Marketing_analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db2ee84e",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f753630d",
   "metadata": {},
   "source": [
    "Pandas and numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6e0170ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f1827149",
   "metadata": {},
   "source": [
    "Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1856a3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import graphviz\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4af05fc4",
   "metadata": {},
   "source": [
    "Preprocessing and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90cb519d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "991ed1a8",
   "metadata": {},
   "source": [
    "Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d498fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.neighbors import KNeighborsClassifier as knn\n",
    "from sklearn.ensemble import RandomForestClassifier as rf\n",
    "from sklearn.svm import SVC as svc\n",
    "\n",
    "from tensorflow.keras.models import Sequential #FFNN\n",
    "from tensorflow.keras.layers import * #Layers\n",
    "from tensorflow.keras.optimizers import Adam #SGD optimiser\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError #Using the RMSE to determine the residuals\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "992da711",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e274eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row ID</th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Row0</td>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
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       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row1</td>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row2</td>\n",
       "      <td>45</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row3</td>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row4</td>\n",
       "      <td>41</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26355</th>\n",
       "      <td>Row26355</td>\n",
       "      <td>29</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26356</th>\n",
       "      <td>Row26356</td>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26357</th>\n",
       "      <td>Row26357</td>\n",
       "      <td>56</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26358</th>\n",
       "      <td>Row26358</td>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26359</th>\n",
       "      <td>Row26359</td>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26360 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         row ID  age          job  marital            education  default  \\\n",
       "0          Row0   56    housemaid  married             basic.4y       no   \n",
       "1          Row1   56     services  married          high.school       no   \n",
       "2          Row2   45     services  married             basic.9y  unknown   \n",
       "3          Row3   59       admin.  married  professional.course       no   \n",
       "4          Row4   41  blue-collar  married              unknown  unknown   \n",
       "...         ...  ...          ...      ...                  ...      ...   \n",
       "26355  Row26355   29   unemployed   single             basic.4y       no   \n",
       "26356  Row26356   46  blue-collar  married  professional.course       no   \n",
       "26357  Row26357   56      retired  married    university.degree       no   \n",
       "26358  Row26358   44   technician  married  professional.course       no   \n",
       "26359  Row26359   74      retired  married  professional.course       no   \n",
       "\n",
       "      housing loan    contact month  ... campaign  pdays  previous  \\\n",
       "0          no   no  telephone   may  ...        1    999         0   \n",
       "1          no  yes  telephone   may  ...        1    999         0   \n",
       "2          no   no  telephone   may  ...        1    999         0   \n",
       "3          no   no  telephone   may  ...        1    999         0   \n",
       "4          no   no  telephone   may  ...        1    999         0   \n",
       "...       ...  ...        ...   ...  ...      ...    ...       ...   \n",
       "26355     yes   no   cellular   nov  ...        1      9         1   \n",
       "26356      no   no   cellular   nov  ...        1    999         0   \n",
       "26357     yes   no   cellular   nov  ...        2    999         0   \n",
       "26358      no   no   cellular   nov  ...        1    999         0   \n",
       "26359     yes   no   cellular   nov  ...        3    999         1   \n",
       "\n",
       "          poutcome  emp.var.rate cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "0      nonexistent           1.1         93.994          -36.4      4.857   \n",
       "1      nonexistent           1.1         93.994          -36.4      4.857   \n",
       "2      nonexistent           1.1         93.994          -36.4      4.857   \n",
       "3      nonexistent           1.1         93.994          -36.4      4.857   \n",
       "4      nonexistent           1.1         93.994          -36.4      4.857   \n",
       "...            ...           ...            ...            ...        ...   \n",
       "26355      success          -1.1         94.767          -50.8      1.028   \n",
       "26356  nonexistent          -1.1         94.767          -50.8      1.028   \n",
       "26357  nonexistent          -1.1         94.767          -50.8      1.028   \n",
       "26358  nonexistent          -1.1         94.767          -50.8      1.028   \n",
       "26359      failure          -1.1         94.767          -50.8      1.028   \n",
       "\n",
       "       nr.employed  subscribed  \n",
       "0           5191.0           0  \n",
       "1           5191.0           0  \n",
       "2           5191.0           0  \n",
       "3           5191.0           0  \n",
       "4           5191.0           0  \n",
       "...            ...         ...  \n",
       "26355       4963.6           0  \n",
       "26356       4963.6           0  \n",
       "26357       4963.6           0  \n",
       "26358       4963.6           1  \n",
       "26359       4963.6           0  \n",
       "\n",
       "[26360 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(pd.read_csv(\"C:\\Projects\\Visual\\Python_projects\\Marketing_analysis\\Marketing-Dataset.csv\"))\n",
    "features = df.columns.drop(\"row ID\").drop(\"subscribed\")\n",
    "target = df.subscribed\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81bc129d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row ID             object\n",
       "age                 int64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp.var.rate      float64\n",
       "cons.price.idx    float64\n",
       "cons.conf.idx     float64\n",
       "euribor3m         float64\n",
       "nr.employed       float64\n",
       "subscribed          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "da3d695d",
   "metadata": {},
   "source": [
    "Nominal attributes\n",
    "* Job\n",
    "* Martical\n",
    "* Default\n",
    "* Housing\n",
    "* Loan\n",
    "* Contact\n",
    "* Month\n",
    "* Day_of_the_week\n",
    "* Poutcome\n",
    "* Subscribed\n",
    "\n",
    "Ordinal attributes\n",
    "* Education\n",
    "\n",
    "Ratio attributes\n",
    "* Duration\n",
    "* Campaign\n",
    "* Pdays\n",
    "* Previous\n",
    "* Euribor3m\n",
    "* Nr.employed\n",
    "\n",
    "Interval attributes\n",
    "* Emp.var.rate\n",
    "* Cons.price.idx\n",
    "* Cons.conf.idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bdb22ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "      <td>26360.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.055994</td>\n",
       "      <td>258.799317</td>\n",
       "      <td>2.556639</td>\n",
       "      <td>961.516161</td>\n",
       "      <td>0.173065</td>\n",
       "      <td>0.074848</td>\n",
       "      <td>93.570798</td>\n",
       "      <td>-40.490303</td>\n",
       "      <td>3.618071</td>\n",
       "      <td>5167.032845</td>\n",
       "      <td>0.112443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.452764</td>\n",
       "      <td>263.562218</td>\n",
       "      <td>2.715861</td>\n",
       "      <td>189.261156</td>\n",
       "      <td>0.490529</td>\n",
       "      <td>1.576832</td>\n",
       "      <td>0.578183</td>\n",
       "      <td>4.635120</td>\n",
       "      <td>1.735283</td>\n",
       "      <td>72.089807</td>\n",
       "      <td>0.315917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.444000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age      duration      campaign         pdays      previous  \\\n",
       "count  26360.000000  26360.000000  26360.000000  26360.000000  26360.000000   \n",
       "mean      40.055994    258.799317      2.556639    961.516161      0.173065   \n",
       "std       10.452764    263.562218      2.715861    189.261156      0.490529   \n",
       "min       17.000000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.000000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.000000    179.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.000000    320.000000      3.000000    999.000000      0.000000   \n",
       "max       98.000000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m  \\\n",
       "count  26360.000000    26360.000000   26360.000000  26360.000000   \n",
       "mean       0.074848       93.570798     -40.490303      3.618071   \n",
       "std        1.576832        0.578183       4.635120      1.735283   \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   \n",
       "50%        1.100000       93.444000     -41.800000      4.857000   \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   \n",
       "max        1.400000       94.767000     -26.900000      5.045000   \n",
       "\n",
       "        nr.employed    subscribed  \n",
       "count  26360.000000  26360.000000  \n",
       "mean    5167.032845      0.112443  \n",
       "std       72.089807      0.315917  \n",
       "min     4963.600000      0.000000  \n",
       "25%     5099.100000      0.000000  \n",
       "50%     5191.000000      0.000000  \n",
       "75%     5228.100000      0.000000  \n",
       "max     5228.100000      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "98608e60",
   "metadata": {},
   "source": [
    "We can look at the count of each subscribed/not-subscribed instance and their ratios to check out the balance of the dataset.\n",
    "\n",
    "0\n",
    "*23396 instances\n",
    "*~88.7% of the set\n",
    "\n",
    "1\n",
    "*2964 instances\n",
    "*~11.2%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab770998",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:  23396 0.887556904400607\n",
      "1:  2964 0.11244309559939301\n"
     ]
    }
   ],
   "source": [
    "print(\"0: \",df[\"subscribed\"].value_counts()[0], df[\"subscribed\"].value_counts()[0]/df[\"subscribed\"].value_counts().sum())\n",
    "print(\"1: \",df[\"subscribed\"].value_counts()[1], df[\"subscribed\"].value_counts()[1]/df[\"subscribed\"].value_counts().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "753bff9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "values = df['subscribed'].value_counts().values\n",
    "labels = df['subscribed'].value_counts().index\n",
    "colors = sns.color_palette('pastel')[0:2]\n",
    "\n",
    "plt.pie(\n",
    "    df['subscribed'].value_counts().values,\n",
    "    labels = labels,\n",
    "    colors = colors,\n",
    "    explode=[0.2, 0],\n",
    "    autopct='%.0f%%',\n",
    "    textprops={'fontsize': 20}\n",
    "    )\n",
    "\n",
    "plt.legend(\n",
    "    title = \"subscribed\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9590013",
   "metadata": {},
   "source": [
    "So as we can see, the dataset is very unbalanced. \n",
    "\n",
    "Although this isn't ideal, it should be noted and recognised during while we are getting results. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5dd2cbdf",
   "metadata": {},
   "source": [
    "We are going to drop the row ID row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01c507b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"row ID\", axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fe74703a",
   "metadata": {},
   "source": [
    "Checking for missing values, we can see that job, marital, education, default, housing and loan all contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b23a1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               False\n",
       "job               False\n",
       "marital           False\n",
       "education         False\n",
       "default           False\n",
       "housing           False\n",
       "loan              False\n",
       "contact           False\n",
       "month             False\n",
       "day_of_week       False\n",
       "duration          False\n",
       "campaign          False\n",
       "pdays             False\n",
       "previous          False\n",
       "poutcome          False\n",
       "emp.var.rate      False\n",
       "cons.price.idx    False\n",
       "cons.conf.idx     False\n",
       "euribor3m         False\n",
       "nr.employed       False\n",
       "subscribed        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a673124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.set_context('paper', font_scale=1.4)\n",
    "\n",
    "sns.heatmap(\n",
    "    df.corr(), \n",
    "    annot=True, \n",
    "    cmap='Blues',\n",
    "    linewidths=.2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "90a2cb03",
   "metadata": {},
   "source": [
    "We can see interesting correlations. \n",
    "\n",
    "There is a strong positive correlation between being subscribed and duration and previous contact. \n",
    "\n",
    "There are strongly negative correlation between being subscribed and pdays (number of days passed after the client was last contacted from a previous contact), emp.var.rate, euribor3m, nr.employed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d61c9369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1de4203de20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1343.48x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(\n",
    "    df[['duration', \n",
    "    'pdays', \n",
    "    'previous', \n",
    "    'emp.var.rate', \n",
    "    'cons.price.idx', \n",
    "    'euribor3m', \n",
    "    'nr.employed', \n",
    "    'subscribed']]\n",
    "    , hue='subscribed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1968b458",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4918\n",
      "179.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    df['duration'],\n",
    "    kde=True)\n",
    "\n",
    "print(np.max(df.duration))\n",
    "print(np.median(df.duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33e1c0e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CL-11\\AppData\\Local\\Temp/ipykernel_30288/1220985388.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  len(df[df.duration > 179][df.subscribed == 1]) / len(df[df.subscribed == 1]) #Ratio of subscribed members where the duration is greater than the median\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8751686909581646"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.duration > 179][df.subscribed == 1]) / len(df[df.subscribed == 1]) #Ratio of subscribed members where the duration is greater than the median"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d2cbf18f",
   "metadata": {},
   "source": [
    "Duration has the highest positive correlation so it is worth looking into a bit deeper. \n",
    "* Max = 4918\n",
    "* Median = 179\n",
    "\n",
    "We checked the ratio of subscribed members where the duration is greater than the median. It showed that ~88% of successfully subscribed calls occured after a call longer than the duration of the median duration. \n",
    "\n",
    "A good hypothesis to investigate would be 'as duration of the calls increase, so does the chance that the customer would subscribe'."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3f6f5313",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b001359",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('subscribed', inplace=True, axis=1)\n",
    "df.replace('unknown', pd.NA, inplace=True, regex=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a688ad10",
   "metadata": {},
   "source": [
    "We can start getting into the data preparation. Going through each of the previously mentioned attributes, we can start preprocessing. \n",
    "\n",
    "While preprocessing, we should create some simple functions to save time when preprocessing the unknown dataset. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7fe501b2",
   "metadata": {},
   "source": [
    "Job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81c8dab6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:4901: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "def pre_process_job(df):  \n",
    "    job_dummy = pd.get_dummies(df.job)\n",
    "    df.drop('job', axis=1, inplace=True)\n",
    "    df = pd.concat([df, job_dummy], axis=1)\n",
    "    return df\n",
    "\n",
    "df['job'].isna().sum() #A minor 0.08% (212) here is na so lets drop these\n",
    "df = df[df['job'].notna()]\n",
    "\n",
    "df = pre_process_job(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a966514f",
   "metadata": {},
   "source": [
    "Marital  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c4238476",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_marital(df):\n",
    "    marital_dummy = pd.get_dummies(df.marital)\n",
    "    df.drop('marital', inplace=True, axis=1)\n",
    "    df = pd.concat([df, marital_dummy], axis=1)\n",
    "    return df\n",
    "\n",
    "df['marital'].isna().sum() #These cannot really be replaced so it is best to drop these 51 occurences\n",
    "df = df[df['marital'].notna()]\n",
    "\n",
    "df = pre_process_marital(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "997b757a",
   "metadata": {},
   "source": [
    "Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79992053",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_education(df):\n",
    "    df['education'].replace(\"illiterate\", 0, inplace=True, regex=True)\n",
    "    df['education'].replace(\"basic.4y\", 1, inplace=True, regex=True)\n",
    "    df['education'].replace(\"basic.6y\", 2, inplace=True, regex=True)\n",
    "    df['education'].replace(\"basic.9y\", 3, inplace=True, regex=True)\n",
    "    df['education'].replace(\"high.school\", 4, inplace=True, regex=True)\n",
    "    df['education'].replace(\"professional.course\", 5, inplace=True, regex=True)\n",
    "    df['education'].replace(\"university.degree\", 6, inplace=True, regex=True)\n",
    "    return df\n",
    "\n",
    "df = df[df['education'].notna()] #Drop all the remaining rows with nan, as this column is important to consider\n",
    "\n",
    "df = pre_process_education(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5339d497",
   "metadata": {},
   "source": [
    "Default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c451d657",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"default\", axis=1, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ff8418ff",
   "metadata": {},
   "source": [
    "Housing and loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1aac9a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    13292\n",
       "no     11198\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"housing\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1a0cbed",
   "metadata": {},
   "source": [
    "Every instance where housing is a NAN value, is where loan is also NAN. \n",
    "There are 624 instances like this and because it is binary, it is not safe to simply replace them with an average or a guesstimate. \n",
    "Here these instances should be dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52cfa40b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CL-11\\AppData\\Local\\Temp/ipykernel_30288/2353928684.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  df[df['housing'].isna()][df['loan'].isna()]\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",
       "        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>age</th>\n",
       "      <th>education</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>...</th>\n",
       "      <th>management</th>\n",
       "      <th>retired</th>\n",
       "      <th>self-employed</th>\n",
       "      <th>services</th>\n",
       "      <th>student</th>\n",
       "      <th>technician</th>\n",
       "      <th>unemployed</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>249</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>437</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>176</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>352</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>153</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26179</th>\n",
       "      <td>57</td>\n",
       "      <td>6</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>wed</td>\n",
       "      <td>229</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>26190</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>fri</td>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26212</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>wed</td>\n",
       "      <td>216</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26244</th>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26255</th>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>thu</td>\n",
       "      <td>212</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age education housing  loan    contact month day_of_week  duration  \\\n",
       "51      51         1    <NA>  <NA>  telephone   may         mon       212   \n",
       "249     59         1    <NA>  <NA>  telephone   may         tue       437   \n",
       "259     48         4    <NA>  <NA>  telephone   may         tue       152   \n",
       "314     36         6    <NA>  <NA>  telephone   may         tue       176   \n",
       "352     50         1    <NA>  <NA>  telephone   may         tue       153   \n",
       "...    ...       ...     ...   ...        ...   ...         ...       ...   \n",
       "26179   57         6    <NA>  <NA>   cellular   oct         wed       229   \n",
       "26190   60         1    <NA>  <NA>   cellular   oct         fri       272   \n",
       "26212   66         1    <NA>  <NA>   cellular   oct         wed       216   \n",
       "26244   31         6    <NA>  <NA>   cellular   oct         tue       192   \n",
       "26255   31         5    <NA>  <NA>   cellular   oct         thu       212   \n",
       "\n",
       "       campaign  pdays  ...  management retired  self-employed  services  \\\n",
       "51            1    999  ...           0       0              0         0   \n",
       "249           1    999  ...           1       0              0         0   \n",
       "259           1    999  ...           0       0              0         1   \n",
       "314           2    999  ...           0       0              0         0   \n",
       "352           1    999  ...           0       0              0         0   \n",
       "...         ...    ...  ...         ...     ...            ...       ...   \n",
       "26179         2      3  ...           0       0              0         0   \n",
       "26190         1     10  ...           0       1              0         0   \n",
       "26212         1      6  ...           0       1              0         0   \n",
       "26244         1      3  ...           0       0              0         0   \n",
       "26255         2    999  ...           0       0              0         0   \n",
       "\n",
       "       student  technician  unemployed  divorced  married  single  \n",
       "51           0           0           0         0        1       0  \n",
       "249          0           0           0         0        1       0  \n",
       "259          0           0           0         0        1       0  \n",
       "314          0           0           0         0        1       0  \n",
       "352          0           0           0         0        1       0  \n",
       "...        ...         ...         ...       ...      ...     ...  \n",
       "26179        0           0           0         0        1       0  \n",
       "26190        0           0           0         0        1       0  \n",
       "26212        0           0           0         0        1       0  \n",
       "26244        0           0           0         0        0       1  \n",
       "26255        0           1           0         0        0       1  \n",
       "\n",
       "[624 rows x 31 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['housing'].isna()][df['loan'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b128fa42",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_housing_loan(df):\n",
    "    df = df[df['housing'].notna()]\n",
    "    df = df[df['loan'].notna()]\n",
    "    return df\n",
    "\n",
    "df = preprocess_housing_loan(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1f9e1a3",
   "metadata": {},
   "source": [
    "contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0446850a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_contact(df):\n",
    "    df.replace('telephone', 1, inplace=True)\n",
    "    df.replace('cellular', 0, inplace=True)\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = pre_process_contact(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6872473b",
   "metadata": {},
   "source": [
    "month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e0a7754",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_month(df):\n",
    "    df['month'].isna().sum()\n",
    "\n",
    "    months = ['jan', 'feb', 'mar', 'apr',\n",
    "              'may', 'jun', 'jul', 'aug',\n",
    "             'sep', 'oct', 'nov', 'dec']\n",
    "\n",
    "    i = 1\n",
    "    for m in months:\n",
    "        df.month.replace(m, i, inplace=True)\n",
    "        i+=1\n",
    "    return df\n",
    "\n",
    "df = pre_process_month(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d3b27542",
   "metadata": {},
   "source": [
    "day_of_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "482afb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_day(df):\n",
    "    df['day_of_week'].isna().sum()\n",
    "\n",
    "    days = ['mon', 'tue', 'wed',\n",
    "           'thu', 'fri', 'sat',\n",
    "           'sun']\n",
    "    i = 1\n",
    "    for d in days:\n",
    "        df.day_of_week.replace(d, i, inplace=True)\n",
    "        i+=1\n",
    "    return df\n",
    "\n",
    "df = pre_process_day(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5a662f61",
   "metadata": {},
   "source": [
    "poutcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3ac8553",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_process_poutcome(df):\n",
    "    df.poutcome.value_counts() #nonexistent:21132, fail:2533, success:825\n",
    "    poutcome_dummy = pd.get_dummies(df.poutcome)\n",
    "    df.drop('poutcome', inplace=True, axis=1)\n",
    "    df = pd.concat([df, poutcome_dummy], axis=1)\n",
    "    \n",
    "    return df\n",
    "    \n",
    "df =  pre_process_poutcome(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "48a718fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26e287c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['subscribed'] = target\n",
    "df.replace(\"yes\", 1, inplace=True)\n",
    "df.replace(\"no\", 0, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "556cd407",
   "metadata": {},
   "source": [
    "# Data analysis"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44120df5",
   "metadata": {},
   "source": [
    "## Train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a2676dea",
   "metadata": {},
   "outputs": [],
   "source": [
    "features, target = list(df.iloc[:,:-1].columns), list(df.iloc[:,-1:].columns)\n",
    "\n",
    "X, y = df.drop(target, axis=1), df[target]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "65c91c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "nu_features = {\n",
    "    0:features[0], 1:features[1], 2:features[2],\n",
    "    3:features[3], 4:features[4], 5:features[5],\n",
    "    6:features[6], 7:features[7], 8:features[8],\n",
    "    9:features[9], 10:features[10], \n",
    "     \n",
    "    11:features[11],\n",
    "    12:features[12], 13:features[13], 14:features[14],\n",
    "    15:features[15], 16:features[16], 17:features[17],\n",
    "    18:features[18], 19:features[19], 20:features[20],\n",
    "     \n",
    "    21:features[21],\n",
    "    22:features[22], 23:features[23], 24:features[24],\n",
    "    25:features[25], 26:features[26], 27:features[27],\n",
    "    28:features[28], 29:features[29], 30:features[30],\n",
    "     \n",
    "    31:features[31], 32:features[32]\n",
    " }\n",
    "\n",
    "X_train = pd.DataFrame(X_train).rename(columns=nu_features)\n",
    "y_train = pd.DataFrame(y_train).rename(columns={0:\"subscribed\"})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "48f890d8",
   "metadata": {},
   "source": [
    "## Classifiers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f8d396a2",
   "metadata": {},
   "source": [
    "### DT"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7706fbf9",
   "metadata": {},
   "source": [
    "DTs are capable of accurate classification while being simplistic and intuitive. DTs create a tree, starting from a root, creating branches with decision splits and leaf nodes which act as output predictions. This is done with the ID3 algorithm which checks each instance attribute with a statistical test to see how it does. \n",
    "\n",
    "The criterion used was entropy instead of gini. Entropy, the measure of uncertainty, was utilised to evaluate the quality of the features. I had a much clearer idea of how to evaluate the resulting features with entropy, which is why I opted to use it. \n",
    "\n",
    "The depth directly affects the quality of the predictions. Experimentation with this parameter was required, as there is no ‘best’ depth, only one which provides good results. A depth of 5 generalised too much while a depth of 10 was overfitting- it was finalised at a depth of 6. This provided consistently good results with no signs of underfitting/overfitting. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b8344c7e",
   "metadata": {},
   "outputs": [
    {
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5087.65</text>\r\n<text text-anchor=\"middle\" x=\"2456\" y=\"-751.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.502</text>\r\n<text text-anchor=\"middle\" x=\"2456\" y=\"-736.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 19592</text>\r\n<text text-anchor=\"middle\" x=\"2456\" y=\"-721.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [17426, 2166]</text>\r\n<text text-anchor=\"middle\" x=\"2456\" y=\"-706.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 1 -->\r\n<g id=\"node2\" class=\"node\">\r\n<title>1</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1724.5,-663 1593.5,-663 1593.5,-580 1724.5,-580 1724.5,-663\"/>\r\n<text text-anchor=\"middle\" x=\"1659\" y=\"-647.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 163.5</text>\r\n<text text-anchor=\"middle\" x=\"1659\" y=\"-632.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.991</text>\r\n<text text-anchor=\"middle\" x=\"1659\" y=\"-617.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2272</text>\r\n<text text-anchor=\"middle\" x=\"1659\" y=\"-602.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1265, 1007]</text>\r\n<text text-anchor=\"middle\" x=\"1659\" y=\"-587.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 0&#45;&gt;1 -->\r\n<g id=\"edge1\" class=\"edge\">\r\n<title>0&#45;&gt;1</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2378.49,-728.12C2225.54,-705.67 1886.57,-655.91 1734.91,-633.64\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1735.16,-630.14 1724.76,-632.15 1734.15,-637.07 1735.16,-630.14\"/>\r\n<text text-anchor=\"middle\" x=\"1739.68\" y=\"-648.51\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">True</text>\r\n</g>\r\n<!-- 48 -->\r\n<g id=\"node49\" 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font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"574\" y=\"-387.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 126</text>\r\n<text text-anchor=\"middle\" x=\"574\" y=\"-372.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [126, 0]</text>\r\n<text text-anchor=\"middle\" x=\"574\" y=\"-357.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 2&#45;&gt;3 -->\r\n<g id=\"edge3\" class=\"edge\">\r\n<title>2&#45;&gt;3</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M658.24,-460.91C645.23,-449.1 631.02,-436.22 618.05,-424.45\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"620.33,-421.79 610.57,-417.67 615.62,-426.98 620.33,-421.79\"/>\r\n</g>\r\n<!-- 4 -->\r\n<g id=\"node5\" class=\"node\">\r\n<title>4</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"762,-425 644,-425 644,-342 762,-342 762,-425\"/>\r\n<text text-anchor=\"middle\" x=\"703\" y=\"-409.8\" 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y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [17, 11]</text>\r\n<text text-anchor=\"middle\" x=\"177\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 5&#45;&gt;6 -->\r\n<g id=\"edge6\" class=\"edge\">\r\n<title>5&#45;&gt;6</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M329.26,-232.15C305.04,-218.52 276.33,-202.37 250.41,-187.79\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"251.8,-184.56 241.37,-182.71 248.37,-190.66 251.8,-184.56\"/>\r\n</g>\r\n<!-- 9 -->\r\n<g id=\"node10\" class=\"node\">\r\n<title>9</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"464,-187 306,-187 306,-104 464,-104 464,-187\"/>\r\n<text text-anchor=\"middle\" x=\"385\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">cons.price.idx &lt;= 93.812</text>\r\n<text text-anchor=\"middle\" x=\"385\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.493</text>\r\n<text text-anchor=\"middle\" x=\"385\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 371</text>\r\n<text text-anchor=\"middle\" x=\"385\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [331, 40]</text>\r\n<text text-anchor=\"middle\" x=\"385\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 5&#45;&gt;9 -->\r\n<g id=\"edge9\" class=\"edge\">\r\n<title>5&#45;&gt;9</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M385,-222.91C385,-214.65 385,-205.86 385,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"388.5,-197.02 385,-187.02 381.5,-197.02 388.5,-197.02\"/>\r\n</g>\r\n<!-- 7 -->\r\n<g id=\"node8\" class=\"node\">\r\n<title>7</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"106,-68 0,-68 0,0 106,0 106,-68\"/>\r\n<text text-anchor=\"middle\" x=\"53\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.991</text>\r\n<text text-anchor=\"middle\" x=\"53\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 18</text>\r\n<text text-anchor=\"middle\" x=\"53\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [8, 10]</text>\r\n<text text-anchor=\"middle\" x=\"53\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 6&#45;&gt;7 -->\r\n<g id=\"edge7\" class=\"edge\">\r\n<title>6&#45;&gt;7</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M130.83,-103.73C120.19,-94.33 108.88,-84.35 98.28,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"100.52,-72.3 90.71,-68.3 95.89,-77.54 100.52,-72.3\"/>\r\n</g>\r\n<!-- 8 -->\r\n<g id=\"node9\" class=\"node\">\r\n<title>8</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"230,-68 124,-68 124,0 230,0 230,-68\"/>\r\n<text text-anchor=\"middle\" x=\"177\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.469</text>\r\n<text text-anchor=\"middle\" x=\"177\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 10</text>\r\n<text text-anchor=\"middle\" x=\"177\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9, 1]</text>\r\n<text text-anchor=\"middle\" x=\"177\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 6&#45;&gt;8 -->\r\n<g id=\"edge8\" class=\"edge\">\r\n<title>6&#45;&gt;8</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M177,-103.73C177,-95.52 177,-86.86 177,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"180.5,-78.3 177,-68.3 173.5,-78.3 180.5,-78.3\"/>\r\n</g>\r\n<!-- 10 -->\r\n<g id=\"node11\" class=\"node\">\r\n<title>10</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"359.5,-68 248.5,-68 248.5,0 359.5,0 359.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"304\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.547</text>\r\n<text text-anchor=\"middle\" x=\"304\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 309</text>\r\n<text text-anchor=\"middle\" x=\"304\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [270, 39]</text>\r\n<text text-anchor=\"middle\" x=\"304\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 9&#45;&gt;10 -->\r\n<g id=\"edge10\" class=\"edge\">\r\n<title>9&#45;&gt;10</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M354.84,-103.73C348.23,-94.79 341.22,-85.32 334.59,-76.36\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"337.39,-74.26 328.63,-68.3 331.77,-78.42 337.39,-74.26\"/>\r\n</g>\r\n<!-- 11 -->\r\n<g id=\"node12\" class=\"node\">\r\n<title>11</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"484,-68 378,-68 378,0 484,0 484,-68\"/>\r\n<text text-anchor=\"middle\" x=\"431\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.119</text>\r\n<text text-anchor=\"middle\" x=\"431\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 62</text>\r\n<text text-anchor=\"middle\" x=\"431\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [61, 1]</text>\r\n<text text-anchor=\"middle\" x=\"431\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 9&#45;&gt;11 -->\r\n<g id=\"edge11\" class=\"edge\">\r\n<title>9&#45;&gt;11</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M402.13,-103.73C405.69,-95.24 409.46,-86.28 413.05,-77.73\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"416.36,-78.87 417.01,-68.3 409.91,-76.16 416.36,-78.87\"/>\r\n</g>\r\n<!-- 13 -->\r\n<g id=\"node14\" class=\"node\">\r\n<title>13</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"703,-187 569,-187 569,-104 703,-104 703,-187\"/>\r\n<text text-anchor=\"middle\" x=\"636\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">day_of_week &lt;= 1.5</text>\r\n<text text-anchor=\"middle\" x=\"636\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.884</text>\r\n<text text-anchor=\"middle\" x=\"636\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 278</text>\r\n<text text-anchor=\"middle\" x=\"636\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [194, 84]</text>\r\n<text text-anchor=\"middle\" x=\"636\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 12&#45;&gt;13 -->\r\n<g id=\"edge13\" class=\"edge\">\r\n<title>12&#45;&gt;13</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M679.75,-222.91C674.77,-214.2 669.44,-204.9 664.29,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"667.21,-193.96 659.2,-187.02 661.14,-197.44 667.21,-193.96\"/>\r\n</g>\r\n<!-- 16 -->\r\n<g id=\"node17\" class=\"node\">\r\n<title>16</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"819,-179.5 721,-179.5 721,-111.5 819,-111.5 819,-179.5\"/>\r\n<text text-anchor=\"middle\" x=\"770\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"770\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 21</text>\r\n<text text-anchor=\"middle\" x=\"770\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [21, 0]</text>\r\n<text text-anchor=\"middle\" x=\"770\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 12&#45;&gt;16 -->\r\n<g id=\"edge16\" class=\"edge\">\r\n<title>12&#45;&gt;16</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M726.25,-222.91C732.63,-211.76 739.56,-199.66 745.98,-188.44\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"749.07,-190.08 751.01,-179.67 743,-186.61 749.07,-190.08\"/>\r\n</g>\r\n<!-- 14 -->\r\n<g id=\"node15\" class=\"node\">\r\n<title>14</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"608,-68 502,-68 502,0 608,0 608,-68\"/>\r\n<text text-anchor=\"middle\" x=\"555\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.619</text>\r\n<text text-anchor=\"middle\" x=\"555\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 65</text>\r\n<text text-anchor=\"middle\" x=\"555\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [55, 10]</text>\r\n<text text-anchor=\"middle\" x=\"555\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 13&#45;&gt;14 -->\r\n<g id=\"edge14\" class=\"edge\">\r\n<title>13&#45;&gt;14</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M605.84,-103.73C599.23,-94.79 592.22,-85.32 585.59,-76.36\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"588.39,-74.26 579.63,-68.3 582.77,-78.42 588.39,-74.26\"/>\r\n</g>\r\n<!-- 15 -->\r\n<g id=\"node16\" class=\"node\">\r\n<title>15</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"737.5,-68 626.5,-68 626.5,0 737.5,0 737.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"682\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.932</text>\r\n<text text-anchor=\"middle\" x=\"682\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 213</text>\r\n<text text-anchor=\"middle\" x=\"682\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [139, 74]</text>\r\n<text text-anchor=\"middle\" x=\"682\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 13&#45;&gt;15 -->\r\n<g id=\"edge15\" class=\"edge\">\r\n<title>13&#45;&gt;15</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M653.13,-103.73C656.69,-95.24 660.46,-86.28 664.05,-77.73\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"667.36,-78.87 668.01,-68.3 660.91,-76.16 667.36,-78.87\"/>\r\n</g>\r\n<!-- 18 -->\r\n<g id=\"node19\" class=\"node\">\r\n<title>18</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1537,-425 1419,-425 1419,-342 1537,-342 1537,-425\"/>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 248.5</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.999</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1043</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [504, 539]</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 17&#45;&gt;18 -->\r\n<g id=\"edge18\" class=\"edge\">\r\n<title>17&#45;&gt;18</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1599.73,-463.19C1582.46,-452.02 1563.45,-439.73 1545.69,-428.26\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1547.39,-425.19 1537.09,-422.7 1543.59,-431.06 1547.39,-425.19\"/>\r\n</g>\r\n<!-- 33 -->\r\n<g id=\"node34\" class=\"node\">\r\n<title>33</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2068,-425 1920,-425 1920,-342 2068,-342 2068,-425\"/>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">nr.employed &lt;= 5020.5</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.675</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 405</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [72, 333]</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 17&#45;&gt;33 -->\r\n<g id=\"edge33\" class=\"edge\">\r\n<title>17&#45;&gt;33</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1718.12,-480.85C1771.41,-462.24 1850.35,-434.67 1910.15,-413.78\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1911.67,-416.96 1919.95,-410.36 1909.36,-410.35 1911.67,-416.96\"/>\r\n</g>\r\n<!-- 19 -->\r\n<g id=\"node20\" class=\"node\">\r\n<title>19</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1180,-306 1062,-306 1062,-223 1180,-223 1180,-306\"/>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">contact &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.949</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 367</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [232, 135]</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 18&#45;&gt;19 -->\r\n<g id=\"edge19\" class=\"edge\">\r\n<title>18&#45;&gt;19</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1418.65,-363.05C1355.62,-342.39 1256.13,-309.79 1189.74,-288.03\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1190.79,-284.69 1180.2,-284.9 1188.61,-291.34 1190.79,-284.69\"/>\r\n</g>\r\n<!-- 26 -->\r\n<g id=\"node27\" class=\"node\">\r\n<title>26</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1545,-306 1411,-306 1411,-223 1545,-223 1545,-306\"/>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">day_of_week &lt;= 1.5</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.972</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 676</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [272, 404]</text>\r\n<text text-anchor=\"middle\" x=\"1478\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 18&#45;&gt;26 -->\r\n<g id=\"edge26\" class=\"edge\">\r\n<title>18&#45;&gt;26</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1478,-341.91C1478,-333.65 1478,-324.86 1478,-316.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1481.5,-316.02 1478,-306.02 1474.5,-316.02 1481.5,-316.02\"/>\r\n</g>\r\n<!-- 20 -->\r\n<g id=\"node21\" class=\"node\">\r\n<title>20</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"995,-187 877,-187 877,-104 995,-104 995,-187\"/>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 191.5</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.97</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 316</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [190, 126]</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 19&#45;&gt;20 -->\r\n<g id=\"edge20\" class=\"edge\">\r\n<title>19&#45;&gt;20</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1061.96,-226.16C1043.43,-214.44 1022.84,-201.42 1003.76,-189.36\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1005.44,-186.27 995.12,-183.89 1001.7,-192.19 1005.44,-186.27\"/>\r\n</g>\r\n<!-- 23 -->\r\n<g id=\"node24\" class=\"node\">\r\n<title>23</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1177.5,-187 1064.5,-187 1064.5,-104 1177.5,-104 1177.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">technician &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.672</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 51</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [42, 9]</text>\r\n<text text-anchor=\"middle\" x=\"1121\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 19&#45;&gt;23 -->\r\n<g id=\"edge23\" class=\"edge\">\r\n<title>19&#45;&gt;23</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1121,-222.91C1121,-214.65 1121,-205.86 1121,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1124.5,-197.02 1121,-187.02 1117.5,-197.02 1124.5,-197.02\"/>\r\n</g>\r\n<!-- 21 -->\r\n<g id=\"node22\" class=\"node\">\r\n<title>21</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"862,-68 756,-68 756,0 862,0 862,-68\"/>\r\n<text text-anchor=\"middle\" x=\"809\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.913</text>\r\n<text text-anchor=\"middle\" x=\"809\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 122</text>\r\n<text text-anchor=\"middle\" x=\"809\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [82, 40]</text>\r\n<text text-anchor=\"middle\" x=\"809\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 20&#45;&gt;21 -->\r\n<g id=\"edge21\" class=\"edge\">\r\n<title>20&#45;&gt;21</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M888.71,-103.73C877.81,-94.33 866.24,-84.35 855.38,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"857.48,-72.18 847.62,-68.3 852.91,-77.48 857.48,-72.18\"/>\r\n</g>\r\n<!-- 22 -->\r\n<g id=\"node23\" class=\"node\">\r\n<title>22</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"991.5,-68 880.5,-68 880.5,0 991.5,0 991.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.991</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 194</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [108, 86]</text>\r\n<text text-anchor=\"middle\" x=\"936\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 20&#45;&gt;22 -->\r\n<g id=\"edge22\" class=\"edge\">\r\n<title>20&#45;&gt;22</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M936,-103.73C936,-95.52 936,-86.86 936,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"939.5,-78.3 936,-68.3 932.5,-78.3 939.5,-78.3\"/>\r\n</g>\r\n<!-- 24 -->\r\n<g id=\"node25\" class=\"node\">\r\n<title>24</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1108.5,-68 1009.5,-68 1009.5,0 1108.5,0 1108.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"1059\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.75</text>\r\n<text text-anchor=\"middle\" x=\"1059\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 42</text>\r\n<text text-anchor=\"middle\" x=\"1059\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [33, 9]</text>\r\n<text text-anchor=\"middle\" x=\"1059\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 23&#45;&gt;24 -->\r\n<g id=\"edge24\" class=\"edge\">\r\n<title>23&#45;&gt;24</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1097.91,-103.73C1093.01,-95.06 1087.82,-85.9 1082.88,-77.18\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1085.83,-75.28 1077.85,-68.3 1079.74,-78.73 1085.83,-75.28\"/>\r\n</g>\r\n<!-- 25 -->\r\n<g id=\"node26\" class=\"node\">\r\n<title>25</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1219,-68 1127,-68 1127,0 1219,0 1219,-68\"/>\r\n<text text-anchor=\"middle\" x=\"1173\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"1173\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n<text text-anchor=\"middle\" x=\"1173\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9, 0]</text>\r\n<text text-anchor=\"middle\" x=\"1173\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 23&#45;&gt;25 -->\r\n<g id=\"edge25\" class=\"edge\">\r\n<title>23&#45;&gt;25</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1140.36,-103.73C1144.43,-95.15 1148.74,-86.09 1152.84,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1156.06,-78.83 1157.19,-68.3 1149.74,-75.83 1156.06,-78.83\"/>\r\n</g>\r\n<!-- 27 -->\r\n<g id=\"node28\" class=\"node\">\r\n<title>27</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1456,-187 1350,-187 1350,-104 1456,-104 1456,-187\"/>\r\n<text text-anchor=\"middle\" x=\"1403\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">month &lt;= 7.5</text>\r\n<text text-anchor=\"middle\" x=\"1403\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.999</text>\r\n<text text-anchor=\"middle\" x=\"1403\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 137</text>\r\n<text text-anchor=\"middle\" x=\"1403\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [71, 66]</text>\r\n<text text-anchor=\"middle\" x=\"1403\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 26&#45;&gt;27 -->\r\n<g id=\"edge27\" class=\"edge\">\r\n<title>26&#45;&gt;27</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1451.98,-222.91C1446.34,-214.1 1440.31,-204.7 1434.48,-195.61\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1437.32,-193.55 1428.97,-187.02 1431.43,-197.33 1437.32,-193.55\"/>\r\n</g>\r\n<!-- 30 -->\r\n<g id=\"node31\" class=\"node\">\r\n<title>30</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1632,-187 1474,-187 1474,-104 1632,-104 1632,-187\"/>\r\n<text text-anchor=\"middle\" x=\"1553\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">cons.price.idx &lt;= 92.559</text>\r\n<text text-anchor=\"middle\" x=\"1553\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.953</text>\r\n<text text-anchor=\"middle\" x=\"1553\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 539</text>\r\n<text text-anchor=\"middle\" x=\"1553\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [201, 338]</text>\r\n<text text-anchor=\"middle\" x=\"1553\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 26&#45;&gt;30 -->\r\n<g id=\"edge30\" class=\"edge\">\r\n<title>26&#45;&gt;30</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1504.02,-222.91C1509.66,-214.1 1515.69,-204.7 1521.52,-195.61\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1524.57,-197.33 1527.03,-187.02 1518.68,-193.55 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d=\"M1548.53,-103.73C1547.62,-95.43 1546.66,-86.67 1545.74,-78.28\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1549.22,-77.86 1544.65,-68.3 1542.26,-78.62 1549.22,-77.86\"/>\r\n</g>\r\n<!-- 32 -->\r\n<g id=\"node33\" class=\"node\">\r\n<title>32</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1733,-68 1615,-68 1615,0 1733,0 1733,-68\"/>\r\n<text text-anchor=\"middle\" x=\"1674\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.913</text>\r\n<text text-anchor=\"middle\" x=\"1674\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 335</text>\r\n<text text-anchor=\"middle\" x=\"1674\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [110, 225]</text>\r\n<text text-anchor=\"middle\" x=\"1674\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 30&#45;&gt;32 -->\r\n<g id=\"edge32\" class=\"edge\">\r\n<title>30&#45;&gt;32</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1598.06,-103.73C1608.34,-94.42 1619.25,-84.54 1629.51,-75.26\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1632.14,-77.6 1637.2,-68.3 1627.44,-72.41 1632.14,-77.6\"/>\r\n</g>\r\n<!-- 34 -->\r\n<g id=\"node35\" class=\"node\">\r\n<title>34</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2058,-306 1930,-306 1930,-223 2058,-223 2058,-306\"/>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 0.879</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.596</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 318</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [46, 272]</text>\r\n<text text-anchor=\"middle\" x=\"1994\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 33&#45;&gt;34 -->\r\n<g id=\"edge34\" class=\"edge\">\r\n<title>33&#45;&gt;34</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M1994,-341.91C1994,-333.65 1994,-324.86 1994,-316.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1997.5,-316.02 1994,-306.02 1990.5,-316.02 1997.5,-316.02\"/>\r\n</g>\r\n<!-- 41 -->\r\n<g id=\"node42\" class=\"node\">\r\n<title>41</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2398.5,-306 2281.5,-306 2281.5,-223 2398.5,-223 2398.5,-306\"/>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 235.0</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.88</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 87</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [26, 61]</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 33&#45;&gt;41 -->\r\n<g id=\"edge41\" class=\"edge\">\r\n<title>33&#45;&gt;41</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2068.23,-357.4C2128.6,-336.98 2213.12,-308.4 2271.81,-288.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2273.01,-291.85 2281.37,-285.33 2270.77,-285.22 2273.01,-291.85\"/>\r\n</g>\r\n<!-- 35 -->\r\n<g id=\"node36\" class=\"node\">\r\n<title>35</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"1986.5,-187 1869.5,-187 1869.5,-104 1986.5,-104 1986.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"1928\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration 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stroke=\"black\" d=\"M1929.12,-103.73C1929.34,-95.52 1929.58,-86.86 1929.81,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"1933.31,-78.39 1930.09,-68.3 1926.32,-78.2 1933.31,-78.39\"/>\r\n</g>\r\n<!-- 39 -->\r\n<g id=\"node40\" class=\"node\">\r\n<title>39</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2111,-68 2005,-68 2005,0 2111,0 2111,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2058\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.267</text>\r\n<text text-anchor=\"middle\" x=\"2058\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 88</text>\r\n<text text-anchor=\"middle\" x=\"2058\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4, 84]</text>\r\n<text text-anchor=\"middle\" x=\"2058\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 38&#45;&gt;39 -->\r\n<g id=\"edge39\" class=\"edge\">\r\n<title>38&#45;&gt;39</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2059.26,-103.73C2059.11,-95.52 2058.95,-86.86 2058.8,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2062.29,-78.23 2058.61,-68.3 2055.29,-78.36 2062.29,-78.23\"/>\r\n</g>\r\n<!-- 40 -->\r\n<g id=\"node41\" class=\"node\">\r\n<title>40</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2221,-68 2129,-68 2129,0 2221,0 2221,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2175\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"2175\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n<text text-anchor=\"middle\" x=\"2175\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 0]</text>\r\n<text text-anchor=\"middle\" x=\"2175\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 38&#45;&gt;40 -->\r\n<g id=\"edge40\" class=\"edge\">\r\n<title>38&#45;&gt;40</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2102.82,-103.73C2112.59,-94.42 2122.97,-84.54 2132.72,-75.26\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2135.2,-77.73 2140.03,-68.3 2130.37,-72.66 2135.2,-77.73\"/>\r\n</g>\r\n<!-- 42 -->\r\n<g id=\"node43\" class=\"node\">\r\n<title>42</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2393,-187 2287,-187 2287,-104 2393,-104 2393,-187\"/>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">age &lt;= 71.0</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.337</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 15]</text>\r\n<text text-anchor=\"middle\" x=\"2340\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 41&#45;&gt;42 -->\r\n<g id=\"edge42\" class=\"edge\">\r\n<title>41&#45;&gt;42</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2340,-222.91C2340,-214.65 2340,-205.86 2340,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2343.5,-197.02 2340,-187.02 2336.5,-197.02 2343.5,-197.02\"/>\r\n</g>\r\n<!-- 45 -->\r\n<g id=\"node46\" class=\"node\">\r\n<title>45</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2573.5,-187 2462.5,-187 2462.5,-104 2573.5,-104 2573.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">campaign &lt;= 1.5</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.936</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 71</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [25, 46]</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 41&#45;&gt;45 -->\r\n<g id=\"edge45\" class=\"edge\">\r\n<title>41&#45;&gt;45</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2398.78,-224.87C2416.36,-213.31 2435.72,-200.58 2453.64,-188.81\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2455.87,-191.53 2462.3,-183.11 2452.02,-185.68 2455.87,-191.53\"/>\r\n</g>\r\n<!-- 43 -->\r\n<g id=\"node44\" class=\"node\">\r\n<title>43</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2337,-68 2239,-68 2239,0 2337,0 2337,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2288\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"2288\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15</text>\r\n<text text-anchor=\"middle\" x=\"2288\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 15]</text>\r\n<text text-anchor=\"middle\" x=\"2288\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 42&#45;&gt;43 -->\r\n<g id=\"edge43\" class=\"edge\">\r\n<title>42&#45;&gt;43</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2320.64,-103.73C2316.57,-95.15 2312.26,-86.09 2308.16,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2311.26,-75.83 2303.81,-68.3 2304.94,-78.83 2311.26,-75.83\"/>\r\n</g>\r\n<!-- 44 -->\r\n<g id=\"node45\" class=\"node\">\r\n<title>44</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2447,-68 2355,-68 2355,0 2447,0 2447,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2401\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"2401\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n<text text-anchor=\"middle\" x=\"2401\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 0]</text>\r\n<text text-anchor=\"middle\" x=\"2401\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 42&#45;&gt;44 -->\r\n<g id=\"edge44\" class=\"edge\">\r\n<title>42&#45;&gt;44</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2362.71,-103.73C2367.54,-95.06 2372.65,-85.9 2377.5,-77.18\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2380.64,-78.74 2382.45,-68.3 2374.53,-75.33 2380.64,-78.74\"/>\r\n</g>\r\n<!-- 46 -->\r\n<g id=\"node47\" class=\"node\">\r\n<title>46</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2571,-68 2465,-68 2465,0 2571,0 2571,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.999</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 29</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14, 15]</text>\r\n<text text-anchor=\"middle\" x=\"2518\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 45&#45;&gt;46 -->\r\n<g id=\"edge46\" class=\"edge\">\r\n<title>45&#45;&gt;46</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2518,-103.73C2518,-95.52 2518,-86.86 2518,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2521.5,-78.3 2518,-68.3 2514.5,-78.3 2521.5,-78.3\"/>\r\n</g>\r\n<!-- 47 -->\r\n<g id=\"node48\" class=\"node\">\r\n<title>47</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2693,-68 2589,-68 2589,0 2693,0 2693,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2641\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.83</text>\r\n<text text-anchor=\"middle\" x=\"2641\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 42</text>\r\n<text text-anchor=\"middle\" x=\"2641\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [11, 31]</text>\r\n<text text-anchor=\"middle\" x=\"2641\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 45&#45;&gt;47 -->\r\n<g id=\"edge47\" class=\"edge\">\r\n<title>45&#45;&gt;47</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2563.8,-103.73C2574.35,-94.33 2585.57,-84.35 2596.08,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2598.45,-77.56 2603.59,-68.3 2593.8,-72.33 2598.45,-77.56\"/>\r\n</g>\r\n<!-- 49 -->\r\n<g id=\"node50\" class=\"node\">\r\n<title>49</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3571.5,-544 3440.5,-544 3440.5,-461 3571.5,-461 3571.5,-544\"/>\r\n<text text-anchor=\"middle\" x=\"3506\" y=\"-528.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">month &lt;= 4.5</text>\r\n<text text-anchor=\"middle\" x=\"3506\" y=\"-513.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.149</text>\r\n<text text-anchor=\"middle\" x=\"3506\" y=\"-498.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 15038</text>\r\n<text text-anchor=\"middle\" x=\"3506\" y=\"-483.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14718, 320]</text>\r\n<text text-anchor=\"middle\" x=\"3506\" y=\"-468.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 48&#45;&gt;49 -->\r\n<g id=\"edge49\" class=\"edge\">\r\n<title>48&#45;&gt;49</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3506,-579.91C3506,-571.65 3506,-562.86 3506,-554.3\"/>\r\n<polygon fill=\"black\" 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-->\r\n<g id=\"edge78\" class=\"edge\">\r\n<title>48&#45;&gt;78</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3575.17,-615.17C3827.01,-595.76 4693.68,-528.96 4951.25,-509.11\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4951.54,-512.6 4961.24,-508.34 4951.01,-505.62 4951.54,-512.6\"/>\r\n</g>\r\n<!-- 50 -->\r\n<g id=\"node51\" class=\"node\">\r\n<title>50</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3344,-425 3186,-425 3186,-342 3344,-342 3344,-425\"/>\r\n<text text-anchor=\"middle\" x=\"3265\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">cons.price.idx &lt;= 92.959</text>\r\n<text text-anchor=\"middle\" x=\"3265\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.616</text>\r\n<text text-anchor=\"middle\" x=\"3265\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1129</text>\r\n<text text-anchor=\"middle\" x=\"3265\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [957, 172]</text>\r\n<text text-anchor=\"middle\" x=\"3265\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 49&#45;&gt;50 -->\r\n<g id=\"edge50\" class=\"edge\">\r\n<title>49&#45;&gt;50</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3440.46,-469.68C3413.67,-456.68 3382.27,-441.43 3353.5,-427.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3354.78,-424.19 3344.25,-422.98 3351.72,-430.49 3354.78,-424.19\"/>\r\n</g>\r\n<!-- 63 -->\r\n<g id=\"node64\" class=\"node\">\r\n<title>63</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3772.5,-425 3641.5,-425 3641.5,-342 3772.5,-342 3772.5,-425\"/>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">emp.var.rate &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.085</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 13909</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [13761, 148]</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 49&#45;&gt;63 -->\r\n<g id=\"edge63\" class=\"edge\">\r\n<title>49&#45;&gt;63</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3571.54,-463.35C3591,-452.02 3612.45,-439.53 3632.44,-427.9\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3634.33,-430.85 3641.21,-422.8 3630.81,-424.8 3634.33,-430.85\"/>\r\n</g>\r\n<!-- 51 -->\r\n<g id=\"node52\" class=\"node\">\r\n<title>51</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3121.5,-306 3004.5,-306 3004.5,-223 3121.5,-223 3121.5,-306\"/>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 167.5</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.988</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 138</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [78, 60]</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 50&#45;&gt;51 -->\r\n<g id=\"edge51\" class=\"edge\">\r\n<title>50&#45;&gt;51</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3194.92,-341.91C3173.98,-329.78 3151.08,-316.52 3130.32,-304.49\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3131.98,-301.41 3121.57,-299.42 3128.47,-307.46 3131.98,-301.41\"/>\r\n</g>\r\n<!-- 58 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d=\"M3265,-341.91C3265,-333.65 3265,-324.86 3265,-316.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3268.5,-316.02 3265,-306.02 3261.5,-316.02 3268.5,-316.02\"/>\r\n</g>\r\n<!-- 52 -->\r\n<g id=\"node53\" class=\"node\">\r\n<title>52</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2941,-187 2835,-187 2835,-104 2941,-104 2941,-187\"/>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">age &lt;= 32.5</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.701</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 79</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [64, 15]</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 51&#45;&gt;52 -->\r\n<g id=\"edge52\" class=\"edge\">\r\n<title>51&#45;&gt;52</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3004.24,-224.22C2986.66,-212.46 2967.34,-199.55 2949.59,-187.68\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2951.27,-184.59 2941.01,-181.94 2947.38,-190.41 2951.27,-184.59\"/>\r\n</g>\r\n<!-- 55 -->\r\n<g id=\"node56\" class=\"node\">\r\n<title>55</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3126,-187 3000,-187 3000,-104 3126,-104 3126,-187\"/>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">unemployed &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.791</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 59</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [14, 45]</text>\r\n<text text-anchor=\"middle\" x=\"3063\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 51&#45;&gt;55 -->\r\n<g id=\"edge55\" class=\"edge\">\r\n<title>51&#45;&gt;55</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3063,-222.91C3063,-214.65 3063,-205.86 3063,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3066.5,-197.02 3063,-187.02 3059.5,-197.02 3066.5,-197.02\"/>\r\n</g>\r\n<!-- 53 -->\r\n<g id=\"node54\" class=\"node\">\r\n<title>53</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2817,-68 2711,-68 2711,0 2817,0 2817,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2764\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.863</text>\r\n<text text-anchor=\"middle\" x=\"2764\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 42</text>\r\n<text text-anchor=\"middle\" x=\"2764\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [30, 12]</text>\r\n<text text-anchor=\"middle\" x=\"2764\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 52&#45;&gt;53 -->\r\n<g id=\"edge53\" class=\"edge\">\r\n<title>52&#45;&gt;53</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2841.83,-103.73C2831.19,-94.33 2819.88,-84.35 2809.28,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2811.52,-72.3 2801.71,-68.3 2806.89,-77.54 2811.52,-72.3\"/>\r\n</g>\r\n<!-- 54 -->\r\n<g id=\"node55\" class=\"node\">\r\n<title>54</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"2941,-68 2835,-68 2835,0 2941,0 2941,-68\"/>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.406</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 37</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [34, 3]</text>\r\n<text text-anchor=\"middle\" x=\"2888\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 52&#45;&gt;54 -->\r\n<g id=\"edge54\" class=\"edge\">\r\n<title>52&#45;&gt;54</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M2888,-103.73C2888,-95.52 2888,-86.86 2888,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"2891.5,-78.3 2888,-68.3 2884.5,-78.3 2891.5,-78.3\"/>\r\n</g>\r\n<!-- 56 -->\r\n<g id=\"node57\" class=\"node\">\r\n<title>56</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3065,-68 2959,-68 2959,0 3065,0 3065,-68\"/>\r\n<text text-anchor=\"middle\" x=\"3012\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.742</text>\r\n<text text-anchor=\"middle\" x=\"3012\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 57</text>\r\n<text text-anchor=\"middle\" x=\"3012\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [12, 45]</text>\r\n<text text-anchor=\"middle\" x=\"3012\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 55&#45;&gt;56 -->\r\n<g id=\"edge56\" class=\"edge\">\r\n<title>55&#45;&gt;56</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3044.01,-103.73C3040.02,-95.15 3035.79,-86.09 3031.77,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3034.9,-75.89 3027.51,-68.3 3028.56,-78.84 3034.9,-75.89\"/>\r\n</g>\r\n<!-- 57 -->\r\n<g id=\"node58\" class=\"node\">\r\n<title>57</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3175,-68 3083,-68 3083,0 3175,0 3175,-68\"/>\r\n<text text-anchor=\"middle\" x=\"3129\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"3129\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n<text text-anchor=\"middle\" x=\"3129\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 0]</text>\r\n<text text-anchor=\"middle\" x=\"3129\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 55&#45;&gt;57 -->\r\n<g id=\"edge57\" class=\"edge\">\r\n<title>55&#45;&gt;57</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3087.58,-103.73C3092.85,-94.97 3098.44,-85.7 3103.74,-76.91\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3106.76,-78.67 3108.93,-68.3 3100.77,-75.06 3106.76,-78.67\"/>\r\n</g>\r\n<!-- 59 -->\r\n<g id=\"node60\" class=\"node\">\r\n<title>59</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3249,-179.5 3145,-179.5 3145,-111.5 3249,-111.5 3249,-179.5\"/>\r\n<text text-anchor=\"middle\" x=\"3197\" y=\"-164.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"3197\" y=\"-149.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 164</text>\r\n<text text-anchor=\"middle\" x=\"3197\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [164, 0]</text>\r\n<text text-anchor=\"middle\" x=\"3197\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 58&#45;&gt;59 -->\r\n<g id=\"edge59\" class=\"edge\">\r\n<title>58&#45;&gt;59</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3241.41,-222.91C3234.93,-211.76 3227.9,-199.66 3221.37,-188.44\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3224.33,-186.55 3216.28,-179.67 3218.28,-190.07 3224.33,-186.55\"/>\r\n</g>\r\n<!-- 60 -->\r\n<g id=\"node61\" class=\"node\">\r\n<title>60</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3395,-187 3267,-187 3267,-104 3395,-104 3395,-187\"/>\r\n<text text-anchor=\"middle\" x=\"3331\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 1.402</text>\r\n<text text-anchor=\"middle\" x=\"3331\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.572</text>\r\n<text text-anchor=\"middle\" x=\"3331\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 827</text>\r\n<text text-anchor=\"middle\" x=\"3331\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [715, 112]</text>\r\n<text text-anchor=\"middle\" x=\"3331\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 58&#45;&gt;60 -->\r\n<g id=\"edge60\" class=\"edge\">\r\n<title>58&#45;&gt;60</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3287.9,-222.91C3292.81,-214.2 3298.06,-204.9 3303.14,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3306.28,-197.45 3308.14,-187.02 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3353.33,-86.28 3356.61,-77.73\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3359.91,-78.89 3360.23,-68.3 3353.38,-76.38 3359.91,-78.89\"/>\r\n</g>\r\n<!-- 64 -->\r\n<g id=\"node65\" class=\"node\">\r\n<title>64</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3771,-306 3643,-306 3643,-223 3771,-223 3771,-306\"/>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 4.207</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.181</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4104</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3992, 112]</text>\r\n<text text-anchor=\"middle\" x=\"3707\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 63&#45;&gt;64 -->\r\n<g id=\"edge64\" class=\"edge\">\r\n<title>63&#45;&gt;64</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3707,-341.91C3707,-333.65 3707,-324.86 3707,-316.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3710.5,-316.02 3707,-306.02 3703.5,-316.02 3710.5,-316.02\"/>\r\n</g>\r\n<!-- 71 -->\r\n<g id=\"node72\" class=\"node\">\r\n<title>71</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4141,-306 4023,-306 4023,-223 4141,-223 4141,-306\"/>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 355.5</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.035</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9805</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9769, 36]</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 63&#45;&gt;71 -->\r\n<g id=\"edge71\" class=\"edge\">\r\n<title>63&#45;&gt;71</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3772.75,-361.99C3840.12,-340.97 3944.44,-308.42 4012.97,-287.04\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4014.32,-290.28 4022.82,-283.96 4012.23,-283.6 4014.32,-290.28\"/>\r\n</g>\r\n<!-- 65 -->\r\n<g id=\"node66\" class=\"node\">\r\n<title>65</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3700,-187 3582,-187 3582,-104 3700,-104 3700,-187\"/>\r\n<text text-anchor=\"middle\" x=\"3641\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 222.5</text>\r\n<text text-anchor=\"middle\" x=\"3641\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.146</text>\r\n<text text-anchor=\"middle\" x=\"3641\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4051</text>\r\n<text text-anchor=\"middle\" x=\"3641\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3967, 84]</text>\r\n<text text-anchor=\"middle\" x=\"3641\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 64&#45;&gt;65 -->\r\n<g id=\"edge65\" class=\"edge\">\r\n<title>64&#45;&gt;65</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3684.1,-222.91C3679.19,-214.2 3673.94,-204.9 3668.86,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3671.82,-194.01 3663.86,-187.02 3665.72,-197.45 3671.82,-194.01\"/>\r\n</g>\r\n<!-- 68 -->\r\n<g id=\"node69\" class=\"node\">\r\n<title>68</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3828,-187 3718,-187 3718,-104 3828,-104 3828,-187\"/>\r\n<text text-anchor=\"middle\" x=\"3773\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 88.0</text>\r\n<text text-anchor=\"middle\" x=\"3773\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.998</text>\r\n<text text-anchor=\"middle\" x=\"3773\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 53</text>\r\n<text text-anchor=\"middle\" x=\"3773\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [25, 28]</text>\r\n<text text-anchor=\"middle\" x=\"3773\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 64&#45;&gt;68 -->\r\n<g id=\"edge68\" class=\"edge\">\r\n<title>64&#45;&gt;68</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3729.9,-222.91C3734.81,-214.2 3740.06,-204.9 3745.14,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3748.28,-197.45 3750.14,-187.02 3742.18,-194.01 3748.28,-197.45\"/>\r\n</g>\r\n<!-- 66 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3772.4,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3775.9,-78.27 3772.3,-68.3 3768.9,-78.33 3775.9,-78.27\"/>\r\n</g>\r\n<!-- 70 -->\r\n<g id=\"node71\" class=\"node\">\r\n<title>70</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"3949,-68 3843,-68 3843,0 3949,0 3949,-68\"/>\r\n<text text-anchor=\"middle\" x=\"3896\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.868</text>\r\n<text text-anchor=\"middle\" x=\"3896\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 38</text>\r\n<text text-anchor=\"middle\" x=\"3896\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [11, 27]</text>\r\n<text text-anchor=\"middle\" x=\"3896\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 68&#45;&gt;70 -->\r\n<g id=\"edge70\" class=\"edge\">\r\n<title>68&#45;&gt;70</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M3818.8,-103.73C3829.35,-94.33 3840.57,-84.35 3851.08,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"3853.45,-77.56 3858.59,-68.3 3848.8,-72.33 3853.45,-77.56\"/>\r\n</g>\r\n<!-- 72 -->\r\n<g id=\"node73\" class=\"node\">\r\n<title>72</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4140.5,-187 4023.5,-187 4023.5,-104 4140.5,-104 4140.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 181.5</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.008</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 8943</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [8937, 6]</text>\r\n<text text-anchor=\"middle\" x=\"4082\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 71&#45;&gt;72 -->\r\n<g id=\"edge72\" class=\"edge\">\r\n<title>71&#45;&gt;72</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4082,-222.91C4082,-214.65 4082,-205.86 4082,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4085.5,-197.02 4082,-187.02 4078.5,-197.02 4085.5,-197.02\"/>\r\n</g>\r\n<!-- 75 -->\r\n<g id=\"node76\" class=\"node\">\r\n<title>75</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4345,-187 4217,-187 4217,-104 4345,-104 4345,-187\"/>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 4.965</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.218</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 862</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [832, 30]</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 71&#45;&gt;75 -->\r\n<g id=\"edge75\" class=\"edge\">\r\n<title>71&#45;&gt;75</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4141.17,-228.71C4162.21,-216.34 4186.14,-202.27 4208.19,-189.31\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4210.01,-192.3 4216.85,-184.21 4206.46,-186.27 4210.01,-192.3\"/>\r\n</g>\r\n<!-- 73 -->\r\n<g id=\"node74\" class=\"node\">\r\n<title>73</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4078.5,-68 3967.5,-68 3967.5,0 4078.5,0 4078.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4023\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"4023\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 5931</text>\r\n<text text-anchor=\"middle\" x=\"4023\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5931, 0]</text>\r\n<text text-anchor=\"middle\" x=\"4023\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 72&#45;&gt;73 -->\r\n<g id=\"edge73\" class=\"edge\">\r\n<title>72&#45;&gt;73</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4060.03,-103.73C4055.36,-95.06 4050.42,-85.9 4045.73,-77.18\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4048.77,-75.44 4040.94,-68.3 4042.6,-78.76 4048.77,-75.44\"/>\r\n</g>\r\n<!-- 74 -->\r\n<g id=\"node75\" class=\"node\">\r\n<title>74</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4207.5,-68 4096.5,-68 4096.5,0 4207.5,0 4207.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4152\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.021</text>\r\n<text text-anchor=\"middle\" x=\"4152\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3012</text>\r\n<text text-anchor=\"middle\" x=\"4152\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3006, 6]</text>\r\n<text text-anchor=\"middle\" x=\"4152\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 72&#45;&gt;74 -->\r\n<g id=\"edge74\" class=\"edge\">\r\n<title>72&#45;&gt;74</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4108.07,-103.73C4113.66,-94.97 4119.59,-85.7 4125.21,-76.91\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4128.28,-78.61 4130.71,-68.3 4122.38,-74.84 4128.28,-78.61\"/>\r\n</g>\r\n<!-- 76 -->\r\n<g id=\"node77\" class=\"node\">\r\n<title>76</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4336.5,-68 4225.5,-68 4225.5,0 4336.5,0 4336.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.151</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 735</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [719, 16]</text>\r\n<text text-anchor=\"middle\" x=\"4281\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 75&#45;&gt;76 -->\r\n<g id=\"edge76\" class=\"edge\">\r\n<title>75&#45;&gt;76</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4281,-103.73C4281,-95.52 4281,-86.86 4281,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4284.5,-78.3 4281,-68.3 4277.5,-78.3 4284.5,-78.3\"/>\r\n</g>\r\n<!-- 77 -->\r\n<g id=\"node78\" class=\"node\">\r\n<title>77</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4465.5,-68 4354.5,-68 4354.5,0 4465.5,0 4465.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4410\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.501</text>\r\n<text text-anchor=\"middle\" x=\"4410\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 127</text>\r\n<text text-anchor=\"middle\" x=\"4410\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [113, 14]</text>\r\n<text text-anchor=\"middle\" x=\"4410\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 75&#45;&gt;77 -->\r\n<g id=\"edge77\" class=\"edge\">\r\n<title>75&#45;&gt;77</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4329.03,-103.73C4340.1,-94.33 4351.86,-84.35 4362.89,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4365.41,-77.44 4370.77,-68.3 4360.88,-72.1 4365.41,-77.44\"/>\r\n</g>\r\n<!-- 79 -->\r\n<g id=\"node80\" class=\"node\">\r\n<title>79</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5088,-425 4960,-425 4960,-342 5088,-342 5088,-425\"/>\r\n<text text-anchor=\"middle\" x=\"5024\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 1.402</text>\r\n<text text-anchor=\"middle\" x=\"5024\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.839</text>\r\n<text text-anchor=\"middle\" x=\"5024\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1445</text>\r\n<text text-anchor=\"middle\" x=\"5024\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1057, 388]</text>\r\n<text text-anchor=\"middle\" x=\"5024\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 78&#45;&gt;79 -->\r\n<g id=\"edge79\" class=\"edge\">\r\n<title>78&#45;&gt;79</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5024,-460.91C5024,-452.65 5024,-443.86 5024,-435.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5027.5,-435.02 5024,-425.02 5020.5,-435.02 5027.5,-435.02\"/>\r\n</g>\r\n<!-- 94 -->\r\n<g id=\"node95\" class=\"node\">\r\n<title>94</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5785,-425 5661,-425 5661,-342 5785,-342 5785,-425\"/>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 1206.5</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.996</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-379.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 837</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-364.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [386, 451]</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 78&#45;&gt;94 -->\r\n<g id=\"edge94\" class=\"edge\">\r\n<title>78&#45;&gt;94</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5086.65,-491.01C5216.35,-469.3 5513.63,-419.54 5650.99,-396.55\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5651.71,-399.98 5661,-394.88 5650.55,-393.08 5651.71,-399.98\"/>\r\n</g>\r\n<!-- 80 -->\r\n<g id=\"node81\" class=\"node\">\r\n<title>80</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4920,-306 4792,-306 4792,-223 4920,-223 4920,-306\"/>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 1.349</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.995</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 290</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [157, 133]</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 79&#45;&gt;80 -->\r\n<g id=\"edge80\" class=\"edge\">\r\n<title>79&#45;&gt;80</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4965.71,-341.91C4951.77,-332.2 4936.78,-321.76 4922.5,-311.81\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4924.39,-308.86 4914.18,-306.02 4920.39,-314.61 4924.39,-308.86\"/>\r\n</g>\r\n<!-- 87 -->\r\n<g id=\"node88\" class=\"node\">\r\n<title>87</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5145,-306 5027,-306 5027,-223 5145,-223 5145,-306\"/>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 628.5</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.762</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1155</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [900, 255]</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 79&#45;&gt;87 -->\r\n<g id=\"edge87\" class=\"edge\">\r\n<title>79&#45;&gt;87</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5045.51,-341.91C5050.13,-333.2 5055.05,-323.9 5059.83,-314.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5062.94,-316.5 5064.53,-306.02 5056.75,-313.22 5062.94,-316.5\"/>\r\n</g>\r\n<!-- 81 -->\r\n<g id=\"node82\" class=\"node\">\r\n<title>81</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4725,-187 4607,-187 4607,-104 4725,-104 4725,-187\"/>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">failure &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.975</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 253</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [150, 103]</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 80&#45;&gt;81 -->\r\n<g id=\"edge81\" class=\"edge\">\r\n<title>80&#45;&gt;81</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4791.94,-224.05C4773.25,-212.55 4752.78,-199.94 4733.87,-188.29\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4735.65,-185.28 4725.3,-183.02 4731.98,-191.24 4735.65,-185.28\"/>\r\n</g>\r\n<!-- 84 -->\r\n<g id=\"node85\" class=\"node\">\r\n<title>84</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4914.5,-187 4797.5,-187 4797.5,-104 4914.5,-104 4914.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 589.0</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.7</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 37</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 30]</text>\r\n<text text-anchor=\"middle\" x=\"4856\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 80&#45;&gt;84 -->\r\n<g id=\"edge84\" class=\"edge\">\r\n<title>80&#45;&gt;84</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4856,-222.91C4856,-214.65 4856,-205.86 4856,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4859.5,-197.02 4856,-187.02 4852.5,-197.02 4859.5,-197.02\"/>\r\n</g>\r\n<!-- 82 -->\r\n<g id=\"node83\" class=\"node\">\r\n<title>82</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4594.5,-68 4483.5,-68 4483.5,0 4594.5,0 4594.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4539\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.998</text>\r\n<text text-anchor=\"middle\" x=\"4539\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 189</text>\r\n<text text-anchor=\"middle\" x=\"4539\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [100, 89]</text>\r\n<text text-anchor=\"middle\" x=\"4539\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 81&#45;&gt;82 -->\r\n<g id=\"edge82\" class=\"edge\">\r\n<title>81&#45;&gt;82</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4618.71,-103.73C4607.81,-94.33 4596.24,-84.35 4585.38,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4587.48,-72.18 4577.62,-68.3 4582.91,-77.48 4587.48,-72.18\"/>\r\n</g>\r\n<!-- 83 -->\r\n<g id=\"node84\" class=\"node\">\r\n<title>83</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4719,-68 4613,-68 4613,0 4719,0 4719,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.758</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 64</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [50, 14]</text>\r\n<text text-anchor=\"middle\" x=\"4666\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 81&#45;&gt;83 -->\r\n<g id=\"edge83\" class=\"edge\">\r\n<title>81&#45;&gt;83</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4666,-103.73C4666,-95.52 4666,-86.86 4666,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4669.5,-78.3 4666,-68.3 4662.5,-78.3 4669.5,-78.3\"/>\r\n</g>\r\n<!-- 85 -->\r\n<g id=\"node86\" class=\"node\">\r\n<title>85</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4843,-68 4737,-68 4737,0 4843,0 4843,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4790\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.934</text>\r\n<text text-anchor=\"middle\" x=\"4790\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 20</text>\r\n<text text-anchor=\"middle\" x=\"4790\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 13]</text>\r\n<text text-anchor=\"middle\" x=\"4790\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 84&#45;&gt;85 -->\r\n<g id=\"edge85\" class=\"edge\">\r\n<title>84&#45;&gt;85</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4831.42,-103.73C4826.15,-94.97 4820.56,-85.7 4815.26,-76.91\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4818.23,-75.06 4810.07,-68.3 4812.24,-78.67 4818.23,-75.06\"/>\r\n</g>\r\n<!-- 86 -->\r\n<g id=\"node87\" class=\"node\">\r\n<title>86</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"4959,-68 4861,-68 4861,0 4959,0 4959,-68\"/>\r\n<text text-anchor=\"middle\" x=\"4910\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"4910\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n<text text-anchor=\"middle\" x=\"4910\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 17]</text>\r\n<text text-anchor=\"middle\" x=\"4910\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 84&#45;&gt;86 -->\r\n<g id=\"edge86\" class=\"edge\">\r\n<title>84&#45;&gt;86</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M4876.11,-103.73C4880.34,-95.15 4884.81,-86.09 4889.06,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"4892.29,-78.82 4893.58,-68.3 4886.02,-75.72 4892.29,-78.82\"/>\r\n</g>\r\n<!-- 88 -->\r\n<g id=\"node89\" class=\"node\">\r\n<title>88</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5165,-187 5007,-187 5007,-104 5165,-104 5165,-187\"/>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">cons.price.idx &lt;= 93.956</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.673</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 758</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [624, 134]</text>\r\n<text text-anchor=\"middle\" x=\"5086\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 87&#45;&gt;88 -->\r\n<g id=\"edge88\" class=\"edge\">\r\n<title>87&#45;&gt;88</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5086,-222.91C5086,-214.65 5086,-205.86 5086,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5089.5,-197.02 5086,-187.02 5082.5,-197.02 5089.5,-197.02\"/>\r\n</g>\r\n<!-- 91 -->\r\n<g id=\"node92\" class=\"node\">\r\n<title>91</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5354,-187 5236,-187 5236,-104 5354,-104 5354,-187\"/>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 767.5</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.887</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 397</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [276, 121]</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 87&#45;&gt;91 -->\r\n<g id=\"edge91\" class=\"edge\">\r\n<title>87&#45;&gt;91</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5145.05,-230.44C5170.54,-216.17 5200.5,-199.4 5226.92,-184.61\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5228.71,-187.62 5235.73,-179.68 5225.3,-181.51 5228.71,-187.62\"/>\r\n</g>\r\n<!-- 89 -->\r\n<g id=\"node90\" class=\"node\">\r\n<title>89</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5088.5,-68 4977.5,-68 4977.5,0 5088.5,0 5088.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5033\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.753</text>\r\n<text text-anchor=\"middle\" x=\"5033\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 454</text>\r\n<text text-anchor=\"middle\" x=\"5033\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [356, 98]</text>\r\n<text text-anchor=\"middle\" x=\"5033\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 88&#45;&gt;89 -->\r\n<g id=\"edge89\" class=\"edge\">\r\n<title>88&#45;&gt;89</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5066.26,-103.73C5062.11,-95.15 5057.73,-86.09 5053.55,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5056.62,-75.78 5049.12,-68.3 5050.32,-78.83 5056.62,-75.78\"/>\r\n</g>\r\n<!-- 90 -->\r\n<g id=\"node91\" class=\"node\">\r\n<title>90</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5217.5,-68 5106.5,-68 5106.5,0 5217.5,0 5217.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5162\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.525</text>\r\n<text text-anchor=\"middle\" x=\"5162\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 304</text>\r\n<text text-anchor=\"middle\" x=\"5162\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [268, 36]</text>\r\n<text text-anchor=\"middle\" x=\"5162\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 88&#45;&gt;90 -->\r\n<g id=\"edge90\" class=\"edge\">\r\n<title>88&#45;&gt;90</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5114.3,-103.73C5120.44,-94.88 5126.94,-85.51 5133.11,-76.63\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5136.06,-78.51 5138.89,-68.3 5130.31,-74.52 5136.06,-78.51\"/>\r\n</g>\r\n<!-- 92 -->\r\n<g id=\"node93\" class=\"node\">\r\n<title>92</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5354,-68 5236,-68 5236,0 5354,0 5354,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.895</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 388</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [267, 121]</text>\r\n<text text-anchor=\"middle\" x=\"5295\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 91&#45;&gt;92 -->\r\n<g id=\"edge92\" class=\"edge\">\r\n<title>91&#45;&gt;92</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5295,-103.73C5295,-95.52 5295,-86.86 5295,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5298.5,-78.3 5295,-68.3 5291.5,-78.3 5298.5,-78.3\"/>\r\n</g>\r\n<!-- 93 -->\r\n<g id=\"node94\" class=\"node\">\r\n<title>93</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5464,-68 5372,-68 5372,0 5464,0 5464,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5418\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"5418\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n<text text-anchor=\"middle\" x=\"5418\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [9, 0]</text>\r\n<text text-anchor=\"middle\" x=\"5418\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 91&#45;&gt;93 -->\r\n<g id=\"edge93\" class=\"edge\">\r\n<title>91&#45;&gt;93</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5340.8,-103.73C5351.35,-94.33 5362.57,-84.35 5373.08,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5375.45,-77.56 5380.59,-68.3 5370.8,-72.33 5375.45,-77.56\"/>\r\n</g>\r\n<!-- 95 -->\r\n<g id=\"node96\" class=\"node\">\r\n<title>95</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5787,-306 5659,-306 5659,-223 5787,-223 5787,-306\"/>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 1.385</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.0</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 643</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [320, 323]</text>\r\n<text text-anchor=\"middle\" x=\"5723\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 94&#45;&gt;95 -->\r\n<g id=\"edge95\" class=\"edge\">\r\n<title>94&#45;&gt;95</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5723,-341.91C5723,-333.65 5723,-324.86 5723,-316.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5726.5,-316.02 5723,-306.02 5719.5,-316.02 5726.5,-316.02\"/>\r\n</g>\r\n<!-- 102 -->\r\n<g id=\"node103\" class=\"node\">\r\n<title>102</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6148.5,-306 6037.5,-306 6037.5,-223 6148.5,-223 6148.5,-306\"/>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-290.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">admin. &lt;= 0.5</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-275.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.925</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-260.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 194</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [66, 128]</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 94&#45;&gt;102 -->\r\n<g id=\"edge102\" class=\"edge\">\r\n<title>94&#45;&gt;102</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5785.35,-362.78C5852.58,-341.53 5959.15,-307.83 6027.56,-286.19\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6028.88,-289.45 6037.36,-283.09 6026.77,-282.77 6028.88,-289.45\"/>\r\n</g>\r\n<!-- 96 -->\r\n<g id=\"node97\" class=\"node\">\r\n<title>96</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5711.5,-187 5600.5,-187 5600.5,-104 5711.5,-104 5711.5,-187\"/>\r\n<text text-anchor=\"middle\" x=\"5656\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">campaign &lt;= 4.5</text>\r\n<text text-anchor=\"middle\" x=\"5656\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.91</text>\r\n<text text-anchor=\"middle\" x=\"5656\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 117</text>\r\n<text text-anchor=\"middle\" x=\"5656\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [38, 79]</text>\r\n<text text-anchor=\"middle\" x=\"5656\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 95&#45;&gt;96 -->\r\n<g id=\"edge96\" class=\"edge\">\r\n<title>95&#45;&gt;96</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5699.75,-222.91C5694.77,-214.2 5689.44,-204.9 5684.29,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5687.21,-193.96 5679.2,-187.02 5681.14,-197.44 5687.21,-193.96\"/>\r\n</g>\r\n<!-- 99 -->\r\n<g id=\"node100\" class=\"node\">\r\n<title>99</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5848,-187 5730,-187 5730,-104 5848,-104 5848,-187\"/>\r\n<text text-anchor=\"middle\" x=\"5789\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">month &lt;= 5.5</text>\r\n<text text-anchor=\"middle\" x=\"5789\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.996</text>\r\n<text text-anchor=\"middle\" x=\"5789\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 526</text>\r\n<text text-anchor=\"middle\" x=\"5789\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [282, 244]</text>\r\n<text text-anchor=\"middle\" x=\"5789\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 95&#45;&gt;99 -->\r\n<g id=\"edge99\" class=\"edge\">\r\n<title>95&#45;&gt;99</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5745.9,-222.91C5750.81,-214.2 5756.06,-204.9 5761.14,-195.89\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5764.28,-197.45 5766.14,-187.02 5758.18,-194.01 5764.28,-197.45\"/>\r\n</g>\r\n<!-- 97 -->\r\n<g id=\"node98\" class=\"node\">\r\n<title>97</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5588,-68 5482,-68 5482,0 5588,0 5588,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5535\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.873</text>\r\n<text text-anchor=\"middle\" x=\"5535\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 109</text>\r\n<text text-anchor=\"middle\" x=\"5535\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [32, 77]</text>\r\n<text text-anchor=\"middle\" x=\"5535\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 96&#45;&gt;97 -->\r\n<g id=\"edge97\" class=\"edge\">\r\n<title>96&#45;&gt;97</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5610.94,-103.73C5600.66,-94.42 5589.75,-84.54 5579.49,-75.26\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5581.56,-72.41 5571.8,-68.3 5576.86,-77.6 5581.56,-72.41\"/>\r\n</g>\r\n<!-- 98 -->\r\n<g id=\"node99\" class=\"node\">\r\n<title>98</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5712,-68 5606,-68 5606,0 5712,0 5712,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5659\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.811</text>\r\n<text text-anchor=\"middle\" x=\"5659\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 8</text>\r\n<text text-anchor=\"middle\" x=\"5659\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [6, 2]</text>\r\n<text text-anchor=\"middle\" x=\"5659\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 96&#45;&gt;98 -->\r\n<g id=\"edge98\" class=\"edge\">\r\n<title>96&#45;&gt;98</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5657.12,-103.73C5657.34,-95.52 5657.58,-86.86 5657.81,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5661.31,-78.39 5658.09,-68.3 5654.32,-78.2 5661.31,-78.39\"/>\r\n</g>\r\n<!-- 100 -->\r\n<g id=\"node101\" class=\"node\">\r\n<title>100</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5841.5,-68 5730.5,-68 5730.5,0 5841.5,0 5841.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5786\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.933</text>\r\n<text text-anchor=\"middle\" x=\"5786\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 172</text>\r\n<text text-anchor=\"middle\" x=\"5786\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [112, 60]</text>\r\n<text text-anchor=\"middle\" x=\"5786\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 99&#45;&gt;100 -->\r\n<g id=\"edge100\" class=\"edge\">\r\n<title>99&#45;&gt;100</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5787.88,-103.73C5787.66,-95.52 5787.42,-86.86 5787.19,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5790.68,-78.2 5786.91,-68.3 5783.69,-78.39 5790.68,-78.2\"/>\r\n</g>\r\n<!-- 101 -->\r\n<g id=\"node102\" class=\"node\">\r\n<title>101</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"5978,-68 5860,-68 5860,0 5978,0 5978,-68\"/>\r\n<text text-anchor=\"middle\" x=\"5919\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.999</text>\r\n<text text-anchor=\"middle\" x=\"5919\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 354</text>\r\n<text text-anchor=\"middle\" x=\"5919\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [170, 184]</text>\r\n<text text-anchor=\"middle\" x=\"5919\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 99&#45;&gt;101 -->\r\n<g id=\"edge101\" class=\"edge\">\r\n<title>99&#45;&gt;101</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M5837.41,-103.73C5848.56,-94.33 5860.41,-84.35 5871.53,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"5874.07,-77.42 5879.47,-68.3 5869.56,-72.06 5874.07,-77.42\"/>\r\n</g>\r\n<!-- 103 -->\r\n<g id=\"node104\" class=\"node\">\r\n<title>103</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6157,-187 6029,-187 6029,-104 6157,-104 6157,-187\"/>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">euribor3m &lt;= 1.274</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.855</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 143</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [40, 103]</text>\r\n<text text-anchor=\"middle\" x=\"6093\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 102&#45;&gt;103 -->\r\n<g id=\"edge103\" class=\"edge\">\r\n<title>102&#45;&gt;103</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6093,-222.91C6093,-214.65 6093,-205.86 6093,-197.3\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6096.5,-197.02 6093,-187.02 6089.5,-197.02 6096.5,-197.02\"/>\r\n</g>\r\n<!-- 106 -->\r\n<g id=\"node107\" class=\"node\">\r\n<title>106</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6351,-187 6227,-187 6227,-104 6351,-104 6351,-187\"/>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-171.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">duration &lt;= 1238.0</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-156.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 1.0</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 51</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [26, 25]</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 102&#45;&gt;106 -->\r\n<g id=\"edge106\" class=\"edge\">\r\n<title>102&#45;&gt;106</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6148.64,-230.29C6170.22,-217.41 6195.19,-202.5 6218.03,-188.87\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6219.88,-191.84 6226.67,-183.71 6216.29,-185.83 6219.88,-191.84\"/>\r\n</g>\r\n<!-- 104 -->\r\n<g id=\"node105\" class=\"node\">\r\n<title>104</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6088,-68 5996,-68 5996,0 6088,0 6088,-68\"/>\r\n<text text-anchor=\"middle\" x=\"6042\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n<text text-anchor=\"middle\" x=\"6042\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n<text text-anchor=\"middle\" x=\"6042\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [4, 0]</text>\r\n<text text-anchor=\"middle\" x=\"6042\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 103&#45;&gt;104 -->\r\n<g id=\"edge104\" class=\"edge\">\r\n<title>103&#45;&gt;104</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6074.01,-103.73C6070.02,-95.15 6065.79,-86.09 6061.77,-77.46\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6064.9,-75.89 6057.51,-68.3 6058.56,-78.84 6064.9,-75.89\"/>\r\n</g>\r\n<!-- 105 -->\r\n<g id=\"node106\" class=\"node\">\r\n<title>105</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6217.5,-68 6106.5,-68 6106.5,0 6217.5,0 6217.5,-68\"/>\r\n<text text-anchor=\"middle\" x=\"6162\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.825</text>\r\n<text text-anchor=\"middle\" x=\"6162\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 139</text>\r\n<text text-anchor=\"middle\" x=\"6162\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [36, 103]</text>\r\n<text text-anchor=\"middle\" x=\"6162\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 103&#45;&gt;105 -->\r\n<g id=\"edge105\" class=\"edge\">\r\n<title>103&#45;&gt;105</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6118.69,-103.73C6124.21,-94.97 6130.05,-85.7 6135.59,-76.91\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6138.65,-78.63 6141.02,-68.3 6132.72,-74.89 6138.65,-78.63\"/>\r\n</g>\r\n<!-- 107 -->\r\n<g id=\"node108\" class=\"node\">\r\n<title>107</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6342,-68 6236,-68 6236,0 6342,0 6342,-68\"/>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.544</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 8</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 7]</text>\r\n<text text-anchor=\"middle\" x=\"6289\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 1</text>\r\n</g>\r\n<!-- 106&#45;&gt;107 -->\r\n<g id=\"edge107\" class=\"edge\">\r\n<title>106&#45;&gt;107</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6289,-103.73C6289,-95.52 6289,-86.86 6289,-78.56\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6292.5,-78.3 6289,-68.3 6285.5,-78.3 6292.5,-78.3\"/>\r\n</g>\r\n<!-- 108 -->\r\n<g id=\"node109\" class=\"node\">\r\n<title>108</title>\r\n<polygon fill=\"none\" stroke=\"black\" points=\"6466,-68 6360,-68 6360,0 6466,0 6466,-68\"/>\r\n<text text-anchor=\"middle\" x=\"6413\" y=\"-52.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.981</text>\r\n<text text-anchor=\"middle\" x=\"6413\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 43</text>\r\n<text text-anchor=\"middle\" x=\"6413\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [25, 18]</text>\r\n<text text-anchor=\"middle\" x=\"6413\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">class = 0</text>\r\n</g>\r\n<!-- 106&#45;&gt;108 -->\r\n<g id=\"edge108\" class=\"edge\">\r\n<title>106&#45;&gt;108</title>\r\n<path fill=\"none\" stroke=\"black\" d=\"M6335.17,-103.73C6345.81,-94.33 6357.12,-84.35 6367.72,-74.99\"/>\r\n<polygon fill=\"black\" stroke=\"black\" points=\"6370.11,-77.54 6375.29,-68.3 6365.48,-72.3 6370.11,-77.54\"/>\r\n</g>\r\n</g>\r\n</svg>\r\n",
      "text/plain": [
       "<graphviz.files.Source at 0x1de925ca670>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = tree.DecisionTreeClassifier(criterion=\"entropy\",\n",
    "                                 max_depth=6)\n",
    "dt = dt.fit(X_train, \n",
    "               y_train)\n",
    "g = tree.export_graphviz(dt, \n",
    "                        feature_names=X_train.columns,\n",
    "                        class_names=['0', '1']) \n",
    "graph = graphviz.Source(g)\n",
    "graph"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "96b30c1f",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0338e395",
   "metadata": {},
   "source": [
    "This technique looks at the training vectors and determines the k nearest neighbours which specifies the class. The majority of neighbours being a single class will determine the class of the new vector. The k-d tree algorithm builds a tree from the training data by splitting the data at the median values. \n",
    "The number of neighbours fit appropriately at 3. Through experimentation, this was to determine to be the best number of neighbours. Any less and it generalised too much and any more neighbours and it becomes overfit. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "50b78bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:179: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='kd_tree', n_neighbors=3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_neighbors = 3\n",
    "\n",
    "algorithms = ['brute', 'kd_tree']\n",
    "\n",
    "knn = knn(algorithm = algorithms[1],\n",
    "        n_neighbors=n_neighbors)\n",
    "\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ab01d2cc",
   "metadata": {},
   "source": [
    "### RF"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "09f03fde",
   "metadata": {},
   "source": [
    "The random forest technique is essentially where multiple decision trees are used to improve the generalisation. The first step is to perform bootstrapping, where we randomly sample some rows into a set number of groups. Next, only a select few features are used on these groups to apply a decision tree to each group. Aggregation is then applied where an instance is passed into the trees and the majority output is our prediction. \n",
    "The parameters set were a criterion of ‘entropy’, for the same reasons as the DT; as well as setting the max-depth to unlimited. The risk in unlimiting the depth is overfitting, this was not happening, however. The accuracy of the training set, testing set and Kaggle submission score all increased with the depth. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bdd647b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\CL-11\\AppData\\Local\\Temp/ipykernel_30288/2654058757.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(X_train, y_train)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=20)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = rf(criterion = \"entropy\",\n",
    "       max_depth=20)\n",
    "\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c8622898",
   "metadata": {},
   "source": [
    "### SVR"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "01f43ce8",
   "metadata": {},
   "source": [
    "This approach is almost as simple as the KNN approach. Here, the classifier determines a line of best fit known as the hyperplane. SVC looks at the closes points in each class to each other, known as the support vectors then finds hyperplane which gives the maximum margin from both support vectors. Whichever side the data point lies on will determine the class. \n",
    "\n",
    "The parameters for the classification included the regularisation parameter (C) and the kernel. The regularisation parameter was set to 50- this high value punishes the model severely for misclassification. This causes the model to choose smaller margins. The potential kernels include linear, polynomial, RBF, sigmoid and precomputed. The kernel used was linear. Through experimentation, sigmoid and linear provided the best results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "744a25e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = svc(kernel = 'linear',\n",
    "         C = 1,\n",
    "         gamma='scale')\n",
    "\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6d275870",
   "metadata": {},
   "source": [
    "### NN"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "57784791",
   "metadata": {},
   "source": [
    "Using tensorflow, implementing a feed-forward neural network is trivial. \n",
    "\n",
    "Firstly, the training data must be normalised. This can be done with the StandardScaler function. \n",
    "\n",
    "Then, choosing the size of the neural network. Here, it consists of 1 input layer, 1 output layer and 4 hidden layers. Its loss function is set with binary cross entropy and measued with accuracy metric. The activation functions are ReLu with sigmoid for the output layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "03d10f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train, X_test = scaler.transform(X_train), scaler.transform(X_test)\n",
    "y_train, y_test = y_train.values, y_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d4df509",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "551/551 [==============================] - 1s 828us/step - loss: 0.2831 - accuracy: 0.8900 - val_loss: 0.2369 - val_accuracy: 0.8842\n",
      "Epoch 2/50\n",
      "551/551 [==============================] - 0s 578us/step - loss: 0.2084 - accuracy: 0.9015 - val_loss: 0.2233 - val_accuracy: 0.9041\n",
      "Epoch 3/50\n",
      "551/551 [==============================] - 0s 578us/step - loss: 0.2004 - accuracy: 0.9093 - val_loss: 0.2178 - val_accuracy: 0.9041\n",
      "Epoch 4/50\n",
      "551/551 [==============================] - 0s 573us/step - loss: 0.1963 - accuracy: 0.9097 - val_loss: 0.2146 - val_accuracy: 0.9046\n",
      "Epoch 5/50\n",
      "551/551 [==============================] - 0s 597us/step - loss: 0.1937 - accuracy: 0.9121 - val_loss: 0.2139 - val_accuracy: 0.9051\n",
      "Epoch 6/50\n",
      "551/551 [==============================] - 0s 586us/step - loss: 0.1915 - accuracy: 0.9124 - val_loss: 0.2137 - val_accuracy: 0.9077\n",
      "Epoch 7/50\n",
      "551/551 [==============================] - 0s 669us/step - loss: 0.1902 - accuracy: 0.9123 - val_loss: 0.2158 - val_accuracy: 0.9071\n",
      "Epoch 8/50\n",
      "551/551 [==============================] - 0s 617us/step - loss: 0.1889 - accuracy: 0.9129 - val_loss: 0.2116 - val_accuracy: 0.9046\n",
      "Epoch 9/50\n",
      "551/551 [==============================] - 0s 581us/step - loss: 0.1878 - accuracy: 0.9128 - val_loss: 0.2125 - val_accuracy: 0.9082\n",
      "Epoch 10/50\n",
      "551/551 [==============================] - 0s 874us/step - loss: 0.1868 - accuracy: 0.9127 - val_loss: 0.2149 - val_accuracy: 0.9051\n",
      "Epoch 11/50\n",
      "551/551 [==============================] - 0s 655us/step - loss: 0.1860 - accuracy: 0.9134 - val_loss: 0.2152 - val_accuracy: 0.9087\n",
      "Epoch 12/50\n",
      "551/551 [==============================] - 0s 575us/step - loss: 0.1853 - accuracy: 0.9136 - val_loss: 0.2134 - val_accuracy: 0.9056\n",
      "Epoch 13/50\n",
      "551/551 [==============================] - 0s 631us/step - loss: 0.1846 - accuracy: 0.9136 - val_loss: 0.2120 - val_accuracy: 0.9061\n",
      "Epoch 14/50\n",
      "551/551 [==============================] - 0s 593us/step - loss: 0.1838 - accuracy: 0.9154 - val_loss: 0.2120 - val_accuracy: 0.9071\n",
      "Epoch 15/50\n",
      "551/551 [==============================] - 0s 566us/step - loss: 0.1830 - accuracy: 0.9152 - val_loss: 0.2162 - val_accuracy: 0.9051\n",
      "Epoch 16/50\n",
      "551/551 [==============================] - 0s 649us/step - loss: 0.1821 - accuracy: 0.9157 - val_loss: 0.2149 - val_accuracy: 0.9041\n",
      "Epoch 17/50\n",
      "551/551 [==============================] - 0s 733us/step - loss: 0.1821 - accuracy: 0.9145 - val_loss: 0.2175 - val_accuracy: 0.9092\n",
      "Epoch 18/50\n",
      "551/551 [==============================] - 0s 575us/step - loss: 0.1811 - accuracy: 0.9152 - val_loss: 0.2159 - val_accuracy: 0.9077\n",
      "Epoch 19/50\n",
      "551/551 [==============================] - 1s 917us/step - loss: 0.1804 - accuracy: 0.9151 - val_loss: 0.2145 - val_accuracy: 0.9066\n",
      "Epoch 20/50\n",
      "551/551 [==============================] - 0s 782us/step - loss: 0.1803 - accuracy: 0.9152 - val_loss: 0.2147 - val_accuracy: 0.9077\n",
      "Epoch 21/50\n",
      "551/551 [==============================] - 1s 919us/step - loss: 0.1790 - accuracy: 0.9159 - val_loss: 0.2086 - val_accuracy: 0.9061\n",
      "Epoch 22/50\n",
      "551/551 [==============================] - 0s 571us/step - loss: 0.1787 - accuracy: 0.9163 - val_loss: 0.2116 - val_accuracy: 0.9056\n",
      "Epoch 23/50\n",
      "551/551 [==============================] - 0s 842us/step - loss: 0.1785 - accuracy: 0.9153 - val_loss: 0.2129 - val_accuracy: 0.9061\n",
      "Epoch 24/50\n",
      "551/551 [==============================] - 1s 912us/step - loss: 0.1777 - accuracy: 0.9154 - val_loss: 0.2134 - val_accuracy: 0.9026\n",
      "Epoch 25/50\n",
      "551/551 [==============================] - 0s 711us/step - loss: 0.1769 - accuracy: 0.9166 - val_loss: 0.2096 - val_accuracy: 0.9010\n",
      "Epoch 26/50\n",
      "551/551 [==============================] - 0s 735us/step - loss: 0.1764 - accuracy: 0.9170 - val_loss: 0.2116 - val_accuracy: 0.9056\n",
      "Epoch 27/50\n",
      "551/551 [==============================] - 1s 979us/step - loss: 0.1757 - accuracy: 0.9173 - val_loss: 0.2136 - val_accuracy: 0.9051\n",
      "Epoch 28/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1756 - accuracy: 0.9169 - val_loss: 0.2097 - val_accuracy: 0.9051\n",
      "Epoch 29/50\n",
      "551/551 [==============================] - 0s 622us/step - loss: 0.1750 - accuracy: 0.9170 - val_loss: 0.2116 - val_accuracy: 0.9056\n",
      "Epoch 30/50\n",
      "551/551 [==============================] - 0s 676us/step - loss: 0.1741 - accuracy: 0.9183 - val_loss: 0.2070 - val_accuracy: 0.9051\n",
      "Epoch 31/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1741 - accuracy: 0.9169 - val_loss: 0.2096 - val_accuracy: 0.9056\n",
      "Epoch 32/50\n",
      "551/551 [==============================] - 0s 665us/step - loss: 0.1738 - accuracy: 0.9167 - val_loss: 0.2130 - val_accuracy: 0.9046\n",
      "Epoch 33/50\n",
      "551/551 [==============================] - 0s 646us/step - loss: 0.1730 - accuracy: 0.9179 - val_loss: 0.2119 - val_accuracy: 0.8985\n",
      "Epoch 34/50\n",
      "551/551 [==============================] - 0s 768us/step - loss: 0.1725 - accuracy: 0.9184 - val_loss: 0.2116 - val_accuracy: 0.9056\n",
      "Epoch 35/50\n",
      "551/551 [==============================] - 0s 627us/step - loss: 0.1719 - accuracy: 0.9176 - val_loss: 0.2104 - val_accuracy: 0.9000\n",
      "Epoch 36/50\n",
      "551/551 [==============================] - 1s 964us/step - loss: 0.1712 - accuracy: 0.9191 - val_loss: 0.2075 - val_accuracy: 0.9020\n",
      "Epoch 37/50\n",
      "551/551 [==============================] - 1s 2ms/step - loss: 0.1720 - accuracy: 0.9196 - val_loss: 0.2095 - val_accuracy: 0.9046\n",
      "Epoch 38/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1705 - accuracy: 0.9190 - val_loss: 0.2073 - val_accuracy: 0.9051\n",
      "Epoch 39/50\n",
      "551/551 [==============================] - 0s 716us/step - loss: 0.1698 - accuracy: 0.9187 - val_loss: 0.2102 - val_accuracy: 0.9005\n",
      "Epoch 40/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1700 - accuracy: 0.9200 - val_loss: 0.2080 - val_accuracy: 0.9051\n",
      "Epoch 41/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1691 - accuracy: 0.9188 - val_loss: 0.2093 - val_accuracy: 0.9041\n",
      "Epoch 42/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1691 - accuracy: 0.9201 - val_loss: 0.2140 - val_accuracy: 0.9031\n",
      "Epoch 43/50\n",
      "551/551 [==============================] - 1s 965us/step - loss: 0.1686 - accuracy: 0.9199 - val_loss: 0.2115 - val_accuracy: 0.9061\n",
      "Epoch 44/50\n",
      "551/551 [==============================] - 0s 833us/step - loss: 0.1679 - accuracy: 0.9209 - val_loss: 0.2118 - val_accuracy: 0.9010\n",
      "Epoch 45/50\n",
      "551/551 [==============================] - 1s 2ms/step - loss: 0.1680 - accuracy: 0.9206 - val_loss: 0.2125 - val_accuracy: 0.9020\n",
      "Epoch 46/50\n",
      "551/551 [==============================] - 1s 1ms/step - loss: 0.1675 - accuracy: 0.9194 - val_loss: 0.2134 - val_accuracy: 0.9077\n",
      "Epoch 47/50\n",
      "551/551 [==============================] - 0s 800us/step - loss: 0.1675 - accuracy: 0.9210 - val_loss: 0.2131 - val_accuracy: 0.9056\n",
      "Epoch 48/50\n",
      "551/551 [==============================] - 0s 776us/step - loss: 0.1670 - accuracy: 0.9222 - val_loss: 0.2099 - val_accuracy: 0.9071\n",
      "Epoch 49/50\n",
      "551/551 [==============================] - 0s 786us/step - loss: 0.1665 - accuracy: 0.9214 - val_loss: 0.2123 - val_accuracy: 0.9071\n",
      "Epoch 50/50\n",
      "551/551 [==============================] - 0s 742us/step - loss: 0.1658 - accuracy: 0.9219 - val_loss: 0.2144 - val_accuracy: 0.9061\n"
     ]
    }
   ],
   "source": [
    "fnn_model = Sequential([\n",
    "    InputLayer(X_train.shape[1]),\n",
    "    Dense(16, 'relu'),\n",
    "    Dense(8, 'relu'),\n",
    "    Dense(4, 'relu'),\n",
    "    Dense(2, 'relu'),\n",
    "    Dense(1, 'sigmoid')\n",
    "])\n",
    "\n",
    "opt = Adam(learning_rate=0.01)\n",
    "\n",
    "fnn_model.compile(optimizer='adam', \n",
    "              loss='binary_crossentropy', \n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = fnn_model.fit(x=X_train, \n",
    "          y=y_train, \n",
    "          validation_split=0.1,\n",
    "          batch_size = 32, \n",
    "          epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "06bf13d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3EZgF5GBHaxW7zRgz14NyaCDxg75Pqrp8veEA97z3C/eO7cZ1QzvRPCKk3GNTs/P476JdzFqaROeWEZzTszVjesQyoEPT481exhge+WQD/9uSzMdTRtCxRdlLRE9fuJPn/7eDV284jTnLd7MnNYcPbxtOswpevzGqE4GkrtBA4h99n1R12HIggyteWcrDF5zKjcPjPX7eoQwXn63bx8JtKaxKSiU8JIhR3VoypnssO5KzeHflHj64bTg925Yaq3OSVxbu4l8LttIuJoyPbh9BOx/TmjRkGkjcaCDxj75Pqqodzc7jkpeXMLJrS/55ed8K52FUJCu3gGW7jrBwWzILt6VwNCePtyafzpD45h49/6v1B+jdLpr4lhE+vX5Dp4HEjQYS/+j71HAYY3y+aVdmw950Zi1NYtuhDP48riejupU9Z6OgsIgJb64kt6CId24ZSmhQ1SQmNMaQX2gICdKVMqpKrY/aUrXL35UVVcPzv82HGPTkdyxPOFJl58wrKOKzdfu4fPrPXPryEjJd+Zwe34JJM1fy8LwNZLrySz3nqflbSEjJ5pUbBlVZEAF709MgUnPq7hABpVS1WJmYyp3vrGVQx2bcOns1H98+gm6tPctIW5b0nHxmLU1i7ord5BUWcfWQDjx/zUA6NLed2+MHxvHHD3/l/GmLefqKfpzR3eaP+mD1b8xdsYcPbxte7ybgqZNpIFGqEdm8P4M/vLWKW0Z34cHzuvOnj9YzaeYq5t0xwqckpYezcrnu9eUUOUkIL+kfV2rdjL7tY/j87pG8/MNOJs9axZWntefCvm157JONPH1FX69nhau6R+t+9cTUqVM5//zzT9r22muvMXLkSFavXs3IkSNp1qwZrVq1YtKkSWRmZtZSSVVdtedIDhNnruTi/u2OZ579x+V96RIbwU0zV5GVW+DV+VIyc7n2teW0iAjl87tGcvWQjuUuvhQaFMgD5/Xg0ztHsu63NCa8uZIJwztx+aD2VXFpqpZpIKknrrvuOhYuXMjBgydSis2ZM4cbb7yRwMBAnnnmGZKTk1m3bh2bN2/miSeeqMXSqupSvLSpt5IzXdzwxgqGxDfjyUv7HO9kDw4MYPr1gzDAHXPXku9hPquUTFsTaRkZypuThng8ka5PXAyf3zWK1ycM5s8XnOr1dai6SZu2ylNUCFnJNfNaka0goOKOxjZt2jBmzBjeffdd7r//fhITE1m1ahWfffYZzZufGN7Yrl077rnnHp5//vnqLrWqIQfSj7FoWwoLt6Xw887DxEaFcmHftlzYty0920ZVOvIqw5XPxDdX0b5ZE6ZdPaDUss5RYcHMumkI41/+mUc/2cC/ruhX4TmTM11c9/oKWkWF8sZE75eADQkK4Nxerb16jqrbNJCUJysZptbQJ6YHtkJ020oPu/HGG5k2bRr3338/c+fO5YILLqB58+Zs376dBx98kNWrV5OdnU1RURGxsfVjQRxVWmGRYVVSKj9uS2bRthS2HsykU4twxnSPZerVA9ifdoyvNhzg5YU7iW8RwYV923Bh37b0ahuNK7+I7LwCcnILycotICevgGe+2UZQgPDahMHljoxqHR3GzJtO58pXlxLXNLzcxIHJmS6ufW05bWLCmDGhbq8jrmqOBpLyRLayN/iaei0PjB8/nilTprBlyxbefvttnnrqKQBuv/12evXqxdtvv01MTAxvv/02jz32WHWWWFWxgsIiViam8tWGA3yz6SAZrgKGd2nB1UM6MKZHKzqXmCQ3cUQ8yRkuFmw6yFfrDzB9oV05s2SrV0hgAD3aRDHrpiGVrvvdo00U/73xNCa9uYp5v+ylU4sI4luEE98igviW4bSICOWBD9bRNqYJr08YrEFEHaeBpDwBgR7VEmpSREQE48eP57777uPQoUNcdNFFAGRmZhIVFUV0dDSJiYlMmzatlkuqPGGMYVnCEb5cf4BvNh4kK7eAs3q04q8X9+bsU1tVeuNvFR3GhOHxTBgeT3Kmi/1pLiJDAwkPCSIiJIgmIYFez6UY0bUlC+4bzYZ96SQezmb3kRy+WL+fpMPZHM3JZ9QpLTWIqFI0kNQzN954I+effz633HILoaE2k+nUqVO59dZbeeGFF+jVqxdXXXUVr776ai2XVFXEGMOTX27h7RW7GXtqKx6/xAYPX7PNtooKq7K5GF1iI+kSG1lqe4Yrn6jQoGqbDa/qL02R4tDUH55pzO/TwXQXi7YnszwhlfN7t2FcnzY+naewyPDoJxtYsOkgsyefTr/2Tau2oEpVg4pSpGiNRKly5BUUsWb3URZuP9Hp3bF5OAM7NuXud9dy2xldeeDc7l4tfJRfWMQDH/zKsl1HeP/W4fRo4/uMcqXqCg0kSpVQVGR4Z+Uenvt2G8fyChlWRqf3tad35M65a9l8IIP/XDOA6LDgSs/ryi/krnfWsuVAJh9OGV6qA12p+kqbthyNucnGGzX1PuXkFTBv7T5+2JrMXy7qVWM33U3703n0k40kHcnmoXGnMn5gHGHBZXcs70s7xm1zVpOTV8hrNw7mlFal+xWKZecWcOuc1exPc/H2zUOJ0/UuVD2jaeTdaCDxj7/vU2Wpy/ccyWH2siQ+WP0bEaFBdGgezt7UHD6YMpz2zcpe4a4qZOUWMO277cxamsT4gXE8fMGptIgMrfR5rvxC/vzxer7fksx/rhnA2J52op0xhqzcApIzc0nJzOWZBVvJzi1kzs2na4JCVS9pIHGjgcQ//rxPT3+9lbeWJtGxeTjxLYvnJ0TQqUU4uQVFvL1sNz9sS2ZIfHMmjYjnvF6tj6fu2H4okw9uG+5TYsHyFN/sF28/zJNfbiYyLIi/X9aHYV1aeH2eN5Yk8vTXW+kdF0Nqtg0ernybbiQsOIDTOjXj5esG0TRcl29V9ZMGEjflBZKEhAQ6d+6sQxsrYIwhMTGRLl26eP3cn3ceZuKbK3nmyn7kFRSRdCSHpMPZJB2xcxWKjOGyAXFMHBFPr3YnL4uaW1DILbPXsD/tGO/fOqzcmsLuI9k88802ElKyiQgJJDw0iIiQQCKcr0XG5ohKybI3+pTMXI7lFxIWHMA9Y7tx86gufq1hsToplfV704mNCqVVVCixziNSh8yqBkADiZvyAsmBAwcQEVq1akVAgOayLKmoqIjk5GSMMbRt691EzQxXPhf85ye7LsX5PUrtN8aQV1hU4cJGx/IKmThzJZmuAt67ZRgx4Sc6tzNc+bz0w05m/pzI6G6xnNurNTl5heTkFpCdV0hOXgHZuYUAx2/u7jf7tjFhHicdVKqx0kDiprxAYowhLS2NtLS0mi9UPdG0aVOaNm3q9afr//vwVzbuz+CzO0f69Yk/K7eAG2asAODtm4cSFhTAe6t+Y9p322kRGcJjv+t1fNEkpVTV0kDiprxAoqrH/zYf4o65a/nsrpH0bBtd+RMqkZ6Tz7WvLyc0OICc3EKSM108cF4Prh3SgaBArUkqVV00kLjRQFJzjmbnce60xdw0Mp47zzqlys57JCuX2+eupV9cDHeP7UZMk8rncCil/KOBxI0Gkppz1ztr2Xv0GB9NGa61BaXqOU2RomrcF7/u57vNh5h/72gNIko1cPofrqpccqaLv3y2kYfGnUrXMrLIKqUaFq2RKI/lFxax9+ixk+Z/ZLjyycktJDuvgOzcAnLyCknJzKVH6ygmjYiv7SIrpWqABhJVodVJqUxfuIudyVnsSztGYZGhaXgw8S3sjPRm4SG0iQ4jIjSI8JBAIkKCCA8NZHS3WK+y4iql6i/tbFdlOpqdx78WbOXDNXv5/eD2DOvS4vjSq5rmQ6nGRzvbG6E1u4/iyi+kU4tw2sU08bh2YIzhozV7+efXW4lr2oRP7hihCy8ppSqkgaSBWbvnKM99s41VSakEBwaQk1dISFCATZTo1CjaN2tCbFQYraJDiY20aUIiQoPYcSiTRz/dyJb9Gfzx/B7cMKwTgdo8pZSqhDZtNRBbDmTw72+38eO2FC4fGMc9Y7vRvlkTUjJzbYLEI9nHO8n3p7mOJy/MK7AZaiNCAsktKOKCvm35y+960qoKs+wqpeo/nZDopr4FkgxXPpv3Z7D7SDahQScy2RZnts0tKOK1xQl8uX4/F/Rty/3ndK9wgSV3xhgyXAWkZLpIzswlIiSI/h2aVu8FKaXqJQ0kbupyIMkvLGLx9hQ27c9g8/4MNh1I57fUY4QGBdCpRTgFhYbsvILjw22LnMs4+9RWPHBud/rExdTuBSilGiwNJG7qciB5+uutzFmWRP8OTendLppe7aLp3S6GLi0jSs0ON8aQW1BEbkGR5ppSSlU7HbVVD6Tl5DFnWRJTrx7A+b3bVHq8iBAWHFjueuJKKVVTNEVKHTFraRJxzZpwrrPmt1JK1RcaSOqArNwCZv6cxB1jTtHZ4EqpekcDSR0wd/luYpoEc1E/75awVUqpukADSS1z5Rfy+k+JTDmzq6ZbV0rVS3rnqmUfrv6NwAC44rS42i6KUkr5RANJLcovLOLVRQncMroLoUE6+kopVT/VWCARkWARmS4iR0XkiIg8IyJlT24ROU1ElohIhogkiMiEEvtfE5FtIlIkInfVzBVUvc/W7edYfiHXDe1Y20VRSimf1WSN5DFgMNADGAhcCpQKAiLSFJgPzAKaAdcCL4rIKLfDfgXuAFZWa4mrUWGRYfrCnUweGU94iE7nUUrVXzUZSCYDfzfGJBtj9gDPOdtKGgFkG2NmGGMKjTErgHnAzcUHGGNeNsZ8D7hqouDVYcHGg6Rk5HLj8PjaLopSSvmlRgKJiDQD2mNrEsXWAb3LaN4KAMra1q/aCljDjDG8/ONObhzeSdObKKXqvZqqkRSno01325YGBAOhJY5dCkSLyBSnX2UkMB6I9vZFReRxETHuDx/KXqWMMczfcJCEw1lMHtW5toujlFJ+q6nG+SznazQ2gADEAPlArvuBxphUEbkIeBZ4CtgMzASGefuixpjHgcfdt9VGMMnJK2DpziMs3J7Mwm0p7D16jHvGdqNlZMkYqpRS9U+NBBJjzFER2Qv0B/Y4mwcAm8pKxWuMWQYc71wXkfeBRTVQ1Cr1zaaDzFm2m5WJqYQGB3BGt1juGduNM7vH0loXjlJKNRA1OVxoJvCoiCwHwoAHgellHSgiA7E1kQDgBmAMdqRX8f4QZ18AECQiYUCBMaagOi/AW49+spExPWKZe8tQBnZoqjPXq9JXD8KpF0HXs2q7JEo1ejV5Z3sS+AXYge10/xJ4CUBEvhaRR9yOvRdIBlKAq4CzjTH73fZ/CxwDRgPTnO8fq+4L8EaGK5/DWblMObMLQ+KbaxCpSseOwqoZ8OEkOJpU26VRqtHTha2qybrf0rjilaVseWIcIUH1PIhkH4GIFrVdihO2fwPzboH40ZC2B/7wLQQ3qe1SKdWgVbSwlcd3OBHRNgQvJKRk0bF5eP0PIgd+hX93h9SE2i7JCXuWQYdhcNl0yMu2zVyN7AORUnWJN3e5L0Vks4jcLSK6OHglElKy6dIyoraL4b+NH0NRAfzydm2X5IQ9y6HjMAiLgavfhk2fwJpZtV0qpRotbwJJHPBf4DZgn4jMEJHTqqdY9d+ulCy6xNbBQLJvLexe5tmxxtibdMfh8MtcKKwDYxnyXbBvjS0TQOtecMmL8PWfYO+a2i2bUo2Ux4HEGJNmjHneGNMHuAA7kfAnEVkhIpOckVTKkZCSTZfYyMoPrGnf/RU+v9uzpqD9ayF9H1z+mm1C2vld9ZevMgfW2a/tBp7Y1vdKGPwH+GACZB+ulWIp1Zj52oC/CvgR2AX0Av4KJInIuKoqWH1WWGRIPFIHm7YyD8Hun+HITts8VJlNn0KXM6FpR+h7BaydXe1FrNSeZdBuEASXmIdz3pMQ0x4+mlw3ak5KNSJeBRIR6SkizwP7gT8CrwFxxpgu2GAyo+qLWP/sTztGXkFR1dVIDu+A186CJdMgJ9X382z5HJp1hl6XVB4UjLGBpPd4+/OgCXa0VMYB31+/KuxeZvtHSgoMht+/BSlb4cv7PA8mGz+GWRf5974q5a8Fj9jm43rKm1Fbi7GJFtsDVxpjehljXjTGZAAYY2bgQz6shmhXShZRYUG0jKyi1r6fpkJhvv1Dm9oTPrsLDm7w/jybPoXel8Ggibbvw5Ve/rH71kLGPjvpD2wtoFVP+PUdX66gahQVwW/LT/SPlBTVBm74GHZ+D+9cVfH1GQOLnoV5t9nr/PhmKCqsnnIrVRFj4Nd34ZuH623TrDc1koVAF2PMFcaYH8o5pr3/Rar/ivtHylm3yztpv8GGD+B3z8GdK+GadyA7BV4dDW9eAJs/86y/o7hZq/d46HIWRMTCho/KP37TPOgyBsKb259FbK1k7Rx7Q/dFXrZ/n/xTttrg0OH08o9p0xdu+QFyjsAb58PR3aWPKciFT2+HpS/C9R/CxC/h4HpY+LTvZVPKV0cT4VgqRLaGH5707DlFhXZwyZ4VpR8HN1ZvecvgTSB5Cjvb/DgnO+/xzIPFtZPGLuFwFl2rasTWspeh/RDbnBMQAKeMheveh3vWQtwg+4l686eVn2fL59CiK7TuY88z8Ibym7eMsQGquFmrWN+rIGM/JP3k/XUc2QX/PQPePB8K8rx/Ptj+kdieJ4JbeaLbwk1fQ/POMGPsyaO5clJhznjYvRRu/s6mWImJgytnwpKpsG2Bb2VTylf71kJ0e7jsFftBbf+6yp/z9UPwxjnw1kWlH6+Osk1lNVjD9iaQzAdKtikMx6Y6UW4SUrLpWhX9I9lHYO1bMOr+0vuad4Hzn4Jht9u+k8pqJZs+gV6X2ZoFwMDr7WTDA7+WPnbfGsg8AKf+7uTt4c09618pafcymHEOtO4NuZmw8jXvnl+seP6IJ0Ii7ByTflfDrAtts96RXbYchflw8/cQ2+PE8Z1HwzmPw7xb7XFK1ZS9q6H9adB+MPS/xg5lr+j/+df37P/gLT/AX1JKP275HjZ+BO/fALlZ5Z+nCnkTSPpj1wpxtxS3ZIrKqrLJiCv/C83iodt55R8z7HZI3goJP5Z/TOZB+wncvYYR0x5OOcd+Aipp0ycnN2u5GzTB1m48baJa/wHMvgQGT4YrZ8E5f7NNSJmHPHu+uz0V9I+UJSDQBtvzn4KP/wD/PRPa9oeJn0NkbOnjh98FXcfYYcR5Od6XTylf7FsDcYPt92P/HxzaDBs+LPvYgxvgi/vgd/8+eQi8u7jT7Aelo7th5jg7hL+aeRNIDKWzBQdRejXDRi0rt4CDGS7/R2zlZsGK/9raSEV9LZGtbDPVkmnlH7P5c2hxiq0RuBs0wd7o84+d2FZUdPJorZI6jYLoOPu8ihgDP/4TPrsTLn4exv7FNqn1+73ttP/+iYqfX1L6Xkjf43mNxN2Qm20n/FmPwBVvlJ+XSwQufRkK8+zIL027oqpbQZ5tFYhz5nZHtYYxD8G3f7G1d3fHjtpaRr/fw6AbKz5v0w4weYHtd5kxtuyWhyrkTSBZD/yhxLabAB+GDzVciSnZiECnFuH+nWjtWxAWDb0vr/zYEXdD0pLyZ3Zv+sSO1ioZkLqPg6AQG2iK7VsNWQehx4VlnysgwP4Rr32r/BttvsuOglrxKtwwDwZcd2KfCFz4jB2l4s1M9D3LIaqdndPiiy5jYPgdtvwVCY2yTWJbv7IZhpWqTsmboCgf2g04se302+zf4U//PrGtqAg+mQJNmsEFz3h27rBouPZ920T95jjYOr9Ki+7Om0DyGPCciMwXkWki8hUwFXikkuc1KgmHs2jfrAlhwYG+n6QgD5a+BCPugUAPloxp3tkGnJ/LqJVkHLCd1GXVMIJCoP+1J/d5bPrUjuqqqEN7wPWQss12ErrLy7E5r147086Kv/l72/dQUruBthb19f95PgKsuH+kKkbCVSa2h62ZLHgYvnzANh1WpDDfjoB78wI7pFg1DtsW2GZQf+xdDa162T69YkEhMO5pO9CmuL/up3/Dbyvh97NLT8atSGAQXPgcjP0rfHAj7Piff+UthzcpUpYBg7Gz2bsBicBgY0zJfpNGbVdKNl1a+tmsteED27wy4HrPnzPqPvspOmX7ydu3fA4tu9k/1rIMmgC7l8DhnfamvvnT8pu1ikW1sbWZtW/Zn4/uhm8fs3NcFj4Nfa60HYEtTyn/HGP/al/z13c9uz5v+0f81fsyOzruaBJMHwZvXWLfX/eRMFnJsPBfMK2PzUAcEm5rMTofpXFY9hJs+aJ0E5Q39q090azlrts5tg/zm0dg5/9g0dNw5Ru+1chFbF/qhM/K/mBXBbxaIdEYswm4u1pK0kAk+JussagIlvwHhk2xNyZPtekLXcfC0uftp+liJUdrldSyG3QcAb/Msc1ZWclwajnNWu4GTbAd2NmHYfvX0P50uGgq9LzEzjKvTERL22fxv8eh50U2k295XOlwaKNv/SP+OGWsfRzeYUeazbvV1tROm2RrZJs+gZbd7XX0vco2UTx7ig168SP9e+2UbRDTwbu/gbK40u1cpNa9PavNGWPn6zTvaj8Z+6Oo0AbiFl39O09ddGSXHQYfGGJrFb6u1LlvtR3kUZbzn4KXh0LiYhjzMHQ92/fyAsSPqvwYH3kVSJzEjD2BVrh1shtjvq3ictVbCSnZDOvixyJQ276yQ2+H3Oz9c0fdD7MvhTGP2LkRGftts9bvplb8vEETbDLHvCz7D9GkWeWvdYoznLdJU7jlx5PbeD015A+2KWzRM/afpjy/rYKQyNKDBWpKy25w4bNw9l9sDWrtHNuceOOn0GnEyTfoU86xtTp/AokrA14ZCaGR9ncz5GbvP4mmbLPBb927kJ9tP2gMnQJ9rih7sEFeNqx/H1a+Dsmb7aCKq+dUPmenIsULkD2027Mm2vrkl7dtDTko1H5w8CWQuNLh8HY77LcszbvA2Y9B8hYY9YB/5a1m3qRIGQjsxC6Xu8B5fO08FFBUZEg8nO17jcQYO/pq8E2e3cxL6jTC9j8sn25/3vw5tOxhR0lVpNelUOCC1TMrb9YqFhhkVya8bLpvQQRszeWCp22nfMkmOXd7ltnZ7AF+9DtVhbBoGHob3L7E3mTjR5b+lN/rMjuZ05/mrd1LbU3kgmftHJzn+8N710PCoopHkhUV2g7V2ZfaT7JHdtnmkPs329rm//4GU3vZWmD6Xvuco0nwzaO2WXLRs9DncrhjuW1anXGOf3NqDm20H06SN/t+jrqosADWzbVBvuNw2ONj6/6+tRAcAbGnln/MyHth/KuVDxKpZd58TPg3dlLiw0AC0Bnb2f5VNZSrXjqY4eJYfqFvkxHzcmD1m3ac+NU+Jm8TsbWSj2+G0Q86o7XGV96kERJum2bWzi5/tFZ16TLGvuZXD8D1H5XdkbhnuT2uPuhxgU3Tv2eZ700JiYvsMsL9rrKP/b/Aitdg7pW2yamswG2Mfc3swzDgWjuyx33C5VmP2L+JzZ/ZYeU/v2Dn1Oz/xd4ML37e5lUrbpac+AV8docdOnr1XN9qWMlb7Nd9a6BtP++fX5m8bDvfIjDk5JGBFTHGdlz3vdLO0fLFjm/tkPlel8LeVfDz83bAhSdNuu72rbYf/Gr7A1IV8CaQDAAuNsZki134PE1EHgSWAR9XS+nqmYSUbCJCAmkVFVr5wcXS9tgO2rWzITDUjrCIbut7IbqPs2PIv/+bTXB48X88e96o+22nX5Omvr+2r8Y9bVM7zL4Urplr+0+KFeTZf7gxf675cvkiLNo2b2361PdAkrDI9sMUazcQxr8C5z5hPwlnlDPBbPiddmZ0ef1NQaF2DkK/39ub+47vbAAp6yYfHAaXz4CF/4Q5l8HFL9gA5Y2UbRDUxP7+Bt/k3XMrcjTJNsH9MsfWwgJDoN81nn1qT9lq81ktfwWufbfivG3lWTvbBqKQCDuRsCDX5morq9O8IvvW2hntDYA3gaQIcDnfZznL7aajiRqPSzic5VmyRmNsB9rK12DbfJtL68LnbEe1vx2cAQEw8j74dIqtMlfWrFWsaQebNqU2xMTBH/5nJ1vNGAvXfQix3e2+A7/apX69/SetTb3H29E2F/zL+0+bWSl2bkGXM0vvi4y1o/OqQtxplb+nAQFw9qO2rf6LeyA1wdZsPOm0LyyAIzvse1FymLgvjIGEhfZ/ZvsC6DAULvoPdD4Dnutmm9E8qfUkLIJWve2oqFkX2QDd5wrPy5GxH3Z8Y0clgu3HatvP1pq9+Rs1xnbS97/G8+fUYd40vG0FhjjfrwWeAP4G7KnqQtVXNutvBf0jedm2+Wr6cNtMERpt/yD/8K39hONvECnW90rbOdvnyqo5X02IaAETPrU3iDfOsTcNsM01bQf4P3qpJvUYZztSd/vQdp60GCLb2NFgdcWAa+3AgpWv2WYxT6QmnBjCnrzFvyGyYJsL37kamjSHWxfaWdt9Lre117b97QczTyQush3j5z5hJ8bOu832DXmaxWDdXDvoo+2AE9s6Drd/p95I3wvZySdSo9Rz3tRIHsamSQF4FPgQiMHOblfYdUgGdypjlEtqom2++mWO7VwbMhkGTSo731NVCAyG2xbbQFWfBIXC+P/C4mfh7SvtcGJvEjXWFaFR0O1cO3rL23H7CYvsp+yamHjpjfiRNqNB0k92aHplUrbYgNhpJASF2Yy2vs5hSE20N/DJ30KHIaX3dz7DBogR5QyjLVZYYDNAFDcbnjYJmnaCDyZC6i7bzBdUQbN0UZEdsTfi7pN/Px2HwVcf2mDk6e9t32r7/kS38+z4Os6jQCIigdjayy8AxpgNQAVDDRqZxMWQk0rHgxsZ2TYONm2y2wvzYOM8WxUvq0OzOvky6qsuEIEz/2SbUz69A0whXPVWbZfKe73Hw4I/205vb5q3EhfBGf9XfeXyR9sBsOkzz45N3gqtTrWj+9oNsDdOXwPJ0hdtf1NZQQSg85mw6o3KO7wP/Ar5OXZ0Y7GuZ9nlBOZeZZcXuPrt8oc8Jy2GrEO2xu+uwzC7RlBqgudzZvatscN+69oHBh95FEiMMYUiMt8YU8cWIa8DXBnw1sUURbVjSm4+rbeEwfbiPw6xn5ZuW1w9o1Yasr5X2gl5Pzx58j9+fdH9fJuwcvfP9m/AE0d3245kT4+vaW3728SZOamVzy9J2WLXjgHbd7DPi7xq7jIP2Tkb171X/jEdh9sO731roePQ8o9LXGjLEhp18vbYHraJ+b3r7JDn6z8sOyCsnW1HapX8kBbV2n7w2bPM80Cyd43tp2kgvOkj2SYi2rFekisNgB2XzWd03vMU3v0r3LfBeayHS1/SIOKrjkNh0pf+TYqrLaFRJ0ZveSpxMTTr7HtiyurWvKudGOpJJtmUbbZGAvbm7U2CTncrXrHn6VLBhL+QcDv6KnFRxedKXFx+kI5oCRM+t7WnGWMh6eeT9+ek2nQog8rJrdVxhOf9JIUFcGBdg+kfAe8CyXTgfREZJyKnikj34kd1Fa5ecNYF35kpxDVtQpOQ+j8mXFWR3uNtrrPCAs+OT1xU9mituiIgwM6QryyQFObbtDLuNZLM/XbEkzdc6bbJqrKlFMA2byVUEEjyXba/rXMF72/xkOcht9ghz+vc8sCtf9+u4dOpnPk0HYfZ83siZYudh1LeeiL1kDeB5DXsiojzgc3AFuxIri3VUK76w5UBQU3YdSTfvxxbquHpPs6OVtr9c+XHFg8Jr6vNWsXa9q88kKQm2LxjxRMim3aEiFjvm7dWv2lrCj0vqfzYLmfC3pXlL0i2d6X9Wtm8keIhzxe/YIc8//CU08k+29ZGygtoHYfDkZ12+HZl9q2x701YPRsMUwFvAknnEo8ubl8br9wMCIu2yRqrYlVE1XCERtrVLTd/WvmxKdtsR25Fn5jrAk8CSfIWiGp7YnKriNO8tdrz18l3wbLpNkWIJ4MV2g2CgGA7CbcsiYttraGiUVnuioc8r3od3rrY/n76VzB7vkVXCG9Z/uu727u6QTVrgXdp5HeX96jOAtZ5rnQIjSbhcLb/qyKqhqf3ZTbnWWXNW4mLoHWfk2f110Vt+9uhsq6M8o9J2Vo6f1TcYO9qJL++AxJg18vxRFCIHZRRXvNWwiLvg3T8SLumTuYBuzhUVOvyjxXxvHlr31qIG+RdWeo4j+eRiMit5e0zxrxWNcWph1wZmLAYEvb6kaxRNVzdzoe8O+2aLxXlC6sPzVpgk4AGhdmccOXl30reUjqjQtwg+Pk/NqVJZTWMwgKbv2r4HZ7XIMA2b22cV3q7K8MGsQue9vxcxVp0hTuWgfFgAbaOw2FTGa/vLjfL9pGUl/G3nvKmaevhEo9pwItAPUmCVE1c6eQFRZKVW6A1ElVaaCR0P6/sG1yxokI70a+uN2uBnRfSuk/FzVtl1khOs5mAD1eQ5bnYls8g5yic5uVc585n2NFQx9JO3r57qR1t5j4b3RtBoWWn3i+p43D7vuRll3/M/l9sbrDyFpqrp7xp2urs/sDOan8Z+Ee1la4+yE0nk3CaBAfSNtqLJTBV4zFwgrNGfTl9BAfW2U+q9WW+TEX9JIX5ttO5ZI2kSVNo0a3yfpLipRROv9n7zujWfSGsqZ297i5xsZ3QWN1Zdtv2s/00FTXh7VtjA1pNTEquQT4nuTfGFGDXcX+06opTD7kyOFrYhM4tIwgIaBizVFUV63aOHVL6wQSb5r2kxMW26ae+jOKpKJAc2WWTbLqnsC/mycTEXd/bocNDPUjDUlJAgJ09XzLvVk0Nqw4Mtk1WFfWT7Fvd4Jq1wI9A4ggD/FgOsAFwpZOSH6r9I6pi5/7N5nX66KbSHe++dATXprb94fC2sofapmyB6LiyU9m3H2xvpOUxBn6aCgNvgMhWvpWtOO9WsawUmxm4pvqfKkrgmH3YyRLcsDrawbsVEm8t8XgA+AbwMO1mA5WbwYHcEO0fURULDIarZtmVIH/8+4ntBbnOwl31KJC06gkSCIc2ld6XXEb/SLG4QXBoc/lzPbZ8YUc0jbrf97J1HmP7aDIP2Z+TfoLI1hWvQliVOg6D31aW/rCQst3OmG9xih2A0cD409k+CfgV8GFx8QbElc5v2cF01RqJqkxUaxtMlr5kb5pgbzoYaO/DAku1JSjUBpMD60rvS9lS/k27dV/bT1FWs1j+Mbvk7+gH7QxyX7XoamtExc1biTWcTbn9EJsYMtktyCYssksjdBgKEz6zAzAaGJ87240x/YwxNxtjDlZnAeu6IlcGu3OC6dKy4f1xqGrQaTic93f45HbbF5C42N5gylpiuC4rr5+kOOtvWYJCoE2/svtJfn7B3uxH3O1fucRJlJq40P5cnJa/poRF21Ftxf0ka+fA21fAsDvtEgneDGeuR7yZRxIHHDPGpLptaw6EGWO8TKLTcJhj6WSYJsQ182B4oFIAQ2+za32/f4O9sfS6tLZL5L22/W3aEHcFeXayYmzPsp8DZfeTpO2BJVPhijeqJqB2PhMW/sOe92hizfc/dRxu0+Jk7LdL+l72CvS7qmbLUMO8adr6EJsSxV1nZ3ujJbnpHAuIpFl4wxrOp6qRCFzygp25feDX+tXRXqztADvxsCD3xLbUCkZsFYsbXDoT8Ld/sTffU39XNWXrfIYNImvnQLN4aNapas7rqY7DYPNndiG7iZ83+CAC3gWSntgldt394mxvnApyCSjMJSSyaeXrtCvlLiTCLqI0aILvE+VqU+vedtGxZLecrclbILp9xcOY4wbZNU2yku3PiYth65d2ffuq+h+KibOd2sun1062gC5joO9VcPP/6t/qnj7yJpC4gJI9yhFAYdUVp55x8g2FRdbT1QhV7WrRFS550c4Wr29Cwm26FPd+kpQK+keKNe9iF4bat8aObPr6ITj9toprMb7ofKadSV8btb3w5nDFDHutjYQ3gWQl8EiJbQ852yslIsEiMl1EjorIERF5Rsr5GC8ip4nIEhHJEJEEEZlQYn+ciCwQkWwRSRKR6724jqqTawNJREzjnkqjGqmSHe7JFYzYKlacCXjfGlj9hl2idsxDVV+24uHU9SF/WQPgzUehPwOLReRSYDvQDWgDePqbegwYDPTATmT8DvgNm6/rOBFpil3z5FFgpvOcb0UkwRhTnPvgHWATMB4YCnwhIpuMMeu8uB7/udIpQmjatB6u4KeUv9r2h40fn/g5ZatNm1+ZuMGw4xu7bsl5T5U9edFfPS6EGz72fWKj8oo3w3+3AL2BN4F9wCyglzFms4enmAz83RiTbIzZAzznbCtpBJBtjJlhjCk0xqwA5uHMVxGRrs4xjxljjhljFgKfAxM9vZYq40rHJU1oHaMjtlQj1La/nTVeWGA73Y/sKp1jqyxxp9nkhc27wIBqakwIDLZLHasa4VXjrDEmGfi3ty8iIs2A9tgJjMXWAb1FRIwxxm17AFCyySsA6Ot83w/Y6z4M2TlXqY9CIvI48P+8La/HcjPIIILWmqxRNUZt+kKBy6ZLAdv57klfR/vBEBoDFzxr82Opes+bFCnTRGR0iW1niIgngaV4tl6627Y0IBgoOUNnKRAtIlOcfpWR2Cas4qEgkSXOU3yuqJIvaox53Bgj7g8PyuoxcyyN9KImtNFAohqjsGho3tX2k6RshZgOEFrq37C08ObwUCJ0GFL9ZVQ1wpuPA9dQevjvWsCTJcyynK/u4wJjgHwg1/1Ap6ZxEXADcBB4GttXcsTtXCXHF8YAmR6Uo0q5stJIN020RqIar+IO94pybJWlulO6qxrlTSBpAhwrse0YpYcEl2KMOQrsBfq7bR4AbCrRrFV8/DJjzChjTAtjzGhsp35xSs/1QAdnVr37uTZ6eB1VJjsjlQwTTpsYDSSqkSoOJClbKh/6qxosbwJJInBWiW1jgCQPnz8TeFREYkWkA/AgtuO+FBEZKCKhItJERG5xXuc/AMaYXcAy4Eln/xnApcBbXlxLlXBlHiU3MJKwYP10pRqptv3hwHqb1bei1CiqQfOms/0/wHsi8gx2+G934I94vtTuk0AssAMoAt4AXgIQka+Bn4wxxast3ovtFwnE9pmcXSKf17XYIHQESAGm1PjQXyA/J43CkHqyGJFS1aFtf8jPtulRtEbSaHkcSIwxb4lIMPYm3xlbE3nMGDPTw+fnA7c7j5L7Lijx8yRsmvryzrUPqPWk/kXH0jGhbWu7GErVnvDmENPRpj1pWcWz01W94e3w3xnADBEJKd4mIiHGmLwqL1k9IK50giL1U5hq5Nr2s18b4DobyjPepJGPxTZFnUfpUVONspMgMD+T4AjNs6UauS5jIFSbeBszb2okU4FO2Nnos4EbsSslzqmGctULoQWZhEVpIFGN3Om31HYJVC3zJpCMBU43xuwVkUJjzKcisgk7Guul6ile3RZWlE2kJmxUSjVy3gz/jTDG7HW+z3X6RnZwInVJo5JfUEikySG6qQYSpVTj5k0g2SMipzjf7wSuFJHzgOyqL1bddzj1KEFSRLPmGkiUUo2bN01bL2Gz/+7EJm78AJtc8eFqKFedd/hICm2Bps1ia7soSilVq7yZR/Jft+/niUgnINIYs61aSlbHpaemABDYpBrWUlBKqXrE5zU+nUmBjVZ62hHyCSI4WNciUUo1broYgI+yM47iCoiwS4cqpVQjpoHER7mZqeQF6UxepZTSQOKjPE3YqJRSgAYSnxXlpNkV4pRSqpHTQOKr3AwdsaWUUmgg8UlWbgFhhdmasFEppdBA4pOD6S6iJUcTNiqlFBpIfHIow0XTgGOEaI1EKaU0kPjiYLqLFoHHdA0GpZRCA4lPDmW6iAk4pqO2lFIKDSQ+OZTuIpIcCNNRW0oppYHEBwczXIQXZWvTllJKoYHEJ8npOYQWaY1EKaVAA4lPcjJS7TfaR6KUUhpIvFVYZHBlpdkfQrVGopRSGki8dCQrlwjjrC6sNRKllNJA4q1DGblESw4mOBwCg2u7OEopVes0kHjpYIaLuCb5iHa0K6UUoIHEazaQ5OnQX6WUcmgg8dKhdBdtQ/K0f0QppRwaSLx0KMNFbEiuziFRSimHBhIvHcxw0VwTNiql1HEaSLxUnEJem7aUUsrSQOKlg+kuojRho1JKHaeBxAvH8grJcBVowkallHKjgcQLhzJcAIQWZmmNRCmlHBpIvHAww0VoUACBeRkaSJRSyqGBxAuHMly0iQlDcjO0aUsppRwaSLxwKMNF66gwcGmNRCmlimkg8cLB9FziogQKc3X4r1JKOTSQeOFQhotOEQX2B62RKKUUoIHEK4cyXMSF5dkftI9EKaUADSReOZjhok1oHiAQElnbxVFKqTqhxgKJiASLyHQROSoiR0TkGRGRco7tIyI/ikiaiBwUkRdFJNht/7kislZEMkVks4iMq+7yG2NIzsilVajTPxKgMVgppaBmaySPAYOBHsBA4FLgrnKOfQfYAMQCg4CzgHsBRKQLMM85XwzwJ+BjZ3u1Sc3OI6+wiBaBLl2rXSml3NRkIJkM/N0Yk2yM2QM852wrSzzwtjEm3xizH/ga6OPsGwesMsbMN8YUGWO+BFYCE6qz8IcycgGIEc2zpZRS7mokkIhIM6A98Kvb5nVA73Kat54DJopIqIh0AH4HzHf2BQAlnxMA9CvjdR8XEeP+8PUajuUX0qttNMH5mTr0Vyml3NRUjaS4ZzrdbVsaEAyElnH8AmxzVjawB1gFfOTs+xYYKiKXiEiQiFwGjARK3d2NMY8bY8T94esFnNapGfPvHQ06q10ppU5SU4Eky/nqfgeOAfKBXPcDndrLd8ALQBNsP0kM8DyAMWY78HvgcSAZ+APwHnCk2krvTme1K6XUSWokkBhjjgJ7gf5umwcAm4wxJZubugIhxphXnT6Sw8AbwMVu5/vSGDPIGNPcGHMx0A1YVK0XUcyVrk1bSinlpiY722cCj4pIrNPv8SDwZhnHbQNcInKLiAQ6NZSbcOtfEZHBTrNWlIj8FWgOvFUD12CbtrRGopRSx9VkIHkS+AXYgQ0KXwIvAYjI1yLyCIAxJhM7NHgytrlqO1AE3O52rqeBVGwtpx9wljEmu0auwpWufSRKKeVGSrcsNWwiUkZrmhdeGQWn3wynTaqyMimlVF0nIpQ3YEmnZ3srN12btpRSyo0GEm9p05ZSSp1EA4k3jIHcTK2RKKWUGw0k3sjLAlOkgUQppdxoIPGGy5mYr01bSil1nAYSb7gy7FetkSil1HEaSLzhSofAEAgOq+2SKKVUnaGBxBuasFEppUrRQOINTdiolFKlaCDxhitNEzYqpVQJGki8oQkblVKqFA0k3tBZ7UopVYoGEm+4MrRpSymlStBA4o3cDAhrWtulUEqpOkUDiTe0aUsppUrRQOINHf6rlFKlaCDxhq7XrpRSpWgg8YbObFdKqVI0kHhDm7aUUqoUDSSeKsyH/Gxt2lJKqRI0kHgqN9N+1aYtpZQ6iQYST7nS7Fdt2lJKqZNoIPFU8aJWWiNRSqmTaCDxVG4GhERCYFBtl0QppeoUMcbUdhlqlIgYn67ZlQFHdkDcaVVfKKWUquNEBGOMlLlPA4lSSqnKVBRItGlLKaWUXzSQKKWU8osGEqWUUn7RQKKUUsovGkiUUkr5RQOJUkopv2ggUUop5RcNJEoppfzSKPN9iJQ5p0YppZQPGt3Mdn84s+IbXRRqrNcNjffa9bobF3+vW5u2lFJK+UUDiVJKKb9oIFFKKeUXDSTe+VttF6CWNNbrhsZ77XrdjYtf162d7UoppfyiNRKllFJ+0UCilFLKLxpIPCAiwSIyXUSOisgREXlGGuCsRhG5S0RWi0iuiHxUYl+0iLwnIpkickBEHqytclY1EQkVkddFJNG5vi0iMsltf4O8dhF5UkR2i0iGiOwTkakiEuy2v0FetzsRiRWRwyKy2m1bnIgsEJFsEUkSketrs4xVRURmiUieiGS5Pbq77ff5962BxDOPAYOBHsBA4FLgrlotUfXYD/wdeL2MfS8CTYA44DzgIRG5uAbLVp2CgAPAOUA0cBMwTUTOdvY31GufA/QxxkQD/bF/2+43j4Z63e6mAZtKbHsHSABaApOAV0VkQM0Wq9q8YIyJdHtsd9vn++/bGKOPSh7Ab8Albj/fAvxS2+Wqxut9HPjI7edwIBfo57btKeCT2i5rNb4H84C/NpZrx940vwfeaCy/c+dmuRj7wWG1s60rkA80dztuLjCttstbBdc7C3iunH1+/b61RlIJEWkGtAd+ddu8DujdEJu3ytEDCAQ2um1bB/SpldJUMxEJA07HflJt0NcuIreLSCaQAvQDXnZ2NfTrDgdeAG4H3Ieu9gP2GmNS3bato4FcNzBZRFJFZIOITHHb7tfvWwNJ5SKdr+lu29KAYCC0xktTOyKBLGNMkdu2NCCqdopTfZwPBzOAHcAnNPBrN8a8YoyJAk4FXgEOOrsa9HVja92fGGNKNmtFcvL/OjSc634B6A7EAncAj4vIjc4+v37fGkgql+V8jXbbFoOt/ubWfHFqRRYQWaIGFgNk1lJ5qoVzfa9gP51d5vxTNYprN8ZsAzYAbzqbGux1i0h/4DLgiTJ2Z3Hy/zo0kOs2xqw1xhw2xhQaY34Cngeudnb79fvWQFIJY8xRYC+2M7LYAGCTcRoSG4FtQCEnV3MHcHI1uF5z/oFexjZpnWeMKf5U2uCv3U0Q0M35viFf91nYDuVEETmIvaH2d77fA3QQkeZuxw+gYVx3SUVAceDw7/dd2x1A9eGB/eSyHFsl7ABsAe6u7XJVw3UGAWHYkVvznO9DnH2zgU+xVd0+2CaQi2u7zFV47S9j+8FalLGvQV47cCfQwrmZ9HZuGq80gusOB9q4Pe7F9ge0cd6Lxc7fQxPgDOyn8gG1Xe4quO7fO79LAYZjRyr+oSp+37V+cfXhge0PeQXbZpgKPIuTXqYhPbDtxqbEY6GzLxp43/mnOgg8WNvlrcLr7uRcqwtbxS9+vNqQrx2YDxwGsoFE4Dkg3G1/g7zuMt6HSTijtpyf44BvgBxgN3B9bZexiq5zsXMPywQ2U+LDsD+/b821pZRSyi/aR6KUUsovGkiUUkr5RQOJUkopv2ggUUop5RcNJEoppfyigUQppZRfNJAoVY+JSLyIGBE5tbbLohovDSRKKaX8ooFEKaWUXzSQKOUnZ6neJ0Rku4ikichKERnj7JskIgdF5A4R2eMs1/y+iLRwe35TEXlNRPY6Szl/IyK9SrzGRBFZJyLpInJIRJ4vUYzTRWSNs3zqehEZVe0XrpRDA4lS/nsFOBM4F5sEcSrwlYh0cfa3xCbJ682J9SDecnv+HGzq+iHYpKAbgB9EJAZARG52zvkn5/ynYJNquvsDNjV6M2xOpXca0cJrqpZpri2l/OCkGz8C9DXGbHTb/iPwP2AfMBObVTjV2TcIWAO0xWZi3Q8MNsascfYHOc97yBgzS0Q2Am8bY54u4/XjsQkXzzHGfO9s64MNRnHGmP3VcuFKuQmq7QIoVc8Vr9+xpEQFIBjYig0I6ebkpVsTnK8d3bbtKv7GGFMgIklu+ztj14uoyD63790XY9NAoqqdBhKl/FO8NO0gY0xCyZ0iMgmIEZFmxi6SBlDc5PWb26FdsbUURCQQm9p+j7MvCdskplSdpH0kSvnBGLMb218xXUS6AohIhIiMEZHim38hME1EIkWkJfAM8LUx5oAx5gB2XZDnRKSNiDQB/olt8vrUef7zwB9F5BwRCRKRKBE5s+auUqmKaSBRyn/XA0uAL0UkA9tM9Sds8xbYxaNWYlfW3AkcBSa4Pf9G5zlrsMs6D8D2eaQBGGNeA/6M7XA/CuzAdqwrVSdoZ7tS1chp2nraGNOmtsuiVHXRGolSSim/aCBRSinlF23aUkop5RetkSillPKLBhKllFJ+0UCilFLKLxpIlFJK+UUDiVJKKb9oIFFKKeWX/w/IDHOOA5WCtgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d17ca470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "155d570c",
   "metadata": {},
   "source": [
    "Over the epochs, accuracy goes up and loss goes down. This is a nicely fit neural network. The epochs are set to such so that it does not overfit. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "db7ca6de",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bda80b01",
   "metadata": {},
   "source": [
    "We are now ready to evaluate the 5 models. This is done with 2 methods here.\n",
    "* Accuracy\n",
    "* Confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6cbc3d19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8864842792976725\n",
      "[[4342    0]\n",
      " [ 556    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94      4342\n",
      "           1       0.00      0.00      0.00       556\n",
      "\n",
      "    accuracy                           0.89      4898\n",
      "   macro avg       0.44      0.50      0.47      4898\n",
      "weighted avg       0.79      0.89      0.83      4898\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "dt_accuracy = dt.score(X_test, y_test)\n",
    "print(dt_accuracy)\n",
    "\n",
    "print(confusion_matrix(y_test, dt.predict(X_test)))\n",
    "\n",
    "#[0,0] = TN\n",
    "#[1,0] = FN\n",
    "#[0,1] = FP\n",
    "#[1,1] = TP\n",
    "\n",
    "print(classification_report(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "91d65fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8864842792976725\n",
      "[[4342    0]\n",
      " [ 556    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94      4342\n",
      "           1       0.00      0.00      0.00       556\n",
      "\n",
      "    accuracy                           0.89      4898\n",
      "   macro avg       0.44      0.50      0.47      4898\n",
      "weighted avg       0.79      0.89      0.83      4898\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "knn_accuracy = knn.score(X_test, y_test)\n",
    "print(knn_accuracy)\n",
    "\n",
    "print(confusion_matrix(y_test, knn.predict(X_test)))\n",
    "\n",
    "print(classification_report(y_test, knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "13d1d45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8924050632911392\n",
      "[[4308   34]\n",
      " [ 493   63]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94      4342\n",
      "           1       0.65      0.11      0.19       556\n",
      "\n",
      "    accuracy                           0.89      4898\n",
      "   macro avg       0.77      0.55      0.57      4898\n",
      "weighted avg       0.87      0.89      0.86      4898\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_accuracy = rf.score(X_test, y_test)\n",
    "print(rf_accuracy)\n",
    "\n",
    "print(confusion_matrix(y_test, rf.predict(X_test)))\n",
    "\n",
    "print(classification_report(y_test, rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c5ec08f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8864842792976725\n",
      "[[4342    0]\n",
      " [ 556    0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      1.00      0.94      4342\n",
      "           1       0.00      0.00      0.00       556\n",
      "\n",
      "    accuracy                           0.89      4898\n",
      "   macro avg       0.44      0.50      0.47      4898\n",
      "weighted avg       0.79      0.89      0.83      4898\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Python39\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "svc_accuracy = svc.score(X_test, y_test)\n",
    "print(svc_accuracy)\n",
    "\n",
    "print(confusion_matrix(y_test, svc.predict(X_test)))\n",
    "\n",
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "674e8980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "154/154 [==============================] - 0s 404us/step - loss: 0.2118 - accuracy: 0.9047\n",
      "0.9046549797058105\n",
      "[[4180  162]\n",
      " [ 305  251]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.96      0.95      4342\n",
      "           1       0.61      0.45      0.52       556\n",
      "\n",
      "    accuracy                           0.90      4898\n",
      "   macro avg       0.77      0.71      0.73      4898\n",
      "weighted avg       0.90      0.90      0.90      4898\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_, fnn_accuracy = fnn_model.evaluate(X_test, y_test)\n",
    "print(fnn_accuracy)\n",
    "\n",
    "\n",
    "print(confusion_matrix(y_test, (fnn_model.predict(X_test) > 0.5)*1))\n",
    "\n",
    "print(classification_report(y_test, (fnn_model.predict(X_test) > 0.5)*1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a1c4c5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DT</th>\n",
       "      <th>KNN</th>\n",
       "      <th>RF</th>\n",
       "      <th>SVC</th>\n",
       "      <th>FNN</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.886484</td>\n",
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       "      <td>0.892405</td>\n",
       "      <td>0.886484</td>\n",
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      "text/plain": [
       "         DT       KNN        RF       SVC       FNN\n",
       "0  0.886484  0.886484  0.892405  0.886484  0.904655"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = pd.DataFrame()\n",
    "acc[\"DT\"] = [dt_accuracy]\n",
    "acc[\"KNN\"] = [knn_accuracy]\n",
    "acc[\"RF\"] = [rf_accuracy]\n",
    "acc[\"SVC\"] = [svc_accuracy]\n",
    "acc[\"FNN\"] = [fnn_accuracy]\n",
    "acc"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a82d81d8",
   "metadata": {},
   "source": [
    "Having a look at these, the accuracy of the models is very good. ~88-90% across the board. This is because when using regression models, it is usually the case that which one you choose is no where near as important as how you prepare the data.\n",
    "\n",
    "The confusion matrices reveal the problem we noted before- the lack of balance in the data. Ofcourse, the accuracy is still good, however, trying to predict a 'Yes'/'1' response is very difficult for all the models. "
   ]
  }
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