  \n",
       "Dresses                 0      0       0       0      0         0     0      0  \n",
       "Fine gauge              0      0       0       0      0         0     0      0  \n",
       "Intimates               0      0       0       0      0         0     0      0  \n",
       "Jackets                 0      0       0       0      0         0     0      0  \n",
       "Jeans                   0      0       0       0      0         0     0      0  \n",
       "Knits                   0      0       0       0      0         0     0      0  \n",
       "Layering                0      0       0       0      0         0     0      0  \n",
       "Legwear                 0      0       0       0      1         0     0      0  \n",
       "Lounge                  0      0       0       0      0         0     0      0  \n",
       "Outerwear             164      0       0       0      0         0     0      0  \n",
       "Pants                   0    709       0       0      0         0     0      0  \n",
       "Shorts                  0      0     154       0      0         0     0      0  \n",
       "Skirts                  0      0       0     480      0         0     0      0  \n",
       "Sleep                   0      0       0       0    113         0     0      0  \n",
       "Sweaters                0      0       0       0      0       810     0      0  \n",
       "Swim                    0      0       0       0      0         0   161      0  \n",
       "Trend                   0      0       0       0      0         0     0     72  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "rfc = ensemble.RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "rfc = rfc.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy:',rfc.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "rfcscore = cross_val_score(rfc,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',rfcscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (rfcscore.mean(), rfcscore.std() * 2))\n",
    "\n",
    "rfc_predicted = rfc.predict(X_train)\n",
    "rfc_matrix=pd.crosstab(Y_train, rfc_predicted)\n",
    "rfc_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n= 2\n",
      "Accuracy: 0.9718937307630001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.94713479 0.94055803 0.93924666 0.94279919 0.93661113]\n",
      "0.94 (+/- 0.01)\n",
      "n= 3\n",
      "Accuracy: 0.972946703385712\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.95641646 0.9421755  0.94694208 0.94563895 0.94311256]\n",
      "0.95 (+/- 0.01)\n",
      "n= 4\n",
      "Accuracy: 0.9651709055564556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.94834544 0.94500607 0.94491697 0.94361055 0.93986184]\n",
      "0.94 (+/- 0.01)\n",
      "n= 5\n",
      "Accuracy: 0.9649279118742913\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.95076675 0.94621917 0.94491697 0.94685598 0.94555059]\n",
      "0.95 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "n=list(range(2,6))\n",
    "for i in n:\n",
    "    print('n=',i)\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn = knn.fit(X_train, Y_train)\n",
    "\n",
    "    print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "    #Cross Validation \n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "    print('Cross Validation Scores',knnscore)\n",
    "    print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.972946703385712\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.95641646 0.9421755  0.94694208 0.94563895 0.94311256]\n",
      "0.95 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Chemises</th>\n",
       "      <th>Dresses</th>\n",
       "      <th>Fine gauge</th>\n",
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       "    <tr>\n",
       "      <th>Class Name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casual bottoms</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Chemises</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>591</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>397</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>594</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>690</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>453</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Chemises  Dresses  Fine gauge  Intimates  Jackets  \\\n",
       "Class Name                                                                   \n",
       "Blouses            1668         0        0           0          0        0   \n",
       "Casual bottoms        0         0        0           0          1        0   \n",
       "Chemises              0         1        0           0          0        0   \n",
       "Dresses               0         1     3388           0          0        0   \n",
       "Fine gauge            0         0        0         591          0        0   \n",
       "Intimates             0         0        0           0         52        0   \n",
       "Jackets               0         0        0           0          0      397   \n",
       "Jeans                 0         0        3           0          0        0   \n",
       "Knits                 2         0        1           0          1        0   \n",
       "Layering              0         0        1           0          4        0   \n",
       "Legwear               0         0        0           0          2        0   \n",
       "Lounge                3         0        0           0         10        0   \n",
       "Outerwear             0         0        3           0          0        0   \n",
       "Pants                 0         0        6           0          0        0   \n",
       "Shorts                0         0        0           0          7        0   \n",
       "Skirts                0         0        0           0          0        3   \n",
       "Sleep                 1         0        0           0          5        0   \n",
       "Sweaters              0         1        0           2          0        0   \n",
       "Swim                  1         1        0           0          9        0   \n",
       "Trend                 0         0        1           0          0        0   \n",
       "\n",
       "col_0           Jeans  Knits  Layering  Legwear  Lounge  Outerwear  Pants  \\\n",
       "Class Name                                                                  \n",
       "Blouses             0      1         0        0       0          0      0   \n",
       "Casual bottoms      0      0         0        0       0          0      0   \n",
       "Chemises            0      0         0        0       0          0      0   \n",
       "Dresses             0      0         0        0       0          1      1   \n",
       "Fine gauge          0      1         0        0       0          0      0   \n",
       "Intimates           0      0         1        4      15          0      0   \n",
       "Jackets             0      0         0        0       0          2      0   \n",
       "Jeans             594      1         0        0       0          0      0   \n",
       "Knits               0   2448         0        0       0          0      0   \n",
       "Layering            0      0        29        2       0          0      0   \n",
       "Legwear             0      0         4       62       3          0      0   \n",
       "Lounge              0      0         5        8     325          0      1   \n",
       "Outerwear           0      1         0        0       0        159      0   \n",
       "Pants               1      0         2        0       7          0    690   \n",
       "Shorts              0      0         3        1      20          0      0   \n",
       "Skirts             25      0         0        0       0          0      0   \n",
       "Sleep               0      0         7       11      23          0      0   \n",
       "Sweaters            0      1         0        0       0          0      0   \n",
       "Swim                0      0         2        3      31          0      2   \n",
       "Trend               0      0         2        0       0          0      0   \n",
       "\n",
       "col_0           Shorts  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                                    \n",
       "Blouses              0       0      0         0     0      0  \n",
       "Casual bottoms       0       0      0         0     0      0  \n",
       "Chemises             0       0      0         0     0      0  \n",
       "Dresses              0       0      0         0     0      0  \n",
       "Fine gauge           0       0      0         1     0      0  \n",
       "Intimates            4       0      3         0     3      0  \n",
       "Jackets              0       0      0         1     0      0  \n",
       "Jeans                0       3      0         0     1      0  \n",
       "Knits                0       0      0         0     1      0  \n",
       "Layering             1       0      2         0     0      1  \n",
       "Legwear              0       0      1         0     0      0  \n",
       "Lounge               6       0      7         0     6      0  \n",
       "Outerwear            0       0      0         0     0      2  \n",
       "Pants                3       0      0         0     0      0  \n",
       "Shorts             116       0      2         0     3      2  \n",
       "Skirts               0     453      0         0     0      0  \n",
       "Sleep                5       0     62         0     0      0  \n",
       "Sweaters             0       0      0       809     0      0  \n",
       "Swim                11       0      3         0   100      0  \n",
       "Trend                1       0      0         0     0     68  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = knn.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',knnscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "\n",
    "knn_predicted =knn.predict(X_train)\n",
    "pd.crosstab(Y_train, knn_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction-TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16462, 7623)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(max_df=0.5, # drop words that occur in more than half the paragraphs\n",
    "                             min_df=2, # only use words that appear at least twice\n",
    "                             stop_words='english', \n",
    "                             lowercase=True, #convert everything to lower case (since Alice in Wonderland has the HABIT of CAPITALIZING WORDS for EMPHASIS)\n",
    "                             use_idf=True,#we definitely want to use inverse document frequencies in our weighting\n",
    "                             norm=u'l2', #Applies a correction factor so that longer paragraphs and shorter paragraphs get treated equally\n",
    "                             smooth_idf=True #Adds 1 to all document frequencies, as if an extra document existed that used every word once.  Prevents divide-by-zero errors\n",
    "                            )\n",
    "\n",
    "text_count_tfidf=vectorizer.fit_transform(df['Review Text'])\n",
    "columns=vectorizer.get_feature_names()\n",
    "\n",
    "#combine countvectorizer features with original dataframe for supervised learning \n",
    "df_tf_idf=pd.DataFrame(text_count_tfidf.toarray(), columns=columns)\n",
    "df_tf= pd.concat([df, df_tf_idf], axis=1)\n",
    "df_tf=df_tf.dropna()\n",
    "df_tf=pd.get_dummies(df_tf,columns=['Division Name','Department Name'])\n",
    "df_tf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Target Recommended Review or not\n",
    "Y_idf = df_tf['Class Name']\n",
    "X_idf = df_tf.drop(['Class Name','Review Text','Title'],1)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_idf, Y_idf,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7245261623197797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.53914447 0.53012535 0.54272985 0.54807302 0.54977651]\n",
      "0.54 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Dresses</th>\n",
       "      <th>Fine gauge</th>\n",
       "      <th>Jackets</th>\n",
       "      <th>Jeans</th>\n",
       "      <th>Knits</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Pants</th>\n",
       "      <th>Skirts</th>\n",
       "      <th>Sweaters</th>\n",
       "      <th>Swim</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blouses</th>\n",
       "      <td>925</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casual bottoms</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemises</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>3391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>413</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>415</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2376</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>7</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>702</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>13</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Dresses  Fine gauge  Jackets  Jeans  Knits  Lounge  \\\n",
       "Class Name                                                                    \n",
       "Blouses             925        0           0        0      0    743       0   \n",
       "Casual bottoms        0        0           0        0      0      0       0   \n",
       "Chemises              0        0           0        0      0      0       1   \n",
       "Dresses               0     3391           0        0      0      0       0   \n",
       "Fine gauge           90        0          88        0      0    413       0   \n",
       "Intimates             0        0           0        0      0      0      82   \n",
       "Jackets               1       60           0      325      0     14       0   \n",
       "Jeans                 0        0           0        0    415      0       0   \n",
       "Knits                73        0           1        0      0   2376       0   \n",
       "Layering              0        0           0        0      0      0      40   \n",
       "Legwear               0        0           0        0      0      0      72   \n",
       "Lounge                0       34           0        0      0      2     335   \n",
       "Outerwear             7       38           0      106      0     13       0   \n",
       "Pants                 0        0           0        0      6      0       0   \n",
       "Shorts                0        0           0        0     39      0       0   \n",
       "Skirts                0        0           0        0     35      0       0   \n",
       "Sleep                 0        0           0        0      0      0     114   \n",
       "Sweaters             89        0           0        0      0    538       0   \n",
       "Swim                  0        0           0        0      0      0     159   \n",
       "Trend                13       30           2        0      3     17       0   \n",
       "\n",
       "col_0           Pants  Skirts  Sweaters  Swim  \n",
       "Class Name                                     \n",
       "Blouses             0       0         1     0  \n",
       "Casual bottoms      1       0         0     0  \n",
       "Chemises            0       0         0     0  \n",
       "Dresses             0       0         0     0  \n",
       "Fine gauge          0       0         2     0  \n",
       "Intimates           0       0         0     0  \n",
       "Jackets             0       0         0     0  \n",
       "Jeans             186       1         0     0  \n",
       "Knits               0       0         3     0  \n",
       "Layering            0       0         0     0  \n",
       "Legwear             0       0         0     0  \n",
       "Lounge              0       0         0     0  \n",
       "Outerwear           0       0         1     0  \n",
       "Pants             702       1         0     0  \n",
       "Shorts            112       3         0     0  \n",
       "Skirts            248     198         0     0  \n",
       "Sleep               0       0         0     0  \n",
       "Sweaters            0       0       186     0  \n",
       "Swim                0       0         0     4  \n",
       "Trend               3       1         3     0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "bnb = BernoulliNB()\n",
    "bnb = bnb.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy',bnb.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "bnbscore = cross_val_score(bnb,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',bnbscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (bnbscore.mean(),bnbscore.std() * 2))\n",
    "\n",
    "bnb_predicted = bnb.predict(X_train)\n",
    "pd.crosstab(Y_train, bnb_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n= 2\n",
      "Accuracy: 0.977563583346833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.9511703  0.948241   0.94572701 0.95010142 0.94270622]\n",
      "0.95 (+/- 0.01)\n",
      "n= 3\n",
      "Accuracy: 0.9741616717965332\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.95601291 0.948241   0.94775213 0.95212982 0.94555059]\n",
      "0.95 (+/- 0.01)\n",
      "n= 4\n",
      "Accuracy: 0.9676818402721529\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.9527845  0.94904974 0.94856217 0.94523327 0.94514425]\n",
      "0.95 (+/- 0.01)\n",
      "n= 5\n",
      "Accuracy: 0.9679248339543172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.9527845  0.9494541  0.95018226 0.95131846 0.94798862]\n",
      "0.95 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "n=list(range(2,6))\n",
    "for i in n:\n",
    "    print('n=',i)\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn = knn.fit(X_train, Y_train)\n",
    "\n",
    "    print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "    #Cross Validation \n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "    print('Cross Validation Scores',knnscore)\n",
    "    print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.977563583346833\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.9511703  0.948241   0.94572701 0.95010142 0.94270622]\n",
      "0.95 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Casual bottoms</th>\n",
       "      <th>Chemises</th>\n",
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       "      <td>0</td>\n",
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       "      <th>Casual bottoms</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>Fine gauge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2449</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>809</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Casual bottoms  Chemises  Dresses  Fine gauge  \\\n",
       "Class Name                                                               \n",
       "Blouses            1669               0         0        0           0   \n",
       "Casual bottoms        0               1         0        0           0   \n",
       "Chemises              0               0         1        0           0   \n",
       "Dresses               0               0         1     3390           0   \n",
       "Fine gauge            0               0         0        0         593   \n",
       "Intimates             0               0         0        1           0   \n",
       "Jackets               0               0         0        0           0   \n",
       "Jeans                 0               0         0        2           0   \n",
       "Knits                 2               0         0        0           1   \n",
       "Layering              0               0         0        0           0   \n",
       "Legwear               0               0         0        0           0   \n",
       "Lounge                3               0         0        0           0   \n",
       "Outerwear             0               0         0        2           0   \n",
       "Pants                 0               0         0       12           0   \n",
       "Shorts                0               0         0        0           0   \n",
       "Skirts                0               0         0        0           0   \n",
       "Sleep                 1               0         0        0           0   \n",
       "Sweaters              0               0         0        0           3   \n",
       "Swim                  1               0         1        0           0   \n",
       "Trend                 0               0         0        2           0   \n",
       "\n",
       "col_0           Intimates  Jackets  Jeans  Knits  Layering  Legwear  Lounge  \\\n",
       "Class Name                                                                    \n",
       "Blouses                 0        0      0      0         0        0       0   \n",
       "Casual bottoms          0        0      0      0         0        0       0   \n",
       "Chemises                0        0      0      0         0        0       0   \n",
       "Dresses                 0        0      0      0         0        0       0   \n",
       "Fine gauge              0        0      0      0         0        0       0   \n",
       "Intimates              81        0      0      0         0        0       0   \n",
       "Jackets                 0      400      0      0         0        0       0   \n",
       "Jeans                   0        0    600      0         0        0       0   \n",
       "Knits                   1        0      0   2449         0        0       0   \n",
       "Layering                5        0      0      0        35        0       0   \n",
       "Legwear                 3        0      0      0         3       66       0   \n",
       "Lounge                 20        0      0      0         2        9     337   \n",
       "Outerwear               0        2      0      1         0        0       0   \n",
       "Pants                   0        0      2      0         0        1      10   \n",
       "Shorts                  5        0      0      0         3        1      16   \n",
       "Skirts                  0        4     13      0         0        0       0   \n",
       "Sleep                   2        0      0      0         6       10      33   \n",
       "Sweaters                0        0      0      1         0        0       0   \n",
       "Swim                   12        0      1      1         1        5      25   \n",
       "Trend                   0        0      2      0         2        0       0   \n",
       "\n",
       "col_0           Outerwear  Pants  Shorts  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                                                      \n",
       "Blouses                 0      0       0       0      0         0     0      0  \n",
       "Casual bottoms          0      0       0       0      0         0     0      0  \n",
       "Chemises                0      0       0       0      0         0     0      0  \n",
       "Dresses                 0      0       0       0      0         0     0      0  \n",
       "Fine gauge              0      0       0       0      0         0     0      0  \n",
       "Intimates               0      0       0       0      0         0     0      0  \n",
       "Jackets                 0      0       0       0      0         0     0      0  \n",
       "Jeans                   0      0       0       0      0         0     0      0  \n",
       "Knits                   0      0       0       0      0         0     0      0  \n",
       "Layering                0      0       0       0      0         0     0      0  \n",
       "Legwear                 0      0       0       0      0         0     0      0  \n",
       "Lounge                  0      0       0       0      0         0     0      0  \n",
       "Outerwear             160      0       0       0      0         0     0      0  \n",
       "Pants                   0    684       0       0      0         0     0      0  \n",
       "Shorts                  0      5     124       0      0         0     0      0  \n",
       "Skirts                  0      0       0     464      0         0     0      0  \n",
       "Sleep                   0      0       9       0     53         0     0      0  \n",
       "Sweaters                0      0       0       0      0       809     0      0  \n",
       "Swim                    0      3      14       0      9         0    90      0  \n",
       "Trend                   1      0       1       0      0         0     1     63  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "knn = knn.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',knnscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "\n",
    "knn_predicted =knn.predict(X_train)\n",
    "knn_matrix=pd.crosstab(Y_train, knn_predicted)\n",
    "knn_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.82163035 0.82167408 0.82948562 0.82393509 0.81674116]\n",
      "0.82 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
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       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Casual bottoms</th>\n",
       "      <th>Chemises</th>\n",
       "      <th>Dresses</th>\n",
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       "      <th>Sweaters</th>\n",
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       "      <th>Trend</th>\n",
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       "    <tr>\n",
       "      <th>Class Name</th>\n",
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       "  <tbody>\n",
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       "      <th>Blouses</th>\n",
       "      <td>1669</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casual bottoms</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemises</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>602</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2453</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>709</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>813</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Casual bottoms  Chemises  Dresses  Fine gauge  \\\n",
       "Class Name                                                               \n",
       "Blouses            1669               0         0        0           0   \n",
       "Casual bottoms        0               1         0        0           0   \n",
       "Chemises              0               0         1        0           0   \n",
       "Dresses               0               0         0     3391           0   \n",
       "Fine gauge            0               0         0        0         593   \n",
       "Intimates             0               0         0        0           0   \n",
       "Jackets               0               0         0        0           0   \n",
       "Jeans                 0               0         0        0           0   \n",
       "Knits                 0               0         0        0           0   \n",
       "Layering              0               0         0        0           0   \n",
       "Legwear               0               0         0        0           0   \n",
       "Lounge                0               0         0        0           0   \n",
       "Outerwear             0               0         0        0           0   \n",
       "Pants                 0               0         0        0           0   \n",
       "Shorts                0               0         0        0           0   \n",
       "Skirts                0               0         0        0           0   \n",
       "Sleep                 0               0         0        0           0   \n",
       "Sweaters              0               0         0        0           0   \n",
       "Swim                  0               0         0        0           0   \n",
       "Trend                 0               0         0        0           0   \n",
       "\n",
       "col_0           Intimates  Jackets  Jeans  Knits  Layering  Legwear  Lounge  \\\n",
       "Class Name                                                                    \n",
       "Blouses                 0        0      0      0         0        0       0   \n",
       "Casual bottoms          0        0      0      0         0        0       0   \n",
       "Chemises                0        0      0      0         0        0       0   \n",
       "Dresses                 0        0      0      0         0        0       0   \n",
       "Fine gauge              0        0      0      0         0        0       0   \n",
       "Intimates              82        0      0      0         0        0       0   \n",
       "Jackets                 0      400      0      0         0        0       0   \n",
       "Jeans                   0        0    602      0         0        0       0   \n",
       "Knits                   0        0      0   2453         0        0       0   \n",
       "Layering                0        0      0      0        40        0       0   \n",
       "Legwear                 0        0      0      0         0       72       0   \n",
       "Lounge                  0        0      0      0         0        0     371   \n",
       "Outerwear               0        0      0      0         0        0       0   \n",
       "Pants                   0        0      0      0         0        0       0   \n",
       "Shorts                  0        0      0      0         0        0       0   \n",
       "Skirts                  0        0      0      0         0        0       0   \n",
       "Sleep                   0        0      0      0         0        0       0   \n",
       "Sweaters                0        0      0      0         0        0       0   \n",
       "Swim                    0        0      0      0         0        0       0   \n",
       "Trend                   0        0      0      0         0        0       0   \n",
       "\n",
       "col_0           Outerwear  Pants  Shorts  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                                                      \n",
       "Blouses                 0      0       0       0      0         0     0      0  \n",
       "Casual bottoms          0      0       0       0      0         0     0      0  \n",
       "Chemises                0      0       0       0      0         0     0      0  \n",
       "Dresses                 0      0       0       0      0         0     0      0  \n",
       "Fine gauge              0      0       0       0      0         0     0      0  \n",
       "Intimates               0      0       0       0      0         0     0      0  \n",
       "Jackets                 0      0       0       0      0         0     0      0  \n",
       "Jeans                   0      0       0       0      0         0     0      0  \n",
       "Knits                   0      0       0       0      0         0     0      0  \n",
       "Layering                0      0       0       0      0         0     0      0  \n",
       "Legwear                 0      0       0       0      0         0     0      0  \n",
       "Lounge                  0      0       0       0      0         0     0      0  \n",
       "Outerwear             165      0       0       0      0         0     0      0  \n",
       "Pants                   0    709       0       0      0         0     0      0  \n",
       "Shorts                  0      0     154       0      0         0     0      0  \n",
       "Skirts                  0      0       0     481      0         0     0      0  \n",
       "Sleep                   0      0       0       0    114         0     0      0  \n",
       "Sweaters                0      0       0       0      0       813     0      0  \n",
       "Swim                    0      0       0       0      0         0   163      0  \n",
       "Trend                   0      0       0       0      0         0     0     72  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "rfc = ensemble.RandomForestClassifier(n_estimators=100)\n",
    "rfc = rfc.fit(X_train, Y_train)\n",
    "\n",
    "print('Accuracy:',rfc.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "rfcscore = cross_val_score(rfc,X_train, Y_train, cv=5)\n",
    "\n",
    "print('Cross Validation Scores',rfcscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (rfcscore.mean(), rfcscore.std() * 2))\n",
    "\n",
    "rfc_predicted = rfc.predict(X_train)\n",
    "pd.crosstab(Y_train, rfc_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes performed the worst with the poorest accuracy and cross validation. KNN had the best combination between accuracy and cross validation while Random forest was the most accurate, but had lower cross validation scores. Using 250 of the most common words was sufficient while TF-IDF had over 7000 features. There was only improvement going to a tf-idf model feature selection with Naive Bias while KNN stayed the same and Random Forest got worse. I would choose KNN with countvectorizer in this case due to the combined combination of both metrics and a much quicker run time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Test Set Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KNN Bag of Words model will be used with the test set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16462, 267)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(lowercase=True,stop_words='english',ngram_range = (1,3),max_features=250)\n",
    "text_count= cv.fit_transform(df['Review Text'])\n",
    "columns=cv.get_feature_names()\n",
    "\n",
    "#combine countvectorizer features with original dataframe for supervised learning \n",
    "df_n=pd.DataFrame(text_count.toarray(), columns=columns)\n",
    "df_feat= pd.concat([df, df_n], axis=1)\n",
    "df_feat=df_feat.dropna()\n",
    "df_feat=pd.get_dummies(df_feat,columns=['Department Name','Division Name'])\n",
    "df_feat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Target Recommended Review or not\n",
    "Y = df_feat['Class Name']\n",
    "X = df_feat.drop(['Class Name','Review Text','Title'],1)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n= 2\n",
      "Accuracy: 0.968172983479106\n",
      "Cross Validation Scores [0.92436975 0.91404358 0.90997567 0.91575092 0.9375    ]\n",
      "0.92 (+/- 0.02)\n",
      "n= 3\n",
      "Accuracy: 0.9637998056365403\n",
      "Cross Validation Scores [0.92917167 0.92493947 0.92335766 0.93040293 0.94730392]\n",
      "0.93 (+/- 0.02)\n",
      "n= 4\n",
      "Accuracy: 0.9555393586005831\n",
      "Cross Validation Scores [0.92917167 0.9188862  0.92214112 0.93528694 0.94117647]\n",
      "0.93 (+/- 0.02)\n",
      "n= 5\n",
      "Accuracy: 0.956268221574344\n",
      "Cross Validation Scores [0.92917167 0.92736077 0.92214112 0.94017094 0.94362745]\n",
      "0.93 (+/- 0.02)\n",
      "n= 6\n",
      "Accuracy: 0.9492225461613216\n",
      "Cross Validation Scores [0.92917167 0.92615012 0.9136253  0.93162393 0.94117647]\n",
      "0.93 (+/- 0.02)\n",
      "n= 7\n",
      "Accuracy: 0.9499514091350826\n",
      "Cross Validation Scores [0.93397359 0.92978208 0.91970803 0.93528694 0.93872549]\n",
      "0.93 (+/- 0.01)\n",
      "n= 8\n",
      "Accuracy: 0.9458211856171039\n",
      "Cross Validation Scores [0.92917167 0.91767554 0.91849148 0.92551893 0.93504902]\n",
      "0.93 (+/- 0.01)\n",
      "n= 9\n",
      "Accuracy: 0.9475218658892128\n",
      "Cross Validation Scores [0.92917167 0.92615012 0.91970803 0.93040293 0.93995098]\n",
      "0.93 (+/- 0.01)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "n=list(range(2,10))\n",
    "for i in n:\n",
    "    print('n=',i)\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn = knn.fit(X_test, Y_test)\n",
    "\n",
    "    print('Accuracy:',knn.score(X_test, Y_test))\n",
    "\n",
    "    #Cross Validation \n",
    "    from sklearn.model_selection import cross_val_score\n",
    "    knnscore = cross_val_score(knn,X_test, Y_test, cv=5)\n",
    "    print('Cross Validation Scores',knnscore)\n",
    "    print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9637998056365403\n",
      "Cross Validation Scores [0.92917167 0.92493947 0.92335766 0.93040293 0.94730392]\n",
      "0.93 (+/- 0.02)\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Dresses</th>\n",
       "      <th>Fine gauge</th>\n",
       "      <th>Intimates</th>\n",
       "      <th>Jackets</th>\n",
       "      <th>Jeans</th>\n",
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       "      <th>Legwear</th>\n",
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       "      <th>Outerwear</th>\n",
       "      <th>Pants</th>\n",
       "      <th>Shorts</th>\n",
       "      <th>Skirts</th>\n",
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       "      <th>Trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class Name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blouses</th>\n",
       "      <td>565</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>1121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>779</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>98</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
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       "      <td>2</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>60</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       Blouses  Dresses  Fine gauge  Intimates  Jackets  Jeans  Knits  \\\n",
       "Class Name                                                                   \n",
       "Blouses         565        0           0          0        0      0      1   \n",
       "Dresses           0     1121           0          0        0      0      0   \n",
       "Fine gauge        0        0         182          0        0      0      0   \n",
       "Intimates         0        1           0         17        0      0      0   \n",
       "Jackets           0        0           0          0      131      0      0   \n",
       "Jeans             0        0           0          0        0    205      0   \n",
       "Knits             1        0           0          0        0      1    779   \n",
       "Layering          0        0           0          4        0      0      0   \n",
       "Legwear           0        0           0          1        0      0      0   \n",
       "Lounge            1        0           0          4        0      0      0   \n",
       "Outerwear         0        1           0          0        0      0      0   \n",
       "Pants             0        5           0          0        0      0      0   \n",
       "Shorts            0        0           0          6        0      0      0   \n",
       "Skirts            0        0           0          0        2      5      0   \n",
       "Sleep             0        0           0          3        0      0      0   \n",
       "Sweaters          0        0           2          0        0      0      0   \n",
       "Swim              0        3           0          3        0      1      0   \n",
       "Trend             0        1           0          0        0      0      0   \n",
       "\n",
       "col_0       Layering  Legwear  Lounge  Outerwear  Pants  Shorts  Skirts  \\\n",
       "Class Name                                                                \n",
       "Blouses            0        0       0          0      0       0       0   \n",
       "Dresses            0        0       0          0      1       0       0   \n",
       "Fine gauge         0        0       0          0      0       0       0   \n",
       "Intimates          0        2       5          0      0       1       0   \n",
       "Jackets            0        0       0          0      0       0       0   \n",
       "Jeans              0        0       0          0      0       0       2   \n",
       "Knits              0        0       0          0      0       0       0   \n",
       "Layering          14        0       2          0      0       2       0   \n",
       "Legwear            0       22       0          0      0       0       0   \n",
       "Lounge             1        5      98          0      0      10       0   \n",
       "Outerwear          0        0       0         60      0       0       0   \n",
       "Pants              0        0       4          0    238       0       0   \n",
       "Shorts             3        1       6          0      1      38       0   \n",
       "Skirts             0        0       0          0      0       0     144   \n",
       "Sleep              5        6       8          0      0       1       0   \n",
       "Sweaters           0        0       0          0      0       0       0   \n",
       "Swim               3        1       9          0      0       3       0   \n",
       "Trend              1        0       1          1      0       1       0   \n",
       "\n",
       "col_0       Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                \n",
       "Blouses         0         0     0      0  \n",
       "Dresses         0         0     0      1  \n",
       "Fine gauge      0         0     0      0  \n",
       "Intimates       0         0     0      0  \n",
       "Jackets         0         1     0      0  \n",
       "Jeans           0         0     0      1  \n",
       "Knits           0         0     0      0  \n",
       "Layering        0         0     0      0  \n",
       "Legwear         1         0     0      0  \n",
       "Lounge          2         0     8      0  \n",
       "Outerwear       0         0     0      0  \n",
       "Pants           0         0     1      0  \n",
       "Shorts          0         0     1      0  \n",
       "Skirts          0         0     0      0  \n",
       "Sleep          21         0     0      0  \n",
       "Sweaters        0       270     0      0  \n",
       "Swim            1         0    46      0  \n",
       "Trend           0         0     0     16  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = knn.fit(X_test, Y_test)\n",
    "\n",
    "print('Accuracy:',knn.score(X_test, Y_test))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "knnscore = cross_val_score(knn,X_test, Y_test, cv=5)\n",
    "print('Cross Validation Scores',knnscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "\n",
    "knn_predicted =knn.predict(X_test)\n",
    "pd.crosstab(Y_test, knn_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cross validation and accuracy decreased across all the combinations for n in KNN due to the smaller data set size. The model still performs consistently at a very high level. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering \n",
    "\n",
    "For clustering, I will use K-means, Mean-Shift, and Agglomerative Clustering looking at Adjusted Rand Score, Homogenity, and Completenesss scores. Additionally, look at comparision to Class Name categories and see how accurate it is.\n",
    "\n",
    "K-means Clustering works by assigning a number of clusters and matching each data point with the nearest centroid. The mean average is taken of all the points and the process is repeated until variance between the new and old clusters fall below a specified threshold value. It is a commonly run clustering technique. \n",
    "\n",
    "Mean-Shift Clustering is also an iterative process, but has no input on the number of clusters. It calculates the probability a data point falls within in n-dimesional space based on the features of the input file. The data points try to move up towards peaks in small steps. The peaks are determined by the bandwidth ranging from 0 to 1. A higher bandwidth means a smoother surface with defined peaks, while a lower bandwidth has more smaller peaks across a rougher surface. Once a data point reaches the top of the peak, it stops. After all the data points are settled, the clusters means are formed.\n",
    "\n",
    "Agglomerative Clustering initially considers each data point a cluster merging similiar data points to form a set of cluster means. The user can predetermine the number of clusters to be formed. Once a set of clusters is formed,it cannot be undone always giving the same result every time unless the parameters are changed. \n",
    "\n",
    "In order to preserve the information in the dataset,K-means will not be preprocesed and have PCA performed given that the dataset is small to medium sized. There will be no limit on samples in Mean Shift to run all the possibiliites. For k-means and agglomerative clustering, the number of clusters will be equal to the unique values in Class Name feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjusted Rand Score\n",
    "The Rand Index compares how pairs of datapoints relate in the ground truth and in the post-clustering   assignment.There are four possible types of pair relationships:  \n",
    "a=Members of the same cluster in the ground truth match same cluster in the new solution.  \n",
    "b=Members of the same cluster in the ground truth match different clusters in the new solution. c=Members of   different clusters in the ground truth match the same cluster in the new solution. d=Members of different clusters in the ground truth match different clusters in the new solution.     \n",
    "E(RI)- expected RI  \n",
    "RI=(a+c)/sum(a,b,c,d)  \n",
    "ARI=(RI-E(RI)/(max(RI)-E(RI))  \n",
    "\n",
    "# Homogenity Score\n",
    "Each cluster contains only members of a single class.\n",
    "\n",
    "# Completeness Score\n",
    "All members of a given class are assigned to the same cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Score: 0.5121667293273958\n",
      "Homogenity Score: 0.6619249400149201\n",
      "Completeness Score: 0.7737812940452677\n",
      "K-means Accuracy for Class Name: 4.992452016389907\n",
      "Comparing k-means clusters against the data:\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Class Name</th>\n",
       "      <th>Blouses</th>\n",
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       "      <th>Clusters</th>\n",
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       "      <td>44</td>\n",
       "      <td>239</td>\n",
       "      <td>5</td>\n",
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       "      <td>71</td>\n",
       "      <td>22</td>\n",
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       "      <td>68</td>\n",
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       "      <td>8</td>\n",
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       "      <td>144</td>\n",
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       "      <td>6</td>\n",
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       "      <td>22</td>\n",
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       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>78</td>\n",
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       "      <td>4</td>\n",
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       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>129</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
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       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
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       "      <th>4</th>\n",
       "      <td>10</td>\n",
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       "      <td>4</td>\n",
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       "      <td>40</td>\n",
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       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>77</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
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       "      <th>6</th>\n",
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       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
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       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>213</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>162</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>43</td>\n",
       "      <td>138</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>31</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>41</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class Name  Blouses  Casual bottoms  Dresses  Fine gauge  Intimates  Jackets  \\\n",
       "Clusters                                                                       \n",
       "0               137               0      321          60          9       36   \n",
       "1               144               0      342          38          6       33   \n",
       "2                24               0       43           4          1        3   \n",
       "3                95               0      170          41          4       24   \n",
       "4                10               0       34           9          0        5   \n",
       "5               159               0      360          42          5       42   \n",
       "6                 9               0       18           0          0        0   \n",
       "7                78               0      148          23          2       14   \n",
       "8                16               0       17           2          1        3   \n",
       "9                70               0      126          21          5       18   \n",
       "10               14               0       32           2          0        4   \n",
       "11               62               0      123          20          5        9   \n",
       "12               82               0      177          34          3       26   \n",
       "13               11               0       43           4          0        0   \n",
       "14              111               1      213          46          3       35   \n",
       "15                8               0       26           2          0        5   \n",
       "16                6               0       14           7          0        1   \n",
       "17               15               0       40           2          2        5   \n",
       "18               75               0      182          29          1       21   \n",
       "19               97               0      172          39          7       25   \n",
       "\n",
       "Class Name  Jeans  Knits  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  \\\n",
       "Clusters                                                                        \n",
       "0              44    239         5        4      32         13     71      22   \n",
       "1              56    225         9        5      22          6     65       6   \n",
       "2               6     26         1        0       5          1      4       0   \n",
       "3              27    129         4        8      13          8     33       4   \n",
       "4               5     22         0        1       6          0      2       2   \n",
       "5              69    263         4        5      40         16     70      16   \n",
       "6               1     11         1        1       0          2      5       1   \n",
       "7              25    104         3        3      19          5     24       9   \n",
       "8               1     17         1        1       3          0      5       0   \n",
       "9              20    103         3        2      17         11     31       3   \n",
       "10              1     31         0        0       3          1      5       2   \n",
       "11             18     87         2        2      11          4     24       9   \n",
       "12             35    147         3        1      22          7     40       7   \n",
       "13              5     20         1        1       5          1      6       1   \n",
       "14             35    162         3        7      27          9     36       8   \n",
       "15              8     18         0        1       3          1      5       2   \n",
       "16              3     12         0        0       1          1      3       1   \n",
       "17              7     22         0        0       4          1     13       0   \n",
       "18             43    138         4        1      13          9     50       8   \n",
       "19             31    161         2        6      21         11     30       7   \n",
       "\n",
       "Class Name  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Clusters                                          \n",
       "0               50     11        68    14      8  \n",
       "1               37     11        78    13      4  \n",
       "2                3      3         8     3      0  \n",
       "3               27      7        39     7      8  \n",
       "4                0      1         7     1      0  \n",
       "5               50      9        77    11      6  \n",
       "6                1      1         3     0      3  \n",
       "7               11      5        26     5      5  \n",
       "8                4      1         5     3      0  \n",
       "9               14      4        38     9      0  \n",
       "10               5      1         9     0      1  \n",
       "11              15      6        24     5      5  \n",
       "12              23      4        36     7     10  \n",
       "13               5      0         4     1      1  \n",
       "14              34      9        52     5      5  \n",
       "15               5      0         8     2      0  \n",
       "16               7      0         6     0      0  \n",
       "17               8      1         4     2      0  \n",
       "18              24      6        41     5      2  \n",
       "19              31      5        41    17      4  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "df=pd.get_dummies(df,columns=['Division Name','Department Name'])\n",
    "Y = df['Class Name']\n",
    "X = df.drop(['Class Name','Review Text','Title'],1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.25,random_state=0)\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km=KMeans(n_clusters=len(df['Class Name'].unique()), random_state=42)\n",
    "\n",
    "# Calculate predicted values.\n",
    "y_pred =km.fit_predict(X_train)\n",
    "y_pred=pd.Series(y_pred)\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import pairwise_distances   \n",
    "print('Adjusted Rand Score:',metrics.adjusted_rand_score(y_pred,Y_train))\n",
    "print('Homogenity Score:',metrics.homogeneity_score(y_pred,Y_train))\n",
    "print('Completeness Score:',metrics.completeness_score(y_pred,Y_train))\n",
    "\n",
    "kmean=pd.crosstab(y_pred,Y_train,rownames=['Clusters'])\n",
    "accuracy=(sum(np.diagonal(kmean))/(kmean.sum().sum()))*100\n",
    "print('K-means Accuracy for Class Name:',accuracy)\n",
    "print('Comparing k-means clusters against the data:')\n",
    "kmean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean Shift Clustering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Score: 0.37780390657480134\n",
      "Homogenity Score: 0.8424116641029098\n",
      "Completeness Score: 0.3846871324611394\n",
      "Mean Shift Accuracy for Class Name: 6.663791244339013\n",
      "Comparing Mean Shift clusters against the data:\n"
     ]
    },
    {
     "data": {
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       "      <th>Class Name</th>\n",
       "      <th>Blouses</th>\n",
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       "      <th>Fine gauge</th>\n",
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       "      <th>Clusters</th>\n",
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       "      <th></th>\n",
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       "      <td>43</td>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>276</td>\n",
       "      <td>57</td>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>8</td>\n",
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       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <th>5</th>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "Class Name  Blouses  Casual bottoms  Dresses  Fine gauge  Intimates  Jackets  \\\n",
       "Clusters                                                                       \n",
       "0               553               1     1188         213         23      145   \n",
       "1               569               0     1180         186         27      143   \n",
       "2                26               0       47           4          1        3   \n",
       "3                30               0       63           9          1        6   \n",
       "4                24               0       60           2          2        5   \n",
       "5                21               0       63          11          0        7   \n",
       "\n",
       "Class Name  Jeans  Knits  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  \\\n",
       "Clusters                                                                        \n",
       "0             208    893        20       19     127         48    250      64   \n",
       "1             204    886        22       26     113         52    230      38   \n",
       "2               7     31         1        0       6          1      5       0   \n",
       "3               7     36         2        3       9          1     10       3   \n",
       "4               8     34         1        1       4          3     18       1   \n",
       "5               6     57         0        0       8          2      9       2   \n",
       "\n",
       "Class Name  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Clusters                                          \n",
       "0              162     40       255    43     35  \n",
       "1              162     37       276    57     22  \n",
       "2                4      3        10     3      1  \n",
       "3                8      2        10     5      0  \n",
       "4                9      2         7     2      3  \n",
       "5                9      1        16     0      1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "df=pd.get_dummies(df,columns=['Division Name','Department Name'])\n",
    "Y = df['Class Name']\n",
    "X = df.drop(['Class Name','Review Text','Title'],1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.25,random_state=0)\n",
    "\n",
    "\n",
    "# Here we set the bandwidth. This function automatically derives a bandwidth\n",
    "# number based on an inspection of the distances among points in the data.\n",
    "bandwidth = estimate_bandwidth(X_train, quantile=0.2)\n",
    "\n",
    "# Declare and fit the model.\n",
    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "\n",
    "y_pred =ms.fit_predict(X_train)\n",
    "\n",
    "y_pred=pd.Series(y_pred)\n",
    "\n",
    "from sklearn import metrics   \n",
    "print('Adjusted Rand Score:',metrics.adjusted_rand_score(y_pred,Y_train))\n",
    "print('Homogenity Score:',metrics.homogeneity_score(y_pred,Y_train))\n",
    "print('Completeness Score:',metrics.completeness_score(y_pred,Y_train))\n",
    "\n",
    "mshift=pd.crosstab(y_pred,Y_train,rownames=['Clusters'])\n",
    "accuracy=(sum(np.diagonal(mshift))/(mshift.sum().sum()))*100\n",
    "print('Mean Shift Accuracy for Class Name:',accuracy)\n",
    "print('Comparing Mean Shift clusters against the data:')\n",
    "mshift"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Score: 0.6279357448068155\n",
      "Homogenity Score: 0.726307592946369\n",
      "Completeness Score: 0.8129697118945428\n",
      "Agglomerative Clustering Accuracy for Class Name: 5.876644382143628\n",
      "Comparing Agglomerative clusters against the data:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Class Name</th>\n",
       "      <th>Blouses</th>\n",
       "      <th>Casual bottoms</th>\n",
       "      <th>Dresses</th>\n",
       "      <th>Fine gauge</th>\n",
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       "      <th>Jeans</th>\n",
       "      <th>Knits</th>\n",
       "      <th>Layering</th>\n",
       "      <th>Legwear</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Outerwear</th>\n",
       "      <th>Pants</th>\n",
       "      <th>Shorts</th>\n",
       "      <th>Skirts</th>\n",
       "      <th>Sleep</th>\n",
       "      <th>Sweaters</th>\n",
       "      <th>Swim</th>\n",
       "      <th>Trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>454</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>67</td>\n",
       "      <td>337</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>21</td>\n",
       "      <td>113</td>\n",
       "      <td>30</td>\n",
       "      <td>67</td>\n",
       "      <td>16</td>\n",
       "      <td>97</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>341</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>33</td>\n",
       "      <td>56</td>\n",
       "      <td>226</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>65</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>78</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>309</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>51</td>\n",
       "      <td>227</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>15</td>\n",
       "      <td>46</td>\n",
       "      <td>13</td>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>115</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>317</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>59</td>\n",
       "      <td>239</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "      <td>16</td>\n",
       "      <td>46</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>43</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>195</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>130</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class Name  Blouses  Casual bottoms  Dresses  Fine gauge  Intimates  Jackets  \\\n",
       "Clusters                                                                       \n",
       "0               199               1      454          74          7       52   \n",
       "1               145               0      341          37          6       33   \n",
       "2                86               0      164          43          4       24   \n",
       "3               153               0      309          61          7       40   \n",
       "4                92               0      165          27          2       15   \n",
       "5               142               0      317          39          5       39   \n",
       "6                11               0       43           4          0        1   \n",
       "7                14               0       31           1          1        4   \n",
       "8                11               0       36           9          0        6   \n",
       "9                43               0      102          21          4       15   \n",
       "10               20               0       33           3          1        1   \n",
       "11                9               0       27           4          0        5   \n",
       "12                6               0       16           7          0        1   \n",
       "13               16               0       18           2          1        3   \n",
       "14               59               0      105          15          5       16   \n",
       "15               13               0       30           2          0        3   \n",
       "16                8               0       12           0          0        0   \n",
       "17              118               0      222          43          7       29   \n",
       "18               72               0      152          31          3       20   \n",
       "19                6               0       24           2          1        2   \n",
       "\n",
       "Class Name  Jeans  Knits  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  \\\n",
       "Clusters                                                                        \n",
       "0              67    337         9        4      42         21    113      30   \n",
       "1              56    226         9        5      22          6     65       7   \n",
       "2              27    129         3        9      11          7     31       4   \n",
       "3              51    227         4        9      35         12     52      15   \n",
       "4              28    115         4        3      21          6     29       9   \n",
       "5              59    239         4        4      37         13     64      16   \n",
       "6               7     21         1        1       5          1      6       1   \n",
       "7               6     21         0        0       4          2     14       1   \n",
       "8               5     26         0        1       6          0      2       2   \n",
       "9              30     80         2        1       7          3     18       3   \n",
       "10              3     21         1        0       5          1      3       0   \n",
       "11              7     18         0        1       3          1      5       2   \n",
       "12              3     11         0        0       1          1      3       1   \n",
       "13              1     14         1        1       3          0      5       0   \n",
       "14             17     80         3        1      15         10     26       3   \n",
       "15              1     31         0        0       3          1      5       2   \n",
       "16              1      8         1        1       0          0      2       0   \n",
       "17             41    195         2        7      25         15     38       6   \n",
       "18             28    130         2        1      22          6     38       6   \n",
       "19              2      8         0        0       0          1      3       0   \n",
       "\n",
       "Class Name  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Clusters                                          \n",
       "0               67     16        97    18      9  \n",
       "1               36     11        78    13      4  \n",
       "2               25      7        39     7      7  \n",
       "3               46     13        73    10      8  \n",
       "4               14      5        29     5      6  \n",
       "5               46      9        67    10      6  \n",
       "6                5      1         5     1      1  \n",
       "7                7      1         3     2      1  \n",
       "8                0      1         7     1      0  \n",
       "9               13      3        23     1      5  \n",
       "10               2      2         5     3      0  \n",
       "11               5      0         7     2      0  \n",
       "12               8      0         6     0      0  \n",
       "13               4      1         5     3      0  \n",
       "14              12      4        32     9      0  \n",
       "15               5      1         9     0      1  \n",
       "16               1      1         3     0      2  \n",
       "17              36      5        53    18      4  \n",
       "18              20      4        30     7      8  \n",
       "19               2      0         3     0      0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "df=pd.get_dummies(df,columns=['Division Name','Department Name'])\n",
    "Y = df['Class Name']\n",
    "X = df.drop(['Class Name','Review Text','Title'],1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.25,random_state=0)\n",
    "\n",
    "ac=AgglomerativeClustering(n_clusters=len(df['Class Name'].unique()))\n",
    "# Calculate predicted values.\n",
    "y_pred =ac.fit_predict(X_train)\n",
    "y_pred=pd.Series(y_pred)\n",
    "\n",
    "from sklearn import metrics   \n",
    "print('Adjusted Rand Score:',metrics.adjusted_rand_score(y_pred,Y_train))\n",
    "print('Homogenity Score:',metrics.homogeneity_score(y_pred,Y_train))\n",
    "print('Completeness Score:',metrics.completeness_score(y_pred,Y_train))\n",
    "\n",
    "acm=pd.crosstab(y_pred,Y_train,rownames=['Clusters'])\n",
    "accuracy=(sum(np.diagonal(acm))/(acm.sum().sum()))*100\n",
    "print('Agglomerative Clustering Accuracy for Class Name:',accuracy)\n",
    "print('Comparing Agglomerative clusters against the data:')\n",
    "acm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean-Shift performed the worst looking at all the metrics,also having 6 clusters rather than 20 in the ground truth.  K-means was more consistent accross all the metrics, but it still doesn't seem to be performing well. Agglomerative Clustering performed the best with the most consistent scores overall. All the crosstabs were lower than 7% in accuracy. Clustering did not impress overall with no consistent groupings. It seems to be all over the place. However, K-mean or Agglomerative should be compared over more reviews with respect to consistenty of ARI,homogenity, and completeness to pick the best method to conclude a data narrative.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering by Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Score: 0.6282479955778851\n",
      "Homogenity Score: 0.7313934746798516\n",
      "Completeness Score: 0.8399674748515787\n",
      "Agglomerative Clustering Accuracy for Class Name: 6.25\n",
      "Comparing Agglomerative clusters against the data:\n"
     ]
    },
    {
     "data": {
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       "      <th>Class Name</th>\n",
       "      <th>Blouses</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Class Name  Blouses  Dresses  Fine gauge  Intimates  Jackets  Jeans  Knits  \\\n",
       "Clusters                                                                     \n",
       "0                 2        3           1          1        2      1      4   \n",
       "1                19       34           6          3        6      8     20   \n",
       "2                 1        3           1          0        0      1      4   \n",
       "3                31       36          10          2        2      7     27   \n",
       "4                 0        1           2          0        0      0      4   \n",
       "5                 2        5           1          0        1      0      4   \n",
       "6                 7        9           0          0        1      3      9   \n",
       "7                12       18           2          0        2      2     14   \n",
       "8                 7       26           3          2        3      3     12   \n",
       "9                 5       12           0          0        1      1     10   \n",
       "10                6       13           6          1        0      5     10   \n",
       "11                0        0           0          0        0      0      3   \n",
       "12               16       43          12          2        6      2     29   \n",
       "13                3        1           0          0        1      0      0   \n",
       "14                2        3           0          0        1      0      0   \n",
       "15                1        5           0          0        1      0      1   \n",
       "16                8       15           2          1        1      3      9   \n",
       "17               16       27           3          1        3      3     24   \n",
       "18                7       10           0          2        1      5      7   \n",
       "19                0        1           0          0        1      0      0   \n",
       "\n",
       "Class Name  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  Skirts  \\\n",
       "Clusters                                                                  \n",
       "0                  0        0       0          0      0       1       1   \n",
       "1                  1        2       2          1     12       1       0   \n",
       "2                  0        0       2          1      0       0       0   \n",
       "3                  0        0       0          1      9       0       8   \n",
       "4                  0        0       0          0      2       0       0   \n",
       "5                  0        0       1          0      0       1       3   \n",
       "6                  0        0       3          0      5       3       3   \n",
       "7                  0        1       1          0      1       0       3   \n",
       "8                  1        1       0          0      4       0       1   \n",
       "9                  0        0       2          0      2       0       2   \n",
       "10                 1        0       0          2      3       1       2   \n",
       "11                 0        0       1          0      0       1       2   \n",
       "12                 1        0       3          0     14       7       7   \n",
       "13                 0        0       0          0      0       0       0   \n",
       "14                 0        0       0          0      0       0       2   \n",
       "15                 0        0       0          0      0       0       0   \n",
       "16                 0        0       1          1      2       1       9   \n",
       "17                 0        1       3          2      8       0       2   \n",
       "18                 0        0       2          0      6       1       5   \n",
       "19                 0        0       0          0      0       0       0   \n",
       "\n",
       "Class Name  Sleep  Sweaters  Swim  Trend  \n",
       "Clusters                                  \n",
       "0               0         0     0      0  \n",
       "1               2         7     2      0  \n",
       "2               0         0     0      0  \n",
       "3               1         9     3      0  \n",
       "4               0         1     0      0  \n",
       "5               0         3     1      0  \n",
       "6               0         2     1      0  \n",
       "7               1         3     1      1  \n",
       "8               1         0     1      0  \n",
       "9               2         2     0      0  \n",
       "10              0         7     1      0  \n",
       "11              0         2     0      0  \n",
       "12              0         4     0      0  \n",
       "13              0         0     0      0  \n",
       "14              0         0     0      0  \n",
       "15              1         0     0      0  \n",
       "16              1         2     2      1  \n",
       "17              1         4     0      0  \n",
       "18              1         2     2      0  \n",
       "19              0         0     1      1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "df=pd.get_dummies(df,columns=['Division Name','Department Name'])\n",
    "Y = df['Class Name']\n",
    "X = df.drop(['Class Name','Review Text','Title'],1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.25,random_state=0)\n",
    "\n",
    "ac=AgglomerativeClustering(n_clusters=len(df['Class Name'].unique()))\n",
    "# Calculate predicted values.\n",
    "y_pred =ac.fit_predict(X_test)\n",
    "y_pred=pd.Series(y_pred)\n",
    "\n",
    "from sklearn import metrics   \n",
    "print('Adjusted Rand Score:',metrics.adjusted_rand_score(y_pred,Y_test))\n",
    "print('Homogenity Score:',metrics.homogeneity_score(y_pred,Y_test))\n",
    "print('Completeness Score:',metrics.completeness_score(y_pred,Y_test))\n",
    "\n",
    "acm=pd.crosstab(y_pred,Y_test,rownames=['Clusters'])\n",
    "accuracy=(sum(np.diagonal(acm))/(acm.sum().sum()))*100\n",
    "print('Agglomerative Clustering Accuracy for Class Name:',accuracy)\n",
    "print('Comparing Agglomerative clusters against the data:')\n",
    "acm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is still room for improvement, but the consistent homogenity and completeness scores are a good sign. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Feature Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Womens Clothing E-Commerce Reviews.csv')   \n",
    "df.dropna(axis=0,inplace=True)\n",
    "df.drop(['Unnamed: 0'],1,inplace=True)\n",
    "\n",
    "# Import stopwords with nltk.\n",
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "import string \n",
    "#apply lower case, stop words, and remove punctuation\n",
    "df['Review Text']=df['Review Text'].str.lower()\n",
    "df['Review Text']=df['Review Text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
    "df['Review Text']=df['Review Text'].str.replace('[{}]'.format(string.punctuation), '')\n",
    "\n",
    "#Tokenize words by sentence\n",
    "df['tokenized_sents'] = df.apply(lambda row: nltk.word_tokenize(row['Review Text']), axis=1)\n",
    "#Sentence Length\n",
    "df['sents_length'] = df.apply(lambda row: len(row['tokenized_sents']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dresses': ['maeve', 'clothing', 'exception', 'gill', 'styles'],\n",
       " 'pants': ['pant', 'shorts', 'these', 'trousers', 'jeans'],\n",
       " 'blouse': ['top', 'lace', 'embroidery', 'crochet', 'print'],\n",
       " 'knit': ['jersey', 'cotton', 'yarn', 'texture', 'silky'],\n",
       " 'lingerie': ['spray', 'remover', 'washings', 'unwearable', 'dance'],\n",
       " 'lounge': ['yoga', 'sleep', 'ready', 'kids', 'lounging'],\n",
       " 'sweaters': ['jackets', 'vests', 'boiled', 'sweater', 'cardigans'],\n",
       " 'belts': ['sounds', 'oth', 'level', 'verdugo', 'teach'],\n",
       " 'skirt': ['dress', 'aline', 'pencil', 'natural', 'skirts'],\n",
       " 'panties': ['thong', 'non', 'children', 'inserts', 'grace'],\n",
       " 'bra': ['straps', 'cami', 'cleavage', 'camisole', 'strapless'],\n",
       " 'swim': ['bathing', 'coverup', 'comfiest', 'bandeau', 'bralette'],\n",
       " 'lace': ['crochet', 'embroidered', 'embroidery', 'trim', 'eyelet'],\n",
       " 'jeans': ['pant', 'jean', 'pants', 'skinnys', 'trousers'],\n",
       " 'shorts': ['pants', 'trousers', 'pant', 'skirts', 'legging'],\n",
       " 'yoga': ['sleep', 'gym', 'lounge', 'workout', 'tennis'],\n",
       " 'leggings': ['booties', 'skinny', 'tights', 'boots', 'skinnies']}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gensim\n",
    "from gensim.models import word2vec\n",
    "\n",
    "model = word2vec.Word2Vec(\n",
    "    df['tokenized_sents'] ,\n",
    "    workers=10,     # Number of threads to run in parallel (if your computer does parallel processing).\n",
    "    min_count=3,   # Minimum word count threshold.\n",
    "    window=10,     # Number of words around target word to consider.\n",
    "    sg=0,          # Use CBOW(sg=0) because our corpus is small.\n",
    "    sample=1e-3 ,  # Penalize frequent words.\n",
    "    size=300,      # Word vector length.\n",
    "    hs=1           # Use hierarchical softmax hs=1 for training \n",
    ")\n",
    "\n",
    "vectors = model.wv\n",
    "df_gs=pd.DataFrame(np.array(vectors[words]))\n",
    "\n",
    "vocab = model.wv.vocab.keys()\n",
    "# view similar words based on gensim's model\n",
    "similar_words = {search_term: [item[0] for item in vectors.most_similar([search_term], topn=5)]\n",
    "                 for search_term in ['dresses', 'pants', 'blouse', 'knit', 'lingerie',\n",
    "                                     'lounge', 'sweaters','belts','skirt','panties','bra',\n",
    "                                     'swim', 'lace', 'jeans', 'shorts', 'yoga','leggings']}\n",
    "similar_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After iterating creating a vocab list of about 6566 words with the gensim model, it peformed with mixed results. I originally chose the class name headers, but they all didn't appear in the vocab list. From there, I incorporated words that were relevant to the data. I kept the min count at 3 to try to capture a majority of the data. Knit, shorts, and sweaters have the best associations while lingerie , panties, and belts did the worst. The latter is likely due to the lower count of inventory totals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Class Name</th>\n",
       "      <th>tokenized_sents</th>\n",
       "      <th>sents_length</th>\n",
       "      <th>...</th>\n",
       "      <th>299</th>\n",
       "      <th>Department Name_Bottoms</th>\n",
       "      <th>Department Name_Dresses</th>\n",
       "      <th>Department Name_Intimate</th>\n",
       "      <th>Department Name_Jackets</th>\n",
       "      <th>Department Name_Tops</th>\n",
       "      <th>Department Name_Trend</th>\n",
       "      <th>Division Name_General</th>\n",
       "      <th>Division Name_General Petite</th>\n",
       "      <th>Division Name_Initmates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>high hopes dress really wanted work me initial...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>[high, hopes, dress, really, wanted, work, me,...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534929</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>love love love jumpsuit fun flirty fabulous ev...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pants</td>\n",
       "      <td>[love, love, love, jumpsuit, fun, flirty, fabu...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.723299</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>847.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>shirt flattering due adjustable front tie perf...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>[shirt, flattering, due, adjustable, front, ti...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.945291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1080.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Not for the very petite</td>\n",
       "      <td>love tracy reese dresses one petite 5 feet tal...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>[love, tracy, reese, dresses, one, petite, 5, ...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.110224</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>858.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>Cagrcoal shimmer fun</td>\n",
       "      <td>aded basket hte last mintue see would look lik...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Knits</td>\n",
       "      <td>[aded, basket, hte, last, mintue, see, would, ...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.567747</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 319 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clothing ID   Age                    Title  \\\n",
       "2       1077.0  60.0  Some major design flaws   \n",
       "3       1049.0  50.0         My favorite buy!   \n",
       "4        847.0  47.0         Flattering shirt   \n",
       "5       1080.0  49.0  Not for the very petite   \n",
       "6        858.0  39.0     Cagrcoal shimmer fun   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "2  high hopes dress really wanted work me initial...     3.0              0.0   \n",
       "3  love love love jumpsuit fun flirty fabulous ev...     5.0              1.0   \n",
       "4  shirt flattering due adjustable front tie perf...     5.0              1.0   \n",
       "5  love tracy reese dresses one petite 5 feet tal...     2.0              0.0   \n",
       "6  aded basket hte last mintue see would look lik...     5.0              1.0   \n",
       "\n",
       "   Positive Feedback Count Class Name  \\\n",
       "2                      0.0    Dresses   \n",
       "3                      0.0      Pants   \n",
       "4                      6.0    Blouses   \n",
       "5                      4.0    Dresses   \n",
       "6                      1.0      Knits   \n",
       "\n",
       "                                     tokenized_sents  sents_length  \\\n",
       "2  [high, hopes, dress, really, wanted, work, me,...          51.0   \n",
       "3  [love, love, love, jumpsuit, fun, flirty, fabu...          15.0   \n",
       "4  [shirt, flattering, due, adjustable, front, ti...          16.0   \n",
       "5  [love, tracy, reese, dresses, one, petite, 5, ...          42.0   \n",
       "6  [aded, basket, hte, last, mintue, see, would, ...          51.0   \n",
       "\n",
       "            ...                  299  Department Name_Bottoms  \\\n",
       "2           ...             0.534929                        0   \n",
       "3           ...             0.723299                        1   \n",
       "4           ...             0.945291                        0   \n",
       "5           ...            -0.110224                        0   \n",
       "6           ...            -0.567747                        0   \n",
       "\n",
       "   Department Name_Dresses  Department Name_Intimate  Department Name_Jackets  \\\n",
       "2                        1                         0                        0   \n",
       "3                        0                         0                        0   \n",
       "4                        0                         0                        0   \n",
       "5                        1                         0                        0   \n",
       "6                        0                         0                        0   \n",
       "\n",
       "   Department Name_Tops  Department Name_Trend  Division Name_General  \\\n",
       "2                     0                      0                      1   \n",
       "3                     0                      0                      0   \n",
       "4                     1                      0                      1   \n",
       "5                     0                      0                      1   \n",
       "6                     1                      0                      0   \n",
       "\n",
       "   Division Name_General Petite  Division Name_Initmates  \n",
       "2                             0                        0  \n",
       "3                             1                        0  \n",
       "4                             0                        0  \n",
       "5                             0                        0  \n",
       "6                             1                        0  \n",
       "\n",
       "[5 rows x 319 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combine gensim features with original dataframe for supervised learning \n",
    "df_featgs= pd.concat([df, df_gs], axis=1)\n",
    "df_featgs=df_featgs.dropna()\n",
    "df_featgs=pd.get_dummies(df_featgs,columns=['Department Name','Division Name',])\n",
    "df_featgs.shape\n",
    "df_featgs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Target Recommended Review or not\n",
    "Y = df_featgs['Class Name']\n",
    "X = df_featgs.drop(['Class Name','Review Text','Title','tokenized_sents'],1)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,test_size=0.25,random_state=0)\n",
    "split=['Training','Testing']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "n= 2\n",
      "Accuracy: 0.9505628645056287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.89445196 0.90176152 0.90474576 0.90495587 0.90618627]\n",
      "0.90 (+/- 0.01)\n",
      "\n",
      "n= 3\n",
      "Accuracy: 0.9551742845517428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91305819 0.91327913 0.91728814 0.92057026 0.91740313]\n",
      "0.92 (+/- 0.01)\n",
      "\n",
      "n= 4\n",
      "Accuracy: 0.9460870744608707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.90866035 0.91395664 0.91559322 0.91581806 0.9228416 ]\n",
      "0.92 (+/- 0.01)\n",
      "\n",
      "n= 5\n",
      "Accuracy: 0.9476468194764682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91305819 0.91869919 0.91966102 0.92260692 0.9225017 ]\n",
      "0.92 (+/- 0.01)\n",
      "\n",
      "n= 6\n",
      "Accuracy: 0.9410009494100094\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91271989 0.9200542  0.92237288 0.91989138 0.92352141]\n",
      "0.92 (+/- 0.01)\n",
      "\n",
      "Testing\n",
      "n= 2\n",
      "Accuracy: 0.9497558991049634\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.89728097 0.88145897 0.89602446 0.8886619  0.89446721]\n",
      "0.89 (+/- 0.01)\n",
      "\n",
      "n= 3\n",
      "Accuracy: 0.9564686737184703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.90735146 0.89969605 0.91335372 0.91215526 0.8954918 ]\n",
      "0.91 (+/- 0.01)\n",
      "\n",
      "n= 4\n",
      "Accuracy: 0.9408055329536208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91238671 0.89665653 0.91131498 0.90909091 0.89241803]\n",
      "0.90 (+/- 0.02)\n",
      "\n",
      "n= 5\n",
      "Accuracy: 0.9389747762408462\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91742195 0.89969605 0.91641182 0.91011236 0.89959016]\n",
      "0.91 (+/- 0.02)\n",
      "\n",
      "n= 6\n",
      "Accuracy: 0.9320585842148088\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91440081 0.90172239 0.91539246 0.91215526 0.89754098]\n",
      "0.91 (+/- 0.01)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "n=list(range(2,7))\n",
    "for k in split:\n",
    "    print (k)\n",
    "    if k=='Training':\n",
    "            for i in n:\n",
    "                print('n=',i)\n",
    "                knn = KNeighborsClassifier(n_neighbors=i)\n",
    "                knn = knn.fit(X_train, Y_train)\n",
    "\n",
    "                print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "                #Cross Validation \n",
    "                from sklearn.model_selection import cross_val_score\n",
    "                knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "                print('Cross Validation Scores',knnscore)\n",
    "                print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "                print('')\n",
    "    else: \n",
    "            for i in n:\n",
    "                print('n=',i)\n",
    "                knn = KNeighborsClassifier(n_neighbors=i)\n",
    "                knn = knn.fit(X_test, Y_test)\n",
    "\n",
    "                print('Accuracy:',knn.score(X_test, Y_test))\n",
    "\n",
    "                #Cross Validation \n",
    "                from sklearn.model_selection import cross_val_score\n",
    "                knnscore = cross_val_score(knn,X_test, Y_test, cv=5)\n",
    "                print('Cross Validation Scores',knnscore)\n",
    "                print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "                print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Accuracy: 0.9551742845517428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.91305819 0.91327913 0.91728814 0.92057026 0.91740313]\n",
      "0.92 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
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       "      <td>11</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           Blouses  Casual bottoms  Dresses  Fine gauge  Intimates  \\\n",
       "Class Name                                                                \n",
       "Blouses            1905               0        0           0          0   \n",
       "Casual bottoms        0               1        0           0          0   \n",
       "Dresses               0               0     4048           0          0   \n",
       "Fine gauge            1               0        0         699          0   \n",
       "Intimates             2               0        3           0         52   \n",
       "Jackets               0               0        1           0          0   \n",
       "Jeans                 0               0        3           0          0   \n",
       "Knits                 8               0        1           5          2   \n",
       "Layering              0               0        1           0          9   \n",
       "Legwear               0               0        0           0          6   \n",
       "Lounge                5               0        0           0         15   \n",
       "Outerwear             0               0       39           0          0   \n",
       "Pants                 0               0       41           0          0   \n",
       "Shorts                0               0        0           0         10   \n",
       "Skirts                0               0        0           0          0   \n",
       "Sleep                 2               0        0           0         11   \n",
       "Sweaters              0               0        0           3          0   \n",
       "Swim                  0               0        1           1          7   \n",
       "Trend                 0               0        3           0          0   \n",
       "\n",
       "col_0           Jackets  Jeans  Knits  Layering  Legwear  Lounge  Outerwear  \\\n",
       "Class Name                                                                    \n",
       "Blouses               0      0     29         0        0       0          0   \n",
       "Casual bottoms        0      0      0         0        0       0          0   \n",
       "Dresses               0      1      0         0        0       0          2   \n",
       "Fine gauge            0      0      1         0        0       0          0   \n",
       "Intimates             0      0      0         1        2      11          0   \n",
       "Jackets             446      0      0         0        0       0          1   \n",
       "Jeans                 0    663      0         0        0       0          0   \n",
       "Knits                 0      0   2989         0        0       0          0   \n",
       "Layering              0      1      0        63        3       5          0   \n",
       "Legwear               0      0      0         5       72       6          0   \n",
       "Lounge                0      0      0        27       16     323          0   \n",
       "Outerwear             0      0      0         0        0       0        163   \n",
       "Pants                 0      9      0         2        2      11          0   \n",
       "Shorts                0      0      0         8        3      37          0   \n",
       "Skirts                4     42      0         0        0       0          0   \n",
       "Sleep                 0      0      0        10       18      23          0   \n",
       "Sweaters              1      0      1         1        0       0          0   \n",
       "Swim                  0      0      1        14        7      35          0   \n",
       "Trend                 0      0      0         4        0       1          2   \n",
       "\n",
       "col_0           Pants  Shorts  Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                                           \n",
       "Blouses             0       0       0      0         0     0      0  \n",
       "Casual bottoms      0       0       0      0         0     0      0  \n",
       "Dresses             4       0       0      0         0     1      4  \n",
       "Fine gauge          0       0       0      0         4     0      0  \n",
       "Intimates           0       2       0      4         0     6      0  \n",
       "Jackets             0       0       3      0         5     0      0  \n",
       "Jeans               0       0      14      0         0     0      1  \n",
       "Knits               0       0       0      0         0     0      0  \n",
       "Layering            0       0       0      1         0     5      1  \n",
       "Legwear             0       1       0      3         0     1      0  \n",
       "Lounge              0      12       0     10         0     9      1  \n",
       "Outerwear           0       0       0      0         0     0      1  \n",
       "Pants             815       4       0      0         0     1      0  \n",
       "Shorts              4     119       0      3         0     5      1  \n",
       "Skirts              0       0     561      0         0     0      0  \n",
       "Sleep               1       6       0     53         0     0      0  \n",
       "Sweaters            0       0       0      0       900     0      0  \n",
       "Swim                2      12       0      0         0   137      0  \n",
       "Trend               0       2       0      0         0     0     76  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = knn.fit(X_train, Y_train)\n",
    "print('Training')\n",
    "print('Accuracy:',knn.score(X_train, Y_train))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "knnscore = cross_val_score(knn,X_train, Y_train, cv=5)\n",
    "print('Cross Validation Scores',knnscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "\n",
    "knn_predicted =knn.predict(X_train)\n",
    "pd.crosstab(Y_train, knn_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing\n",
      "Accuracy: 0.9564686737184703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:652: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=5.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Scores [0.90735146 0.89969605 0.91335372 0.91215526 0.8954918 ]\n",
      "0.91 (+/- 0.01)\n"
     ]
    },
    {
     "data": {
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       "      <th>Dresses</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>Fine gauge</th>\n",
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       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>974</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
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       "      <td>28</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       Blouses  Chemises  Dresses  Fine gauge  Intimates  Jackets  Jeans  \\\n",
       "Class Name                                                                      \n",
       "Blouses         642         0        1           0          0        0      0   \n",
       "Chemises          0         1        0           0          0        0      0   \n",
       "Dresses           0         0     1310           0          0        0      0   \n",
       "Fine gauge        0         0        0         221          0        0      0   \n",
       "Intimates         1         0        1           0         30        0      0   \n",
       "Jackets           0         0        0           0          0      140      0   \n",
       "Jeans             0         2        2           0          0        0    283   \n",
       "Knits             0         0        1           1          0        0      0   \n",
       "Layering          0         1        3           0          1        0      0   \n",
       "Legwear           0         0        0           0          3        0      0   \n",
       "Lounge            3         0        1           0          6        0      0   \n",
       "Outerwear         0         1        7           0          0        0      0   \n",
       "Pants             0         0        9           0          0        0      1   \n",
       "Shorts            0         0        0           0          6        0      0   \n",
       "Skirts            0         0        0           0          0        4     20   \n",
       "Sleep             0         0        0           0          2        0      0   \n",
       "Sweaters          0         0        0           0          0        0      0   \n",
       "Swim              0         0        0           0          5        0      1   \n",
       "Trend             0         0        2           0          0        0      0   \n",
       "\n",
       "col_0       Knits  Layering  Legwear  Lounge  Outerwear  Pants  Shorts  \\\n",
       "Class Name                                                               \n",
       "Blouses        10         0        0       0          0      0       0   \n",
       "Chemises        0         0        0       0          0      0       0   \n",
       "Dresses         0         0        0       0          1      0       0   \n",
       "Fine gauge      0         0        0       0          0      0       0   \n",
       "Intimates       0         0        1       3          0      0       0   \n",
       "Jackets         0         0        0       0          0      0       0   \n",
       "Jeans           1         0        0       0          0      0       0   \n",
       "Knits         974         0        0       0          0      0       0   \n",
       "Layering        0        17        1       0          0      0       0   \n",
       "Legwear         0         1       28       3          0      0       0   \n",
       "Lounge          0         2        4     126          0      1       7   \n",
       "Outerwear       0         0        0       0         70      0       0   \n",
       "Pants           0         1        1       2          0    257       0   \n",
       "Shorts          0         4        0      11          0      4      42   \n",
       "Skirts          0         0        0       0          0      0       0   \n",
       "Sleep           0         4        4       5          0      1       1   \n",
       "Sweaters        0         0        0       0          0      0       0   \n",
       "Swim            2         4        4      14          0      0       6   \n",
       "Trend           0         0        0       2          1      1       0   \n",
       "\n",
       "col_0       Skirts  Sleep  Sweaters  Swim  Trend  \n",
       "Class Name                                        \n",
       "Blouses          0      0         0     0      0  \n",
       "Chemises         0      0         0     0      0  \n",
       "Dresses          0      0         0     0      0  \n",
       "Fine gauge       0      0         1     0      0  \n",
       "Intimates        0      0         0     1      0  \n",
       "Jackets          2      0         0     0      0  \n",
       "Jeans            1      0         0     0      0  \n",
       "Knits            0      0         0     0      0  \n",
       "Layering         0      3         0     0      0  \n",
       "Legwear          0      2         0     0      0  \n",
       "Lounge           0      5         0     1      0  \n",
       "Outerwear        0      0         0     0      0  \n",
       "Pants            0      0         0     1      0  \n",
       "Shorts           0      1         0     1      1  \n",
       "Skirts         165      0         0     0      0  \n",
       "Sleep            0     33         0     0      0  \n",
       "Sweaters         0      0       312     0      0  \n",
       "Swim             0      2         0    38      0  \n",
       "Trend            0      0         0     0     13  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn = knn.fit(X_test, Y_test)\n",
    "print('Testing')\n",
    "print('Accuracy:',knn.score(X_test, Y_test))\n",
    "\n",
    "#Cross Validation \n",
    "from sklearn.model_selection import cross_val_score\n",
    "knnscore = cross_val_score(knn,X_test, Y_test, cv=5)\n",
    "print('Cross Validation Scores',knnscore)\n",
    "print(\"%0.2f (+/- %0.2f)\" % (knnscore.mean(), knnscore.std() * 2))\n",
    "\n",
    "knn_predicted =knn.predict(X_test)\n",
    "pd.crosstab(Y_test, knn_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gensim model unsupervised features performs consistent with the countvectorizer results at high accuracy. The standard deviation is looks to be lowest out of the all the models. I am thoroughly impressed, but also think that Gensim provides more options for analysis in similarity and other topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Thoughts\n",
    "\n",
    "Overall, BoW, TF-IDF, and Gensim provided very consistent and accurate models for this data when indentifying the class name on the reviews as compared to the clustering. TF-IDF had the longest run time with the worst results of the methods. However, the drawback is that GenSim is the only method allowing for further analysis to determine any new target class names to add. It would be interesting to see if another dataset of reviews peformed just as well over a longer time period.\n",
    "The clustering results weren't great, but give the insight to look into to see if some of the class names are similiar see which catergories are associated to target sales marketing patterns for the inventory. It could be used to combine to simplify the inventory and promote sales of similiarly inventory. For example if jeans and shorts were associated, offer a discount on the sales of both items together. A combination of Gensim and clustering methods allows the company to back up industry intuition in similiar situations."
   ]
  }
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