Cost Reduction Maintenance -Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73978d6d",
   "metadata": {},
   "source": [
    "## Importing Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5264b2bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from ydata_profiling import ProfileReport\n",
    "from scipy.stats import boxcox\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4d19122b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "data = pd.read_csv(\"predictive_maintenance.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b520bae",
   "metadata": {},
   "source": [
    "### Data Exploration and Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1ae8b08d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>failure</th>\n",
       "      <th>metric1</th>\n",
       "      <th>metric2</th>\n",
       "      <th>metric3</th>\n",
       "      <th>metric4</th>\n",
       "      <th>metric5</th>\n",
       "      <th>metric6</th>\n",
       "      <th>metric7</th>\n",
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       "      <td>1/1/15</td>\n",
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       "      <td>15</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/1/15</td>\n",
       "      <td>S1F01TD5</td>\n",
       "      <td>0</td>\n",
       "      <td>68837488</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>413535</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/1/15</td>\n",
       "      <td>S1F01XDJ</td>\n",
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       "      <td>8</td>\n",
       "      <td>402525</td>\n",
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       "      <td>1/1/15</td>\n",
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       "      <td>3</td>\n",
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       "      <td>S1F02A0J</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "     date    device  failure    metric1  metric2  metric3  metric4  metric5  \\\n",
       "0  1/1/15  S1F01085        0  215630672       56        0       52        6   \n",
       "1  1/1/15  S1F0166B        0   61370680        0        3        0        6   \n",
       "2  1/1/15  S1F01E6Y        0  173295968        0        0        0       12   \n",
       "3  1/1/15  S1F01JE0        0   79694024        0        0        0        6   \n",
       "4  1/1/15  S1F01R2B        0  135970480        0        0        0       15   \n",
       "5  1/1/15  S1F01TD5        0   68837488        0        0       41        6   \n",
       "6  1/1/15  S1F01XDJ        0  227721632        0        0        0        8   \n",
       "7  1/1/15  S1F023H2        0  141503600        0        0        1       19   \n",
       "8  1/1/15  S1F02A0J        0    8217840        0        1        0       14   \n",
       "9  1/1/15  S1F02DZ2        0  116440096        0      323        9        9   \n",
       "\n",
       "   metric6  metric7  metric8  metric9  \n",
       "0   407438        0        0        7  \n",
       "1   403174        0        0        0  \n",
       "2   237394        0        0        0  \n",
       "3   410186        0        0        0  \n",
       "4   313173        0        0        3  \n",
       "5   413535        0        0        1  \n",
       "6   402525        0        0        0  \n",
       "7   494462       16       16        3  \n",
       "8   311869        0        0        0  \n",
       "9   407905        0        0      164  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows\n",
    "data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2194d792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124494 entries, 0 to 124493\n",
      "Data columns (total 12 columns):\n",
      " #   Column   Non-Null Count   Dtype \n",
      "---  ------   --------------   ----- \n",
      " 0   date     124494 non-null  object\n",
      " 1   device   124494 non-null  object\n",
      " 2   failure  124494 non-null  int64 \n",
      " 3   metric1  124494 non-null  int64 \n",
      " 4   metric2  124494 non-null  int64 \n",
      " 5   metric3  124494 non-null  int64 \n",
      " 6   metric4  124494 non-null  int64 \n",
      " 7   metric5  124494 non-null  int64 \n",
      " 8   metric6  124494 non-null  int64 \n",
      " 9   metric7  124494 non-null  int64 \n",
      " 10  metric8  124494 non-null  int64 \n",
      " 11  metric9  124494 non-null  int64 \n",
      "dtypes: int64(10), object(2)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "bd41d246",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       124494\n",
       "device     124494\n",
       "failure    124494\n",
       "metric1    124494\n",
       "metric2    124494\n",
       "metric3    124494\n",
       "metric4    124494\n",
       "metric5    124494\n",
       "metric6    124494\n",
       "metric7    124494\n",
       "metric8    124494\n",
       "metric9    124494\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0f9707fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          304\n",
       "device       1169\n",
       "failure         2\n",
       "metric1    123877\n",
       "metric2       558\n",
       "metric3        47\n",
       "metric4       115\n",
       "metric5        60\n",
       "metric6     44838\n",
       "metric7        28\n",
       "metric8        28\n",
       "metric9        65\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1a9cc5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       0\n",
       "device     0\n",
       "failure    0\n",
       "metric1    0\n",
       "metric2    0\n",
       "metric3    0\n",
       "metric4    0\n",
       "metric5    0\n",
       "metric6    0\n",
       "metric7    0\n",
       "metric8    0\n",
       "metric9    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1d0cbc66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failure Counts:\n",
      "0    124388\n",
      "1       106\n",
      "Name: failure, dtype: int64\n",
      "\n",
      "Failure Percentages:\n",
      "0    99.914855\n",
      "1     0.085145\n",
      "Name: failure, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "failure_counts = data[\"failure\"].value_counts()\n",
    "failure_percentages = failure_counts / len(data) * 100\n",
    "\n",
    "# Print the results\n",
    "print(\"Failure Counts:\")\n",
    "print(failure_counts)\n",
    "print(\"\\nFailure Percentages:\")\n",
    "print(failure_percentages)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cac9ebaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total unique failure devices: 106\n"
     ]
    }
   ],
   "source": [
    "# Filter the dataset for rows where failure = 1\n",
    "failed_devices = data[data['failure'] == 1]\n",
    "\n",
    "# Get the total number of unique devices that experienced failure\n",
    "total_unique_failed_devices = failed_devices['device'].nunique()\n",
    "\n",
    "print(\"Total unique failure devices:\", total_unique_failed_devices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "649dea2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             failure       metric1        metric2        metric3  \\\n",
      "count  124494.000000  1.244940e+05  124494.000000  124494.000000   \n",
      "mean        0.000851  1.223881e+08     159.484762       9.940455   \n",
      "std         0.029167  7.045933e+07    2179.657730     185.747321   \n",
      "min         0.000000  0.000000e+00       0.000000       0.000000   \n",
      "25%         0.000000  6.128476e+07       0.000000       0.000000   \n",
      "50%         0.000000  1.227974e+08       0.000000       0.000000   \n",
      "75%         0.000000  1.833096e+08       0.000000       0.000000   \n",
      "max         1.000000  2.441405e+08   64968.000000   24929.000000   \n",
      "\n",
      "             metric4        metric5        metric6        metric7  \\\n",
      "count  124494.000000  124494.000000  124494.000000  124494.000000   \n",
      "mean        1.741120      14.222669  260172.657726       0.292528   \n",
      "std        22.908507      15.943028   99151.078547       7.436924   \n",
      "min         0.000000       1.000000       8.000000       0.000000   \n",
      "25%         0.000000       8.000000  221452.000000       0.000000   \n",
      "50%         0.000000      10.000000  249799.500000       0.000000   \n",
      "75%         0.000000      12.000000  310266.000000       0.000000   \n",
      "max      1666.000000      98.000000  689161.000000     832.000000   \n",
      "\n",
      "             metric8        metric9  \n",
      "count  124494.000000  124494.000000  \n",
      "mean        0.292528      12.451524  \n",
      "std         7.436924     191.425623  \n",
      "min         0.000000       0.000000  \n",
      "25%         0.000000       0.000000  \n",
      "50%         0.000000       0.000000  \n",
      "75%         0.000000       0.000000  \n",
      "max       832.000000   18701.000000  \n"
     ]
    }
   ],
   "source": [
    "# Generate summary statistics for numerical features\n",
    "print(data.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca2e8f7",
   "metadata": {},
   "source": [
    "The count for metric1 is the same as for the other metrics (124494), but it’s displayed in scientific notation (1.244940e+05) because of the large mean value that follows it in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e81ccff4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows (excluding first occurrence): 1\n"
     ]
    }
   ],
   "source": [
    "# Identify all duplicates (including the first occurrence)\n",
    "duplicates = data.duplicated()\n",
    "\n",
    "# Count the number of duplicates (excluding the first occurrence)\n",
    "num_duplicates = duplicates.sum() - duplicates.head(1).sum()\n",
    "\n",
    "print(f\"Number of duplicate rows (excluding first occurrence): {num_duplicates}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6b1a10a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\ydata_profiling\\profile_report.py:354: UserWarning: Try running command: 'pip install --upgrade Pillow' to avoid ValueError\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "model_id": "6b2356c5b4df447d842e922114568ff5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "067695c707b849ba9d8c73c381f38e61",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
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       "model_id": "1e7edb3075c548ae84a00251b6286b20",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " \n",
    "profile = ProfileReport(data, minimal=False)\n",
    "\n",
    "# Generate and save the HTML report\n",
    "profile.to_file(\"profile_report_full.html\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb204dc",
   "metadata": {},
   "source": [
    "# Insights\n",
    "\n",
    "\n",
    "- The dataset consists of **124,494** entries and **12** columns.\n",
    "- Columns include 'date', 'device', 'failure', and numerical metrics from 'metric1' to 'metric9'.\n",
    "- The data is clean there are **no missing values** since the count of non-null values for each column matches the total number of entries (124,494).\n",
    "- 'date' and 'device' columns are of object data type, while other columns are of integer data type.\n",
    "- It is **imbalanced data set**, as the failuer class is about **0.1%** of unfailure class. Here oversampling approach is used to deal with imbalanced dataset.\n",
    "\n",
    "- **'date'** column has **304** unique values, indicating that the dataset spans over multiple days.\n",
    "- There are total **1,169**  unique devices out of which total unique failure devices: **106**.\n",
    "- Numerical metrics have varying numbers of unique values, suggesting differences in data distribution and variability across metrics.\n",
    "- **metric7** and **metric8** seems to **exactly same**, so we can drop one of them\n",
    "- metric 2,3,4,7,9 are **highly skewed.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4751f288",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d272537d",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b019188b",
   "metadata": {},
   "source": [
    "- date exploration and visualization\n",
    "- device come back to use\n",
    "- Create feature called 'failure_before'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "30d2da9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Date: 1/1/15\n",
      "End Date: 9/9/15\n"
     ]
    }
   ],
   "source": [
    "print(\"Start Date:\", data['date'].min())\n",
    "print(\"End Date:\", data['date'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e07ccac6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Convert data types\n",
    "# Convert 'date' column to datetime data type\n",
    "data['date'] = pd.to_datetime(data['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c447bcbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124494 entries, 0 to 124493\n",
      "Data columns (total 12 columns):\n",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   date     124494 non-null  datetime64[ns]\n",
      " 1   device   124494 non-null  object        \n",
      " 2   failure  124494 non-null  int64         \n",
      " 3   metric1  124494 non-null  int64         \n",
      " 4   metric2  124494 non-null  int64         \n",
      " 5   metric3  124494 non-null  int64         \n",
      " 6   metric4  124494 non-null  int64         \n",
      " 7   metric5  124494 non-null  int64         \n",
      " 8   metric6  124494 non-null  int64         \n",
      " 9   metric7  124494 non-null  int64         \n",
      " 10  metric8  124494 non-null  int64         \n",
      " 11  metric9  124494 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(10), object(1)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "07cae27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['month'] = data['date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2a71912c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "             date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "0      2015-01-01  S1F01085        0  215630672       56        0       52   \n",
       "1      2015-01-01  S1F0166B        0   61370680        0        3        0   \n",
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       "4      2015-01-01  S1F01R2B        0  135970480        0        0        0   \n",
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       "\n",
       "        metric5  metric6  metric7  metric8  metric9  month  weekday  \n",
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       "\n",
       "[124494 rows x 14 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['weekday'] = data['date'].dt.dayofweek\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c86e9609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 124494 entries, 0 to 124493\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   date     124494 non-null  datetime64[ns]\n",
      " 1   device   124494 non-null  object        \n",
      " 2   failure  124494 non-null  int64         \n",
      " 3   metric1  124494 non-null  int64         \n",
      " 4   metric2  124494 non-null  int64         \n",
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      " 12  month    124494 non-null  int64         \n",
      " 13  weekday  124494 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(12), object(1)\n",
      "memory usage: 13.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f8c0ad21",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124490</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0Q8RT</td>\n",
       "      <td>0</td>\n",
       "      <td>172556680</td>\n",
       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>332792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QK05</td>\n",
       "      <td>0</td>\n",
       "      <td>19029120</td>\n",
       "      <td>4832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>350410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124492</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QL3N</td>\n",
       "      <td>0</td>\n",
       "      <td>226953408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>358980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QLC1</td>\n",
       "      <td>0</td>\n",
       "      <td>17572840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>351431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124494 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "0      2015-01-01  S1F01085        0  215630672       56        0       52   \n",
       "1      2015-01-01  S1F0166B        0   61370680        0        3        0   \n",
       "2      2015-01-01  S1F01E6Y        0  173295968        0        0        0   \n",
       "3      2015-01-01  S1F01JE0        0   79694024        0        0        0   \n",
       "4      2015-01-01  S1F01R2B        0  135970480        0        0        0   \n",
       "...           ...       ...      ...        ...      ...      ...      ...   \n",
       "124489 2015-11-02  Z1F0MA1S        0   18310224        0        0        0   \n",
       "124490 2015-11-02  Z1F0Q8RT        0  172556680       96      107        4   \n",
       "124491 2015-11-02  Z1F0QK05        0   19029120     4832        0        0   \n",
       "124492 2015-11-02  Z1F0QL3N        0  226953408        0        0        0   \n",
       "124493 2015-11-02  Z1F0QLC1        0   17572840        0        0        0   \n",
       "\n",
       "        metric5  metric6  metric7  metric8  metric9  month  weekday  \n",
       "0             6   407438        0        0        7      1        3  \n",
       "1             6   403174        0        0        0      1        3  \n",
       "2            12   237394        0        0        0      1        3  \n",
       "3             6   410186        0        0        0      1        3  \n",
       "4            15   313173        0        0        3      1        3  \n",
       "...         ...      ...      ...      ...      ...    ...      ...  \n",
       "124489       10   353705        8        8        0     11        0  \n",
       "124490       11   332792        0        0       13     11        0  \n",
       "124491       11   350410        0        0        0     11        0  \n",
       "124492       12   358980        0        0        0     11        0  \n",
       "124493       10   351431        0        0        0     11        0  \n",
       "\n",
       "[124494 rows x 14 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f60569d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          304\n",
       "device       1169\n",
       "failure         2\n",
       "metric1    123877\n",
       "metric2       558\n",
       "metric3        47\n",
       "metric4       115\n",
       "metric5        60\n",
       "metric6     44838\n",
       "metric7        28\n",
       "metric8        28\n",
       "metric9        65\n",
       "month          11\n",
       "weekday         7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "dc551523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     25032\n",
       "3     19833\n",
       "2     19500\n",
       "4     12012\n",
       "5     11330\n",
       "7     10531\n",
       "6     10469\n",
       "8      8346\n",
       "9      4470\n",
       "10     2940\n",
       "11       31\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "328b591f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       device\n",
       "month        \n",
       "1        1164\n",
       "2         726\n",
       "3         685\n",
       "4         491\n",
       "5         424\n",
       "6         353\n",
       "7         346\n",
       "8         334\n",
       "9         184\n",
       "10        146\n",
       "11         31"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('month').agg({'device':lambda x: x.nunique()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a52aefa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='month'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('month').agg({'device':lambda x: x.nunique()}).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3e72fc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>failure</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25008</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19486</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19824</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12003</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11309</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10463</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10515</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8342</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4470</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2937</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "failure      0   1\n",
       "date              \n",
       "1        25008  24\n",
       "2        19486  14\n",
       "3        19824   9\n",
       "4        12003   9\n",
       "5        11309  21\n",
       "6        10463   6\n",
       "7        10515  16\n",
       "8         8342   4\n",
       "9         4470   0\n",
       "10        2937   3\n",
       "11          31   0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data['date'].dt.month)['failure'].value_counts().unstack(fill_value=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6bdec2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>failure</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17859</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17516</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17121</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18119</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18029</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17889</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>17855</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "failure      0   1\n",
       "weekday           \n",
       "0        17859  27\n",
       "1        17516  18\n",
       "2        17121  15\n",
       "3        18119  22\n",
       "4        18029  12\n",
       "5        17889   8\n",
       "6        17855   4"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# failure_counts_by_weekday\n",
    "data.groupby('weekday')['failure'].value_counts().unstack(fill_value=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3038cf79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device\n",
       "S1F01085   2015-01-06\n",
       "S1F013BB   2015-05-11\n",
       "S1F0166B   2015-01-06\n",
       "S1F01E6Y   2015-02-17\n",
       "S1F01JE0   2015-01-06\n",
       "              ...    \n",
       "Z1F1VMZB   2015-10-19\n",
       "Z1F1VQFY   2015-06-30\n",
       "Z1F26YZB   2015-03-25\n",
       "Z1F282ZV   2015-03-25\n",
       "Z1F2PBHX   2015-04-06\n",
       "Name: date, Length: 1169, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max_date_per_device = \n",
    "data.groupby('device')['date'].max()\n",
    "\n",
    "# print(\"Maximum date for each device:\")\n",
    "# print(max_date_per_device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1d34cdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['max_date_per_device'] = data.groupby('device')['date'].transform('max')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d7a47ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>failure</th>\n",
       "      <th>metric1</th>\n",
       "      <th>metric2</th>\n",
       "      <th>metric3</th>\n",
       "      <th>metric4</th>\n",
       "      <th>metric5</th>\n",
       "      <th>metric6</th>\n",
       "      <th>metric7</th>\n",
       "      <th>metric8</th>\n",
       "      <th>metric9</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>max_date_per_device</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>S1F01085</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>S1F0166B</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>S1F01E6Y</td>\n",
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       "      <td>3</td>\n",
       "      <td>2015-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>S1F01R2B</td>\n",
       "      <td>0</td>\n",
       "      <td>135970480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>313173</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2015-08-24</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>124489</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0MA1S</td>\n",
       "      <td>0</td>\n",
       "      <td>18310224</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124490</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0Q8RT</td>\n",
       "      <td>0</td>\n",
       "      <td>172556680</td>\n",
       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>332792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
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       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QK05</td>\n",
       "      <td>0</td>\n",
       "      <td>19029120</td>\n",
       "      <td>4832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>350410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
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       "    <tr>\n",
       "      <th>124492</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QL3N</td>\n",
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       "      <td>226953408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>358980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QLC1</td>\n",
       "      <td>0</td>\n",
       "      <td>17572840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>351431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124494 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "0      2015-01-01  S1F01085        0  215630672       56        0       52   \n",
       "1      2015-01-01  S1F0166B        0   61370680        0        3        0   \n",
       "2      2015-01-01  S1F01E6Y        0  173295968        0        0        0   \n",
       "3      2015-01-01  S1F01JE0        0   79694024        0        0        0   \n",
       "4      2015-01-01  S1F01R2B        0  135970480        0        0        0   \n",
       "...           ...       ...      ...        ...      ...      ...      ...   \n",
       "124489 2015-11-02  Z1F0MA1S        0   18310224        0        0        0   \n",
       "124490 2015-11-02  Z1F0Q8RT        0  172556680       96      107        4   \n",
       "124491 2015-11-02  Z1F0QK05        0   19029120     4832        0        0   \n",
       "124492 2015-11-02  Z1F0QL3N        0  226953408        0        0        0   \n",
       "124493 2015-11-02  Z1F0QLC1        0   17572840        0        0        0   \n",
       "\n",
       "        metric5  metric6  metric7  metric8  metric9  month  weekday  \\\n",
       "0             6   407438        0        0        7      1        3   \n",
       "1             6   403174        0        0        0      1        3   \n",
       "2            12   237394        0        0        0      1        3   \n",
       "3             6   410186        0        0        0      1        3   \n",
       "4            15   313173        0        0        3      1        3   \n",
       "...         ...      ...      ...      ...      ...    ...      ...   \n",
       "124489       10   353705        8        8        0     11        0   \n",
       "124490       11   332792        0        0       13     11        0   \n",
       "124491       11   350410        0        0        0     11        0   \n",
       "124492       12   358980        0        0        0     11        0   \n",
       "124493       10   351431        0        0        0     11        0   \n",
       "\n",
       "       max_date_per_device  \n",
       "0               2015-01-06  \n",
       "1               2015-01-06  \n",
       "2               2015-02-17  \n",
       "3               2015-01-06  \n",
       "4               2015-08-24  \n",
       "...                    ...  \n",
       "124489          2015-11-02  \n",
       "124490          2015-11-02  \n",
       "124491          2015-11-02  \n",
       "124492          2015-11-02  \n",
       "124493          2015-11-02  \n",
       "\n",
       "[124494 rows x 15 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ba66bd0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                   124494\n",
       "device                 124494\n",
       "failure                124494\n",
       "metric1                124494\n",
       "metric2                124494\n",
       "metric3                124494\n",
       "metric4                124494\n",
       "metric5                124494\n",
       "metric6                124494\n",
       "metric7                124494\n",
       "metric8                124494\n",
       "metric9                124494\n",
       "month                  124494\n",
       "weekday                124494\n",
       "max_date_per_device    124494\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63bfa0e",
   "metadata": {},
   "source": [
    "dff=df[(df.failure==1)&(df.date!=df.max_date)]\n",
    "\n",
    "dff\n",
    "\n",
    "Max date means the last day the device got checked. If the max day is ahead of failure date, it means this device returned to use after failed because got fixed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c4856a",
   "metadata": {},
   "source": [
    " ### last recorded date of telemetry for each device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8a156ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "latest_dates = data.groupby('device')['date'].transform(max)\n",
    "df1 = data[data['date'] == latest_dates].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8f7a05b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1169, 15)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0cbbb11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>device</th>\n",
       "      <th>failure</th>\n",
       "      <th>metric1</th>\n",
       "      <th>metric2</th>\n",
       "      <th>metric3</th>\n",
       "      <th>metric4</th>\n",
       "      <th>metric5</th>\n",
       "      <th>metric6</th>\n",
       "      <th>metric7</th>\n",
       "      <th>metric8</th>\n",
       "      <th>metric9</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>max_date_per_device</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>W1F0WJFT</td>\n",
       "      <td>0</td>\n",
       "      <td>142903640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>217819</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3516</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>S1F04KSC</td>\n",
       "      <td>0</td>\n",
       "      <td>243217648</td>\n",
       "      <td>392</td>\n",
       "      <td>24929</td>\n",
       "      <td>529</td>\n",
       "      <td>3</td>\n",
       "      <td>339302</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10137</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4673</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F03RV3</td>\n",
       "      <td>0</td>\n",
       "      <td>13723560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>263330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F05NAJ</td>\n",
       "      <td>0</td>\n",
       "      <td>201934440</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>341352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F05P51</td>\n",
       "      <td>0</td>\n",
       "      <td>12164440</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>335898</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124489</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0MA1S</td>\n",
       "      <td>0</td>\n",
       "      <td>18310224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>353705</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124490</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0Q8RT</td>\n",
       "      <td>0</td>\n",
       "      <td>172556680</td>\n",
       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>332792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QK05</td>\n",
       "      <td>0</td>\n",
       "      <td>19029120</td>\n",
       "      <td>4832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>350410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124492</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QL3N</td>\n",
       "      <td>0</td>\n",
       "      <td>226953408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>358980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>2015-11-02</td>\n",
       "      <td>Z1F0QLC1</td>\n",
       "      <td>0</td>\n",
       "      <td>17572840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>351431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-11-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "3051   2015-01-03  W1F0WJFT        0  142903640        0        0        0   \n",
       "3516   2015-01-04  S1F04KSC        0  243217648      392    24929      529   \n",
       "4673   2015-01-05  S1F03RV3        0   13723560        0        0        0   \n",
       "4685   2015-01-05  S1F05NAJ        0  201934440      392        0        0   \n",
       "4686   2015-01-05  S1F05P51        0   12164440      512        0        0   \n",
       "...           ...       ...      ...        ...      ...      ...      ...   \n",
       "124489 2015-11-02  Z1F0MA1S        0   18310224        0        0        0   \n",
       "124490 2015-11-02  Z1F0Q8RT        0  172556680       96      107        4   \n",
       "124491 2015-11-02  Z1F0QK05        0   19029120     4832        0        0   \n",
       "124492 2015-11-02  Z1F0QL3N        0  226953408        0        0        0   \n",
       "124493 2015-11-02  Z1F0QLC1        0   17572840        0        0        0   \n",
       "\n",
       "        metric5  metric6  metric7  metric8  metric9  month  weekday  \\\n",
       "3051          9   217819        0        0        1      1        5   \n",
       "3516          3   339302        0        0    10137      1        6   \n",
       "4673          8   263330        0        0        0      1        0   \n",
       "4685          4   341352        0        0        1      1        0   \n",
       "4686          5   335898        0        0        0      1        0   \n",
       "...         ...      ...      ...      ...      ...    ...      ...   \n",
       "124489       10   353705        8        8        0     11        0   \n",
       "124490       11   332792        0        0       13     11        0   \n",
       "124491       11   350410        0        0        0     11        0   \n",
       "124492       12   358980        0        0        0     11        0   \n",
       "124493       10   351431        0        0        0     11        0   \n",
       "\n",
       "       max_date_per_device  \n",
       "3051            2015-01-03  \n",
       "3516            2015-01-04  \n",
       "4673            2015-01-05  \n",
       "4685            2015-01-05  \n",
       "4686            2015-01-05  \n",
       "...                    ...  \n",
       "124489          2015-11-02  \n",
       "124490          2015-11-02  \n",
       "124491          2015-11-02  \n",
       "124492          2015-11-02  \n",
       "124493          2015-11-02  \n",
       "\n",
       "[1169 rows x 15 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6155d58f",
   "metadata": {},
   "source": [
    "#### Create feature called 'failure_before'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75d434b",
   "metadata": {},
   "source": [
    "If we just take the last record for the devices, we may lose information from those come back after failed ones\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3847d150",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77958</th>\n",
       "      <td>2015-05-05</td>\n",
       "      <td>S1F136J0</td>\n",
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       "    <tr>\n",
       "      <th>79590</th>\n",
       "      <td>2015-05-09</td>\n",
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       "    <tr>\n",
       "      <th>79601</th>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>W1F0M35B</td>\n",
       "      <td>1</td>\n",
       "      <td>141805096</td>\n",
       "      <td>0</td>\n",
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       "      <td>267203</td>\n",
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       "      <td>2015-05-11</td>\n",
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       "    <tr>\n",
       "      <th>101999</th>\n",
       "      <td>2015-07-12</td>\n",
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       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104223</th>\n",
       "      <td>2015-07-18</td>\n",
       "      <td>W1F11ZG9</td>\n",
       "      <td>1</td>\n",
       "      <td>32671632</td>\n",
       "      <td>368</td>\n",
       "      <td>9</td>\n",
       "      <td>486</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-08-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "77958  2015-05-05  S1F136J0        1  131519808        0        0       25   \n",
       "79590  2015-05-09  W1F0KCP2        1  176861128    10392        0      121   \n",
       "79601  2015-05-09  W1F0M35B        1  141805096        0        0        1   \n",
       "101999 2015-07-12  S1F0GPFZ        1  236544080        0        0        0   \n",
       "104223 2015-07-18  W1F11ZG9        1   32671632      368        9      486   \n",
       "\n",
       "        metric5  metric6  metric7  metric8  metric9  month  weekday  \\\n",
       "77958        16   343464        8        8        0      5        1   \n",
       "79590         6   310699        0        0        0      5        5   \n",
       "79601         7   267203        0        0        0      5        5   \n",
       "101999        9   264594        0        0        0      7        6   \n",
       "104223        3   344433        0        0        1      7        5   \n",
       "\n",
       "       max_date_per_device  \n",
       "77958           2015-05-06  \n",
       "79590           2015-05-11  \n",
       "79601           2015-05-11  \n",
       "101999          2015-07-24  \n",
       "104223          2015-08-17  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "potential_fixed_devices = data[(data['failure'] == 1) & (data['date'] != data['max_date_per_device'])]\n",
    "potential_fixed_devices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "62de7cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[:, 'failure_before'] = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "85d65844",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[df1.device == 'S1F136J0','failure_before'] = 1\n",
    "df1.loc[df1.device == 'W1F0KCP2','failure_before'] = 1\n",
    "df1.loc[df1.device == 'W1F0M35B','failure_before'] = 1\n",
    "df1.loc[df1.device == 'S1F0GPFZ','failure_before'] = 1\n",
    "df1.loc[df1.device == 'W1F11ZG9','failure_before'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "63bb69f3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4673</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F03RV3</td>\n",
       "      <td>0</td>\n",
       "      <td>13723560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>263330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F05NAJ</td>\n",
       "      <td>0</td>\n",
       "      <td>201934440</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>341352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F05P51</td>\n",
       "      <td>0</td>\n",
       "      <td>12164440</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>335898</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4687</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F05P5Z</td>\n",
       "      <td>0</td>\n",
       "      <td>56574384</td>\n",
       "      <td>936</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>335893</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4692</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F08R94</td>\n",
       "      <td>0</td>\n",
       "      <td>16377144</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>330296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4693</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F08S38</td>\n",
       "      <td>0</td>\n",
       "      <td>120628960</td>\n",
       "      <td>760</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>338658</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4695</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F09MAK</td>\n",
       "      <td>0</td>\n",
       "      <td>97346208</td>\n",
       "      <td>8392</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>306534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4696</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>S1F09MSM</td>\n",
       "      <td>0</td>\n",
       "      <td>234536728</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>343127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    device  failure    metric1  metric2  metric3  metric4  \\\n",
       "3051 2015-01-03  W1F0WJFT        0  142903640        0        0        0   \n",
       "3516 2015-01-04  S1F04KSC        0  243217648      392    24929      529   \n",
       "4673 2015-01-05  S1F03RV3        0   13723560        0        0        0   \n",
       "4685 2015-01-05  S1F05NAJ        0  201934440      392        0        0   \n",
       "4686 2015-01-05  S1F05P51        0   12164440      512        0        0   \n",
       "4687 2015-01-05  S1F05P5Z        0   56574384      936        0        3   \n",
       "4692 2015-01-05  S1F08R94        0   16377144        0        1        0   \n",
       "4693 2015-01-05  S1F08S38        0  120628960      760        3        2   \n",
       "4695 2015-01-05  S1F09MAK        0   97346208     8392        0        7   \n",
       "4696 2015-01-05  S1F09MSM        0  234536728      360        0        0   \n",
       "\n",
       "      metric5  metric6  metric7  metric8  metric9  month  weekday  \\\n",
       "3051        9   217819        0        0        1      1        5   \n",
       "3516        3   339302        0        0    10137      1        6   \n",
       "4673        8   263330        0        0        0      1        0   \n",
       "4685        4   341352        0        0        1      1        0   \n",
       "4686        5   335898        0        0        0      1        0   \n",
       "4687        4   335893        0        0        0      1        0   \n",
       "4692        4   330296        0        0        0      1        0   \n",
       "4693        4   338658        0        0        6      1        0   \n",
       "4695        3   306534        0        0        4      1        0   \n",
       "4696        3   343127        0        0        2      1        0   \n",
       "\n",
       "     max_date_per_device  failure_before  \n",
       "3051          2015-01-03               0  \n",
       "3516          2015-01-04               0  \n",
       "4673          2015-01-05               0  \n",
       "4685          2015-01-05               0  \n",
       "4686          2015-01-05               0  \n",
       "4687          2015-01-05               0  \n",
       "4692          2015-01-05               0  \n",
       "4693          2015-01-05               0  \n",
       "4695          2015-01-05               0  \n",
       "4696          2015-01-05               0  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b185b5",
   "metadata": {},
   "source": [
    "## data transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7823c331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1169 entries, 3051 to 124493\n",
      "Data columns (total 16 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 1169 non-null   datetime64[ns]\n",
      " 1   device               1169 non-null   object        \n",
      " 2   failure              1169 non-null   int64         \n",
      " 3   metric1              1169 non-null   int64         \n",
      " 4   metric2              1169 non-null   int64         \n",
      " 5   metric3              1169 non-null   int64         \n",
      " 6   metric4              1169 non-null   int64         \n",
      " 7   metric5              1169 non-null   int64         \n",
      " 8   metric6              1169 non-null   int64         \n",
      " 9   metric7              1169 non-null   int64         \n",
      " 10  metric8              1169 non-null   int64         \n",
      " 11  metric9              1169 non-null   int64         \n",
      " 12  month                1169 non-null   int64         \n",
      " 13  weekday              1169 non-null   int64         \n",
      " 14  max_date_per_device  1169 non-null   datetime64[ns]\n",
      " 15  failure_before       1169 non-null   int64         \n",
      "dtypes: datetime64[ns](2), int64(13), object(1)\n",
      "memory usage: 155.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ac1aec29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           failure       metric1       metric2       metric3      metric4  \\\n",
      "count  1169.000000  1.169000e+03   1169.000000   1169.000000  1169.000000   \n",
      "mean      0.086399  1.221612e+08    872.260051     31.243798     9.808383   \n",
      "std       0.281072  7.040220e+07   5990.879345    738.252029    78.946049   \n",
      "min       0.000000  0.000000e+00      0.000000      0.000000     0.000000   \n",
      "25%       0.000000  5.972926e+07      0.000000      0.000000     0.000000   \n",
      "50%       0.000000  1.222921e+08      0.000000      0.000000     0.000000   \n",
      "75%       0.000000  1.826172e+08      0.000000      0.000000     0.000000   \n",
      "max       1.000000  2.433294e+08  64792.000000  24929.000000  1666.000000   \n",
      "\n",
      "           metric5        metric6      metric7      metric8       metric9  \\\n",
      "count  1169.000000    1169.000000  1169.000000  1169.000000   1169.000000   \n",
      "mean     12.444825  269958.996578     4.443114     4.443114     49.230967   \n",
      "std      12.366156  104615.250090    43.725859    43.725859    678.117966   \n",
      "min       1.000000      12.000000     0.000000     0.000000      0.000000   \n",
      "25%       7.000000  212306.000000     0.000000     0.000000      0.000000   \n",
      "50%       9.000000  261020.000000     0.000000     0.000000      0.000000   \n",
      "75%      12.000000  328098.000000     0.000000     0.000000      1.000000   \n",
      "max      98.000000  689161.000000   832.000000   832.000000  18701.000000   \n",
      "\n",
      "             month      weekday  failure_before  \n",
      "count  1169.000000  1169.000000     1169.000000  \n",
      "mean      4.303678     1.522669        0.004277  \n",
      "std       3.348379     1.429762        0.065288  \n",
      "min       1.000000     0.000000        0.000000  \n",
      "25%       1.000000     0.000000        0.000000  \n",
      "50%       3.000000     1.000000        0.000000  \n",
      "75%       8.000000     3.000000        0.000000  \n",
      "max      11.000000     6.000000        1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(df1.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b2c0da2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failure Counts:\n",
      "0    1068\n",
      "1     101\n",
      "Name: failure, dtype: int64\n",
      "\n",
      "Failure Percentages:\n",
      "0    0.857873\n",
      "1    0.081128\n",
      "Name: failure, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "failure_counts = df1[\"failure\"].value_counts()\n",
    "failure_percentages = failure_counts / len(data) * 100\n",
    "\n",
    "# Print the results\n",
    "print(\"Failure Counts:\")\n",
    "print(failure_counts)\n",
    "print(\"\\nFailure Percentages:\")\n",
    "print(failure_percentages)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3bb4d07a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3051      W1F0WJFT\n",
       "3516      S1F04KSC\n",
       "4673      S1F03RV3\n",
       "4685      S1F05NAJ\n",
       "4686      S1F05P51\n",
       "            ...   \n",
       "124489    Z1F0MA1S\n",
       "124490    Z1F0Q8RT\n",
       "124491    Z1F0QK05\n",
       "124492    Z1F0QL3N\n",
       "124493    Z1F0QLC1\n",
       "Name: device, Length: 1169, dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6cafbefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Id = df1.device.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "cf987541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S1F0    391\n",
       "W1F0    282\n",
       "Z1F0    149\n",
       "S1F1    139\n",
       "W1F1    138\n",
       "Z1F1     67\n",
       "Z1F2      3\n",
       "Name: device, dtype: int64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change device id values to the first four characters\n",
    "Id1 = [] \n",
    "for i in Id:\n",
    "    i = i[:4]\n",
    "    Id1.append(i)\n",
    "\n",
    "df1.device=Id1\n",
    "\n",
    "df1.device.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f8a33c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "dev=pd.crosstab(df1['device'],df1['failure']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4808441d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot\n",
    "dev.plot(kind=\"bar\", stacked=True)\n",
    "\n",
    "# Customize the plot (optional)\n",
    "plt.xlabel('Device Category')\n",
    "plt.ylabel('Normalized Failure Count (0-1)')\n",
    "plt.title('Normalized Failure Counts per Device Category')\n",
    "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
    "plt.legend(title='Failure')  # Add legend with title 'Failure'\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "48c98da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                    104\n",
       "device                    7\n",
       "failure                   2\n",
       "metric1                1168\n",
       "metric2                 118\n",
       "metric3                  44\n",
       "metric4                  67\n",
       "metric5                  58\n",
       "metric6                1135\n",
       "metric7                  22\n",
       "metric8                  22\n",
       "metric9                  63\n",
       "month                    11\n",
       "weekday                   7\n",
       "max_date_per_device     104\n",
       "failure_before            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c24e555",
   "metadata": {},
   "source": [
    "- droping unimportant and redundant data\n",
    "- data transformation \n",
    "- standardisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3c146b",
   "metadata": {},
   "source": [
    "as we have seen earlier that metrics7 and metric8 is highly correlated so we have to drop one of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7b850e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1169\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df1['metric7']==df1['metric8']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "043fb1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop('metric8',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2397fabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['date','max_date_per_device'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b757ca8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device</th>\n",
       "      <th>failure</th>\n",
       "      <th>metric1</th>\n",
       "      <th>metric2</th>\n",
       "      <th>metric3</th>\n",
       "      <th>metric4</th>\n",
       "      <th>metric5</th>\n",
       "      <th>metric6</th>\n",
       "      <th>metric7</th>\n",
       "      <th>metric9</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>failure_before</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3051</th>\n",
       "      <td>W1F0</td>\n",
       "      <td>0</td>\n",
       "      <td>142903640</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>217819</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3516</th>\n",
       "      <td>S1F0</td>\n",
       "      <td>0</td>\n",
       "      <td>243217648</td>\n",
       "      <td>392</td>\n",
       "      <td>24929</td>\n",
       "      <td>529</td>\n",
       "      <td>3</td>\n",
       "      <td>339302</td>\n",
       "      <td>0</td>\n",
       "      <td>10137</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4673</th>\n",
       "      <td>S1F0</td>\n",
       "      <td>0</td>\n",
       "      <td>13723560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>263330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>S1F0</td>\n",
       "      <td>0</td>\n",
       "      <td>201934440</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>341352</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>S1F0</td>\n",
       "      <td>0</td>\n",
       "      <td>12164440</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>335898</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     device  failure    metric1  metric2  metric3  metric4  metric5  metric6  \\\n",
       "3051   W1F0        0  142903640        0        0        0        9   217819   \n",
       "3516   S1F0        0  243217648      392    24929      529        3   339302   \n",
       "4673   S1F0        0   13723560        0        0        0        8   263330   \n",
       "4685   S1F0        0  201934440      392        0        0        4   341352   \n",
       "4686   S1F0        0   12164440      512        0        0        5   335898   \n",
       "\n",
       "      metric7  metric9  month  weekday  failure_before  \n",
       "3051        0        1      1        5               0  \n",
       "3516        0    10137      1        6               0  \n",
       "4673        0        0      1        0               0  \n",
       "4685        0        1      1        0               0  \n",
       "4686        0        0      1        0               0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c23b1102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1169 entries, 3051 to 124493\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   device          1169 non-null   object\n",
      " 1   failure         1169 non-null   int64 \n",
      " 2   metric1         1169 non-null   int64 \n",
      " 3   metric2         1169 non-null   int64 \n",
      " 4   metric3         1169 non-null   int64 \n",
      " 5   metric4         1169 non-null   int64 \n",
      " 6   metric5         1169 non-null   int64 \n",
      " 7   metric6         1169 non-null   int64 \n",
      " 8   metric7         1169 non-null   int64 \n",
      " 9   metric9         1169 non-null   int64 \n",
      " 10  month           1169 non-null   int64 \n",
      " 11  weekday         1169 non-null   int64 \n",
      " 12  failure_before  1169 non-null   int64 \n",
      "dtypes: int64(12), object(1)\n",
      "memory usage: 127.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3aa5ecf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['month','weekday']:\n",
    "    df1[col]=df1[col].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "903e5da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1169 entries, 3051 to 124493\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   device          1169 non-null   object\n",
      " 1   failure         1169 non-null   int64 \n",
      " 2   metric1         1169 non-null   int64 \n",
      " 3   metric2         1169 non-null   int64 \n",
      " 4   metric3         1169 non-null   int64 \n",
      " 5   metric4         1169 non-null   int64 \n",
      " 6   metric5         1169 non-null   int64 \n",
      " 7   metric6         1169 non-null   int64 \n",
      " 8   metric7         1169 non-null   int64 \n",
      " 9   metric9         1169 non-null   int64 \n",
      " 10  month           1169 non-null   object\n",
      " 11  weekday         1169 non-null   object\n",
      " 12  failure_before  1169 non-null   int64 \n",
      "dtypes: int64(10), object(3)\n",
      "memory usage: 127.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a049611b",
   "metadata": {},
   "source": [
    "## Getting dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c57ae323",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_19192\\842490473.py:2: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  df1 = pd.get_dummies(df1, drop_first=True)\n",
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_19192\\842490473.py:2: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  df1 = pd.get_dummies(df1, drop_first=True)\n"
     ]
    }
   ],
   "source": [
    "# Convert categorical variables into dummy variables\n",
    "df1 = pd.get_dummies(df1, drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c3a93d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>failure</th>\n",
       "      <th>metric1</th>\n",
       "      <th>metric2</th>\n",
       "      <th>metric3</th>\n",
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       "      <th>metric5</th>\n",
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       "      <th>metric7</th>\n",
       "      <th>metric9</th>\n",
       "      <th>failure_before</th>\n",
       "      <th>...</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>0</td>\n",
       "      <td>201934440</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>341352</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>10</td>\n",
       "      <td>353705</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>124490</th>\n",
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       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>332792</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>0</td>\n",
       "      <td>19029120</td>\n",
       "      <td>4832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>350410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>12</td>\n",
       "      <td>358980</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>0</td>\n",
       "      <td>17572840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>351431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        failure    metric1  metric2  metric3  metric4  metric5  metric6  \\\n",
       "3051          0  142903640        0        0        0        9   217819   \n",
       "3516          0  243217648      392    24929      529        3   339302   \n",
       "4673          0   13723560        0        0        0        8   263330   \n",
       "4685          0  201934440      392        0        0        4   341352   \n",
       "4686          0   12164440      512        0        0        5   335898   \n",
       "...         ...        ...      ...      ...      ...      ...      ...   \n",
       "124489        0   18310224        0        0        0       10   353705   \n",
       "124490        0  172556680       96      107        4       11   332792   \n",
       "124491        0   19029120     4832        0        0       11   350410   \n",
       "124492        0  226953408        0        0        0       12   358980   \n",
       "124493        0   17572840        0        0        0       10   351431   \n",
       "\n",
       "        metric7  metric9  failure_before  ...  month_8  month_9  month_10  \\\n",
       "3051          0        1               0  ...        0        0         0   \n",
       "3516          0    10137               0  ...        0        0         0   \n",
       "4673          0        0               0  ...        0        0         0   \n",
       "4685          0        1               0  ...        0        0         0   \n",
       "4686          0        0               0  ...        0        0         0   \n",
       "...         ...      ...             ...  ...      ...      ...       ...   \n",
       "124489        8        0               0  ...        0        0         0   \n",
       "124490        0       13               0  ...        0        0         0   \n",
       "124491        0        0               0  ...        0        0         0   \n",
       "124492        0        0               0  ...        0        0         0   \n",
       "124493        0        0               0  ...        0        0         0   \n",
       "\n",
       "        month_11  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  \\\n",
       "3051           0          0          0          0          0          1   \n",
       "3516           0          0          0          0          0          0   \n",
       "4673           0          0          0          0          0          0   \n",
       "4685           0          0          0          0          0          0   \n",
       "4686           0          0          0          0          0          0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "124489         1          0          0          0          0          0   \n",
       "124490         1          0          0          0          0          0   \n",
       "124491         1          0          0          0          0          0   \n",
       "124492         1          0          0          0          0          0   \n",
       "124493         1          0          0          0          0          0   \n",
       "\n",
       "        weekday_6  \n",
       "3051            0  \n",
       "3516            1  \n",
       "4673            0  \n",
       "4685            0  \n",
       "4686            0  \n",
       "...           ...  \n",
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       "\n",
       "[1169 rows x 32 columns]"
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     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2581930",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1b4a5915",
   "metadata": {},
   "source": [
    "## Feature Scaling Or Standardization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "6242f0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate dependent (y) and independent features (X)\n",
    "X = df1.drop('failure', axis=1)\n",
    "y = df1['failure']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "83f30c06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3051      0\n",
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       "         ..\n",
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       "Name: failure, Length: 1169, dtype: int64"
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     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y"
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  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "6923991e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only numeric columns for metrics 1, 2, 3, 4, 5, 6, 7, and 9\n",
    "numeric_cols = ['metric1', 'metric2', 'metric3', 'metric4', 'metric5', 'metric6', 'metric7', 'metric9']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "96db0639",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store a copy of X before scaling\n",
    "X_before_scaling = X.copy()\n",
    "\n",
    "# Apply StandardScaler only to selected numeric columns\n",
    "scaler = StandardScaler()\n",
    "X.loc[:, numeric_cols] = scaler.fit_transform(X.loc[:, numeric_cols])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ed569a95",
   "metadata": {},
   "outputs": [
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       "         metric1   metric2    metric3   metric4   metric5   metric6   metric7  \\\n",
       "3051    0.294754 -0.145660  -0.042339 -0.124295 -0.278688 -0.498611 -0.101656   \n",
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       "\n",
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       "      <td>0</td>\n",
       "      <td>0</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>4673</th>\n",
       "      <td>13723560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>263330</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>201934440</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>341352</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>12164440</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>335898</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>124489</th>\n",
       "      <td>18310224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>353705</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124490</th>\n",
       "      <td>172556680</td>\n",
       "      <td>96</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>332792</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>19029120</td>\n",
       "      <td>4832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>350410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124492</th>\n",
       "      <td>226953408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>358980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>17572840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>351431</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          metric1  metric2  metric3  metric4  metric5  metric6  metric7  \\\n",
       "3051    142903640        0        0        0        9   217819        0   \n",
       "3516    243217648      392    24929      529        3   339302        0   \n",
       "4673     13723560        0        0        0        8   263330        0   \n",
       "4685    201934440      392        0        0        4   341352        0   \n",
       "4686     12164440      512        0        0        5   335898        0   \n",
       "...           ...      ...      ...      ...      ...      ...      ...   \n",
       "124489   18310224        0        0        0       10   353705        8   \n",
       "124490  172556680       96      107        4       11   332792        0   \n",
       "124491   19029120     4832        0        0       11   350410        0   \n",
       "124492  226953408        0        0        0       12   358980        0   \n",
       "124493   17572840        0        0        0       10   351431        0   \n",
       "\n",
       "        metric9  failure_before  device_S1F1  ...  month_8  month_9  month_10  \\\n",
       "3051          1               0            0  ...        0        0         0   \n",
       "3516      10137               0            0  ...        0        0         0   \n",
       "4673          0               0            0  ...        0        0         0   \n",
       "4685          1               0            0  ...        0        0         0   \n",
       "4686          0               0            0  ...        0        0         0   \n",
       "...         ...             ...          ...  ...      ...      ...       ...   \n",
       "124489        0               0            0  ...        0        0         0   \n",
       "124490       13               0            0  ...        0        0         0   \n",
       "124491        0               0            0  ...        0        0         0   \n",
       "124492        0               0            0  ...        0        0         0   \n",
       "124493        0               0            0  ...        0        0         0   \n",
       "\n",
       "        month_11  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  \\\n",
       "3051           0          0          0          0          0          1   \n",
       "3516           0          0          0          0          0          0   \n",
       "4673           0          0          0          0          0          0   \n",
       "4685           0          0          0          0          0          0   \n",
       "4686           0          0          0          0          0          0   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "124489         1          0          0          0          0          0   \n",
       "124490         1          0          0          0          0          0   \n",
       "124491         1          0          0          0          0          0   \n",
       "124492         1          0          0          0          0          0   \n",
       "124493         1          0          0          0          0          0   \n",
       "\n",
       "        weekday_6  \n",
       "3051            0  \n",
       "3516            1  \n",
       "4673            0  \n",
       "4685            0  \n",
       "4686            0  \n",
       "...           ...  \n",
       "124489          0  \n",
       "124490          0  \n",
       "124491          0  \n",
       "124492          0  \n",
       "124493          0  \n",
       "\n",
       "[1169 rows x 31 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_before_scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434fc641",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "805942c4",
   "metadata": {},
   "source": [
    "## Box Plots To understand Effect Of Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2a39b165",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_19192\\294943947.py:3: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
      "  plt.subplot(1, 2, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot boxplots to visualize the changes\n",
    "plt.subplots(figsize=(15, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=X_before_scaling[numeric_cols])\n",
    "plt.title('X Before Scaling')\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=X[numeric_cols])\n",
    "plt.title('X After Scaling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec22553",
   "metadata": {},
   "source": [
    "## feature selection\n",
    "\n",
    "### Feature Importance\n",
    "You can get the feature importance of each feature of your dataset by using the feature importance property of the model.\n",
    "\n",
    "Feature importance gives you a score for each feature of your data, the higher the score more important or relevant is the feature towards your output variable.\n",
    "\n",
    "Feature importance is an inbuilt class that comes with Tree Based Classifiers, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cc6e11f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importances:\n",
      "[0.04163597 0.05938838 0.00864888 0.07642054 0.07252477 0.06087038\n",
      " 0.10889378 0.02096554 0.00633228 0.00481459 0.01237736 0.01351884\n",
      " 0.01088021 0.00947169 0.00013234 0.0448647  0.02452234 0.01845368\n",
      " 0.03754756 0.04368679 0.10247915 0.02006606 0.00325362 0.01049451\n",
      " 0.00347076 0.06582596 0.03114709 0.03817949 0.02367421 0.01796421\n",
      " 0.00749431]\n"
     ]
    }
   ],
   "source": [
    "# Instantiate ExtraTreesClassifier\n",
    "model = ExtraTreesClassifier()\n",
    "\n",
    "# Fit the model to your data\n",
    "model.fit(X, y)\n",
    "\n",
    "# Display feature importances\n",
    "print(\"Feature Importances:\")\n",
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "d5ab689e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot graph of feature importances for better visualization\n",
    "plt.figure(figsize=(10, 8))  \n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.sort_values(ascending=False).plot(kind='barh')\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f2b94da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set threshold value for selecting important features\n",
    "threshold = 0.005  # Adjust this value as needed\n",
    "\n",
    "# Select only important features based on the threshold\n",
    "important_features = feat_importances[feat_importances > threshold].index.tolist()\n",
    "\n",
    "# Filter the original feature set X to include only the important features\n",
    "X_selected = X[important_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "05367a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4685</th>\n",
       "      <td>1.133592</td>\n",
       "      <td>-0.080200</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.683190</td>\n",
       "      <td>0.682726</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.071155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4686</th>\n",
       "      <td>-1.563074</td>\n",
       "      <td>-0.060160</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.602290</td>\n",
       "      <td>0.630570</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.072630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>124489</th>\n",
       "      <td>-1.475741</td>\n",
       "      <td>-0.145660</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.197787</td>\n",
       "      <td>0.800857</td>\n",
       "      <td>0.081380</td>\n",
       "      <td>-0.072630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124490</th>\n",
       "      <td>0.716129</td>\n",
       "      <td>-0.129629</td>\n",
       "      <td>0.102660</td>\n",
       "      <td>-0.073606</td>\n",
       "      <td>-0.116887</td>\n",
       "      <td>0.600867</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.053452</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124491</th>\n",
       "      <td>-1.465526</td>\n",
       "      <td>0.661244</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.116887</td>\n",
       "      <td>0.769347</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.072630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124492</th>\n",
       "      <td>1.489116</td>\n",
       "      <td>-0.145660</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.035987</td>\n",
       "      <td>0.851301</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.072630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124493</th>\n",
       "      <td>-1.486220</td>\n",
       "      <td>-0.145660</td>\n",
       "      <td>-0.042339</td>\n",
       "      <td>-0.124295</td>\n",
       "      <td>-0.197787</td>\n",
       "      <td>0.779111</td>\n",
       "      <td>-0.101656</td>\n",
       "      <td>-0.072630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         metric1   metric2    metric3   metric4   metric5   metric6   metric7  \\\n",
       "3051    0.294754 -0.145660  -0.042339 -0.124295 -0.278688 -0.498611 -0.101656   \n",
       "3516    1.720234 -0.080200  33.739714  6.579352 -0.764091  0.663122 -0.101656   \n",
       "4673   -1.540918 -0.145660  -0.042339 -0.124295 -0.359588 -0.063393 -0.101656   \n",
       "4685    1.133592 -0.080200  -0.042339 -0.124295 -0.683190  0.682726 -0.101656   \n",
       "4686   -1.563074 -0.060160  -0.042339 -0.124295 -0.602290  0.630570 -0.101656   \n",
       "...          ...       ...        ...       ...       ...       ...       ...   \n",
       "124489 -1.475741 -0.145660  -0.042339 -0.124295 -0.197787  0.800857  0.081380   \n",
       "124490  0.716129 -0.129629   0.102660 -0.073606 -0.116887  0.600867 -0.101656   \n",
       "124491 -1.465526  0.661244  -0.042339 -0.124295 -0.116887  0.769347 -0.101656   \n",
       "124492  1.489116 -0.145660  -0.042339 -0.124295 -0.035987  0.851301 -0.101656   \n",
       "124493 -1.486220 -0.145660  -0.042339 -0.124295 -0.197787  0.779111 -0.101656   \n",
       "\n",
       "          metric9  failure_before  device_W1F0  ...  month_6  month_7  \\\n",
       "3051    -0.071155               0            1  ...        0        0   \n",
       "3516    14.882494               0            0  ...        0        0   \n",
       "4673    -0.072630               0            0  ...        0        0   \n",
       "4685    -0.071155               0            0  ...        0        0   \n",
       "4686    -0.072630               0            0  ...        0        0   \n",
       "...           ...             ...          ...  ...      ...      ...   \n",
       "124489  -0.072630               0            0  ...        0        0   \n",
       "124490  -0.053452               0            0  ...        0        0   \n",
       "124491  -0.072630               0            0  ...        0        0   \n",
       "124492  -0.072630               0            0  ...        0        0   \n",
       "124493  -0.072630               0            0  ...        0        0   \n",
       "\n",
       "        month_8  month_10  weekday_1  weekday_2  weekday_3  weekday_4  \\\n",
       "3051          0         0          0          0          0          0   \n",
       "3516          0         0          0          0          0          0   \n",
       "4673          0         0          0          0          0          0   \n",
       "4685          0         0          0          0          0          0   \n",
       "4686          0         0          0          0          0          0   \n",
       "...         ...       ...        ...        ...        ...        ...   \n",
       "124489        0         0          0          0          0          0   \n",
       "124490        0         0          0          0          0          0   \n",
       "124491        0         0          0          0          0          0   \n",
       "124492        0         0          0          0          0          0   \n",
       "124493        0         0          0          0          0          0   \n",
       "\n",
       "        weekday_5  weekday_6  \n",
       "3051            1          0  \n",
       "3516            0          1  \n",
       "4673            0          0  \n",
       "4685            0          0  \n",
       "4686            0          0  \n",
       "...           ...        ...  \n",
       "124489          0          0  \n",
       "124490          0          0  \n",
       "124491          0          0  \n",
       "124492          0          0  \n",
       "124493          0          0  \n",
       "\n",
       "[1169 rows x 27 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_selected"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6bf441d",
   "metadata": {},
   "source": [
    "after selecting only important features preformance of model degrades so i select all the features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53db2571",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "221d142c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split using the selected important features\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "b1c9c728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((876, 31), (293, 31))"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "09835598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "===================\n",
      "Training Set Performance:\n",
      "Accuracy: 0.934931506849315\n",
      "Precision: 0.8666666666666667\n",
      "Recall: 0.3291139240506329\n",
      "F1-score: 0.47706422018348627\n",
      "\n",
      "\n",
      "Test Set Performance:\n",
      "Accuracy: 0.9419795221843004\n",
      "Precision: 0.8571428571428571\n",
      "Recall: 0.2727272727272727\n",
      "F1-score: 0.41379310344827586\n",
      "\n",
      "\n",
      "Random Forest\n",
      "=============\n",
      "Training Set Performance:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-score: 1.0\n",
      "\n",
      "\n",
      "Test Set Performance:\n",
      "Accuracy: 0.9590443686006825\n",
      "Precision: 0.9166666666666666\n",
      "Recall: 0.5\n",
      "F1-score: 0.6470588235294118\n",
      "\n",
      "\n",
      "XGBoost\n",
      "=======\n",
      "Training Set Performance:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1-score: 1.0\n",
      "\n",
      "\n",
      "Test Set Performance:\n",
      "Accuracy: 0.9488054607508533\n",
      "Precision: 0.7692307692307693\n",
      "Recall: 0.45454545454545453\n",
      "F1-score: 0.5714285714285714\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define the models\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"XGBoost\": xgb.XGBClassifier()\n",
    "}\n",
    "\n",
    "# Loop through each model\n",
    "for model_name, model in models.items():\n",
    "    # Train the model\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predictions on training and test set\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    \n",
    "    # Print model name\n",
    "    print(model_name)\n",
    "    print(\"=\" * len(model_name))\n",
    "    \n",
    "    # Training set performance\n",
    "    print(\"Training Set Performance:\")\n",
    "    print(\"Accuracy:\", accuracy_score(y_train, y_train_pred))\n",
    "    print(\"Precision:\", precision_score(y_train, y_train_pred))\n",
    "    print(\"Recall:\", recall_score(y_train, y_train_pred))\n",
    "    print(\"F1-score:\", f1_score(y_train, y_train_pred))\n",
    "   \n",
    "    print(\"\\n\")\n",
    "    \n",
    "    # Test set performance\n",
    "    print(\"Test Set Performance:\")\n",
    "    print(\"Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
    "    print(\"Precision:\", precision_score(y_test, y_test_pred))\n",
    "    print(\"Recall:\", recall_score(y_test, y_test_pred))\n",
    "    print(\"F1-score:\", f1_score(y_test, y_test_pred))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "04947e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "def evaluate_models(X_train, y_train, X_test, y_test):\n",
    "    # Define the models\n",
    "    logistic_model = LogisticRegression(random_state=42)\n",
    "    random_forest_model = RandomForestClassifier(random_state=42)\n",
    "    xgb_model = xgb.XGBClassifier(random_state=42)\n",
    "\n",
    "    # Train the models\n",
    "    logistic_model.fit(X_train, y_train)\n",
    "    random_forest_model.fit(X_train, y_train)\n",
    "    xgb_model.fit(X_train, y_train)\n",
    "\n",
    "    # Define a dictionary of models\n",
    "    models = {\n",
    "        \"Logistic Regression\": logistic_model,\n",
    "        \"Random Forest\": random_forest_model,\n",
    "        \"XGBoost\": xgb_model\n",
    "    }\n",
    "    \n",
    "    # Loop through each model\n",
    "    for model_name, model in models.items():\n",
    "        # Make predictions\n",
    "        predictions = model.predict(X_test)\n",
    "\n",
    "        # Calculate TP, FN, FP, TN\n",
    "        TP = sum((predictions == 1) & (y_test == 1))\n",
    "        FN = sum((predictions == 0) & (y_test == 1))\n",
    "        FP = sum((predictions == 1) & (y_test == 0))\n",
    "        TN = sum((predictions == 0) & (y_test == 0))\n",
    "\n",
    "        # Print model name\n",
    "        print(model_name)\n",
    "        print(\"=\" * len(model_name))\n",
    "\n",
    "        # Print classification report\n",
    "        print(\"Classification Report:\")\n",
    "        print(classification_report(y_test, predictions))\n",
    "\n",
    "        # Print TP, FN, FP, TN\n",
    "        print('TP:', TP)\n",
    "        print('FN:', FN)\n",
    "        print('FP:', FP)\n",
    "        print('TN:', TN)\n",
    "\n",
    "        print(\"\\n\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "74a503a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "===================\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97       271\n",
      "           1       0.86      0.27      0.41        22\n",
      "\n",
      "    accuracy                           0.94       293\n",
      "   macro avg       0.90      0.63      0.69       293\n",
      "weighted avg       0.94      0.94      0.93       293\n",
      "\n",
      "TP: 6\n",
      "FN: 16\n",
      "FP: 1\n",
      "TN: 270\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "=============\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       271\n",
      "           1       0.91      0.45      0.61        22\n",
      "\n",
      "    accuracy                           0.96       293\n",
      "   macro avg       0.93      0.73      0.79       293\n",
      "weighted avg       0.95      0.96      0.95       293\n",
      "\n",
      "TP: 10\n",
      "FN: 12\n",
      "FP: 1\n",
      "TN: 270\n",
      "\n",
      "\n",
      "\n",
      "XGBoost\n",
      "=======\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.97       271\n",
      "           1       0.77      0.45      0.57        22\n",
      "\n",
      "    accuracy                           0.95       293\n",
      "   macro avg       0.86      0.72      0.77       293\n",
      "weighted avg       0.94      0.95      0.94       293\n",
      "\n",
      "TP: 10\n",
      "FN: 12\n",
      "FP: 3\n",
      "TN: 268\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_models(X_train, y_train, X_test, y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a684fc45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_roc_curve(models, X_test, y_test):\n",
    "    \"\"\"\n",
    "    Plot ROC curve for each model.\n",
    "\n",
    "    Parameters:\n",
    "    - models (dict): A dictionary containing model names as keys and trained models as values.\n",
    "    - X_test (DataFrame or array-like): Test features.\n",
    "    - y_test (Series or array-like): True labels for the test data.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    for model_name, model in models.items():\n",
    "        # Make predictions\n",
    "        predictions = model.predict_proba(X_test)[:, 1]\n",
    "        \n",
    "        # Compute ROC curve\n",
    "        fpr, tpr, thresholds = roc_curve(y_test, predictions)\n",
    "        \n",
    "        # Compute AUC score\n",
    "        auc_score = roc_auc_score(y_test, predictions)\n",
    "        \n",
    "        # Plot ROC curve\n",
    "        plt.plot(fpr, tpr, label=f\"{model_name} (AUC = {auc_score:.2f})\")\n",
    "\n",
    "    # Plot ROC curve for random guessing (baseline)\n",
    "    plt.plot([0, 1], [0, 1], linestyle=\"--\", color=\"grey\", label=\"Random Guessing\")\n",
    "\n",
    "    # Add labels and legend\n",
    "    plt.xlabel(\"False Positive Rate\")\n",
    "    plt.ylabel(\"True Positive Rate\")\n",
    "    plt.title(\"Receiver Operating Characteristic (ROC) Curve\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "015f4b24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3cfr06QLrsrOzgxDCsK7CKBQKDB06FOvWrTOcGl2+fDk8PT0RFhZmtI1NmzYV2EajRo2M6s2Xfzr1afJPLeef8izM9evXAQDe3t5G0z08PArMmz/twYMHZVb32rVrMWjQINSsWRO//PILDh8+jGPHjmHUqFHIzs4u0essirW1NaysrIymWVpaGq33wYMHRqE9X2HTClO7du1i929hXFxcCkyztLREVlaW4bGp+6WwfXv06FGEhoYCAH788UccPHgQx44dw4cffggAhu3dv38fAJ76WSjK3bt3kZKSApVKVeC9kJiYWOr379SpU/HVV1/hyJEjCA8Ph4uLC7p06VLkkFdPc//+fXh5eRl1+SmprKysAu+lx33++ec4duwY9u7diw8//BB3795Fv379kJOTY5inJJ/HjIwMJCUlGT6PJVmmMPm1Pv6eKkxp3r+mKOxYOzs7o0+fPlixYgV0Oh2AvN+LwcHBht8dDx48gFarxXfffVfgPZXfDaG4371UNbHPLFUZAQEBCAoKAgB06tQJOp0OixcvRlRUFCIjI+Hq6gog7w9hREREoeto0KABgLx+rfmtjEVxdXWFWq3G0qVLi3y+OCNHjsSXX35p6LO7ceNGTJ48GXK53GgdTZs2xb///e9C1+Hl5WX0uKStNt26dcMHH3yA9evXF2h5zJc/HmW3bt2MpicmJhaYN39afhgri7p/+eUX+Pn5YfXq1UbPPx4CypOLiwuOHj1aYHphr78wYWFh+O6773DkyJEy7Xdo6n4pbN/+9ttvUCqV2Lx5s1EQe3IM0ho1agAAbt26VeCfmpJwdXWFi4sLtm3bVujzdnZ2T621MAqFAlOmTMGUKVOQkpKCHTt24IMPPkBYWBhu3rwJa2trk+qsUaMGDhw4AL1eb3KgdXV1xcOHD4t8vk6dOobfSx06dIBarcZHH32E7777Dv/6178AAIGBgXBycsLGjRsxe/bsQvfDxo0bodfrDZ/HoKAgODs7Y8OGDUUuU5j8Wp/2+8nU96+VlVWh78GkpKRCt1VUvSNHjsSaNWsQGxuL2rVr49ixY5g/f77heScnJ8jlcgwdOrTIMwZ+fn5PrZeqGIm7ORA9s6JGM3j48KHhCuH8vrDPPfec6NGjx1PXmd9ntrg+pZ988omwtrYWV69efer6iupP1qpVKxEcHCy+//77QvuwjhkzRnh5eT21D1h+39Mvv/zyqbXkyx+a68mxK4X439Bc3bt3N5qOYvrM1q1bt0zrjoiIMOrDKkTeCAm2trbiyV9dzs7OYtCgQQXWUdxoBk/KvwI+X36f2cf7PgtR8j6zJRnaaO3atYbHRQ3NNXz4cKP+qKbsF/x3NIMnTZkyRdja2hr1U87MzBS1a9c26md67do1IZfLxdChQ4t9rREREcLNza3A9F9++cXQn/1p8kczKOq5pw29NnfuXKP+2Pn9Lwvr915Un9klS5Y8tc4njRo1Sjg7OxeYXtRoBrm5uaJevXrCxcVFpKWlGabnD831+eefF1jX3bt3DUNzPf5eetrQXHfv3i3w+V65cqUAIE6dOlXs6zL1/RsWFiYaNmxoNM+FCxeEQqEotM/sk/sln1arFTVr1hSDBg0S//rXv4SVlVWB7Xft2lU0a9asxOPlUtXHllmqspycnDB16lS8++67+PXXX/HKK69g4cKFCA8PR1hYGEaMGIGaNWvi4cOHOH/+PP766y+sWbMGADBr1ixs3boVHTp0wAcffIAmTZogJSUF27Ztw5QpU+Dv74/JkycjOjoaHTp0wFtvvYWmTZtCr9cjPj4e27dvx9tvv41WrVoVW+OoUaMwfvx43LlzB23btjW0DOebNWsWYmNj0bZtW0yaNAkNGjRAdnY2rl+/ji1btmDBggWlPgW8YsUKdO3aFaGhoZg0aRK6dOkCIK8v5TfffAN/f3+jvmv5XF1d0blzZ/zf//2fYTSDf/75B7/99luZ1t2rVy+sXbsWEydORGRkJG7evImPP/4Ynp6eBe7s1qRJE+zZswebNm2Cp6cn7OzsCuxLUw0fPhxff/01XnnlFXzyySeoV68etm7dipiYGAB4aguen5+fodW9efPmeP3119GiRQsAeVdjL126FEII9O/f36S6TNkvRenZsyfmzJmDIUOGYNy4cXjw4AG++uorWFpaGs3n6+uLDz74AB9//DGysrLw0ksvwcHBAefOnUNSUhJmzpwJIG//r127FvPnz0dgYCAsLCwQFBSEF198EStXrkSPHj3w5ptvIjg4GEqlErdu3cLu3bvRt29fk18/kHdlf+PGjREUFIQaNWrgxo0bmDt3Lnx8fAwjeDRp0gQA8M0332D48OFQKpVo0KBBgdZgIK8f9LJlyzBhwgRcuHABnTp1gl6vx59//omAgAC8+OKLRdYSEhKCpUuX4uLFiyXq76tUKvHpp59i0KBB+Oabb/DRRx8BAN577z2cOnXK8H3w4MFwcHDA6dOn8eWXXyI9PR2bN2+Gg4ODYV3vvPMOzp8/j+nTp+Po0aMYMmQIvL29kZqain379mHRokWYOXMm2rVrZ1jmyJEjcHFxMeyfopj6/h06dCheeeUVTJw4EQMGDMCNGzfwxRdfGFr3S0oul2PYsGGYM2cO7O3tERERYfSagbxj+sILL6B9+/Z49dVX4evri/T0dFy+fBmbNm3Crl27TNomVQFSp2miZ1VUy6wQeWNy1q5dWzz33HNCq9UKIYQ4deqUGDRokHBzcxNKpVJ4eHiIzp07F7gq+ObNm2LUqFHCw8PDMIbsoEGDxN27dw3zPHr0SHz00UeGMTTzx7t86623jFovi2pZSk1NFWq1utgrqe/fvy8mTZok/Pz8hFKpFM7OziIwMFB8+OGHhvFsS9Mym1//p59+Kpo3by6sra2FtbW1aNq0qfjkk08KjJUrxP9a+ubNmyfq1q0rlEql8Pf3L3QQ9rKo+7PPPhO+vr7C0tJSBAQEiB9//LFAC6oQQsTFxYl27doJa2vrEo8z+6TC1hsfHy8iIiKEra2tsLOzEwMGDCh0zMviXLlyRUycOFHUq1dPWFpaCrVaLRo2bCimTJlidKV9SVtmTdkvKKJlVgghli5dKho0aCAsLS1FnTp1xOzZs8WSJUsKHQFgxYoVomXLlsLKykrY2tqKFi1aGLVMP3z4UERGRgpHR0chk8mM6tBoNOKrr74SzZo1Myzv7+8vxo8fLy5dumSYz5SW2f/85z+ibdu2wtXVVahUKlG7dm0xevRocf36daPlpk6dKry8vISFhcVTx5nNysoS06ZNM4yf7OLiIjp37iwOHTpUaE35UlNTha2trfjiiy+Mpj+tBbJVq1bCycnJqNVRr9eLlStXipCQEOHo6ChUKpXw8/MTr776aoGRQR63YcMG0bNnT1GjRg2hUCiEk5OT6NSpk1iwYIFR66Verxc+Pj7ijTfeKPY1Pa6k71+9Xi+++OILUadOHWFlZSWCgoLErl27ihzNoKj9IkTeGLL479jAsbGxhc5z7do1MWrUKMM41jVq1BBt27YVn3zySYlfG1UdMiH+e8kyEdFTyGQyvPbaa/j++++lLkUyn376KT766CPEx8eXulWcqpY33ngDO3fuxNmzZ595tIHytHPnToSGhuLs2bPw9/eXuhyiMsNuBkRERcgP7f7+/tBoNNi1axe+/fZbvPLKKwyyZPDRRx9hxYoViI6ONtw4pDL65JNPMGrUKAZZqnIYZomIimBtbY2vv/4a169fR05ODmrXro333nvP0M+RCMgbrm3lypVITk6WupQiJScno2PHjoZhCImqEnYzICIiIiKzxZsmEBEREZHZYpglIiIiIrPFMEtEREREZqvaXQCm1+tx584d2NnZVeohVIiIiIiqKyEE0tPT4eXl9dSb1FS7MHvnzp1S3WOciIiIiCrWzZs3nzoUYrULs/m3Mrx58ybs7e0rZJsajQbbt29HaGgolEplhWyTyg6Pn/njMTR/PIbmjcfP/FX0MUxLS4O3t3eht6B+UrULs/ldC+zt7Ss0zFpbW8Pe3p4fYjPE42f+eAzNH4+heePxM39SHcOSdAnlBWBEREREZLYYZomIiIjIbDHMEhEREZHZYpglIiIiIrPFMEtEREREZothloiIiIjMFsMsEREREZkthlkiIiIiMlsMs0RERERkthhmiYiIiMhsMcwSERERkdlimCUiIiIis8UwS0RERERmi2GWiIiIiMyWpGF237596N27N7y8vCCTybB+/fqnLrN3714EBgbCysoKderUwYIFC8q/UCIiIiKqlCQNsxkZGWjWrBm+//77Es1/7do19OjRA+3bt8fJkyfxwQcfYNKkSYiOji7nSomIiIioMlJIufHw8HCEh4eXeP4FCxagdu3amDt3LgAgICAAx48fx1dffYUBAwaUU5UkOSGA+CNAxn1JNi/T6eCZcgKyf/SAXC5JDfRspD6GQghk6TUVvt2qRKfTwTnlJLLPZkPDz6HZ4fEzPwICGq3e8DhXq4XdwzPQpwcCzrUkrKwgScOsqQ4fPozQ0FCjaWFhYViyZAk0Gg2USmWBZXJycpCTk2N4nJaWBgDQaDTQaCrmj0v+dipqe1WN7OpuKFYNlGz7CgDBAHBNshLoGUl5DAWAYZ7uiLOyrPiNV0Wn/pC6AnoWPH5myTPTE80eNsM+933YcKMNXOzcy32bpmQmswqziYmJcHc33oHu7u7QarVISkqCp6dngWVmz56NmTNnFpi+fft2WFtbl1uthYmNja3Q7VUVDW//hucAZCmdkalykbocIpNkQSDOKlfqMoiITCYTMjRJboL6afUBAP6p/jj+9wXo4st/25mZmSWe16zCLADIZDKjx0KIQqfnmzp1KqZMmWJ4nJaWBm9vb4SGhsLe3r78Cn2MRqNBbGwsunXrVmjrMRVPvmIeAEAZNgP2zYZU+PZ5/MyflMdQqc0Cfm8HANgRsQNqhbpCt19VaDQa7Nq1C507d+bn0Azx+JmXzFwtun4ei47KG6hhkQUAaNSsKepoAhAS3gOWluV/pin/THpJmFWY9fDwQGJiotG0e/fuQaFQwMWl8BY7S0vLQne6Uqms+D9qEmzT7Ok0QMIpAIDCpzUg4f7j8TN/UhxDDf53qszOyg7Wyoo9I1RVaBQaqGQq2Kvt+Tk0Qzx+5uXq5b/RR3UJljIdrKys0K9fP9SpUwdbtmyBpaVlhRxDU7ZhVuPMtmnTpsCp+u3btyMoKIgfjqrq7llAmwVYOgAuz0ldDRERUZWWlZWFbVv+gKVMh3s6GwwbNQYNGjSQuqxiSRpmHz16hLi4OMTFxQHIG3orLi4O8fF5nTGmTp2KYcOGGeafMGECbty4gSlTpuD8+fNYunQplixZgn/9619SlE8V4fbxvO+1AgELs/rfi4iIyOyo1WqE9+yFMxoPbMltAAcHB6lLeipJuxkcP34cnTp1MjzO79s6fPhwLF++HAkJCYZgCwB+fn7YsmUL3nrrLfzwww/w8vLCt99+y2G5qrJb+WG2pbR1EBERVVFnz56FpaUl6tWrBwB4rn4DHNdel7YoE0gaZkNCQgwXcBVm+fLlBaZ17NgRf/31VzlWRZXKrWN53xlmiYiIypRGo0FMTAxOnDgBtVqNV199FXZ2dlKXZTKzugCMqpnMh8CDy3k/1wyUthYiIqIqJCkpCVFRUbh79y4AICgoCDY2NhJXVToMs1R53T6R9925LmDtLG0tREREVcTp06exefNmaDQa2NjYoH///qhbt67UZZUawyxVXuwvS0REVGb0ej02b96MkydPAgB8fX0RERFhll0LHscwS5WXob9skLR1EBERVQEWj40K1LFjR3To0MFomrlimKXKSa9/bFgutswSERGVllarhUKRF/nCw8PRvHlz1K5dW+Kqyo75x3Gqmh5cBrJTAYUacG8kdTVERERmJzc3F+vXr8dvv/1mGD1KqVRWqSALsGWWKqv8Vlmv5oCcd3cjIiIyxd27dxEVFYWkpCTIZDLcunUL3t7eUpdVLhhmqXJif1kiIiKTCSHw119/Ydu2bdBqtbCzs8OAAQOqbJAFGGapsuLNEoiIiEySk5ODzZs34++//wYA1KtXD/3794e1tbXElZUvhlmqfHIzgLtn835mmCUiIiqRqKgoXL58GTKZDF26dEHbtm0hk8mkLqvcMcySaYQA7p0HctLKbxv3zgFCD9jXBOy9ym87VCwhBLI0ulIvm63LLuOKSk+j0SBdm4sHmelQKiu2D3aWNut/P2t0gNBW6ParCo1GixwdkJmrhVJU/T/OVQ2PX8Vo274jHjx4iPBevVGzVq1S/w7PzC3dclJhmCXT/B0NRI+umG2xv6xkhBCIXHAYJ24kl2ZpWPssgNz6RpnX9aw+Xz9L0u0HfrwDECpJazBvCrx7dJfURVCp8fiVNSW0cLPIwG29g2GaDH6Ys+gsgLPSFVbBGGbJNDcO5n1XOwNqx/LbjkINBI8rv/VTsbI0ulIGWQAyTaUMslLTZvoAgiNzEFHZcJFlIER1FTayXPyR448HwgYAIFB2Ld9BPk5QK+Vltr7ywjBLprl/Ie9798+AZoOlrYUqxPGPusJaVfJfZlnaLISsmQYA2Np/J9QKdXmVVmIajQY7duxE165dKrybQT4ruVW16LtWXjQaDWJitiMsLFSyY0ilx+NXdoQQ+Ov4MezZ9Rf0ej0cHBywpn9reHiWfbc8tVJuFr+3GGbJNPf/yfteo4G0dVCFsVbJYa0y4VeF7H/B11ltC2ul9FfRajQa2ClUcLG24x9SM6WRCVjKAWuVAkol/3SZGx6/spGVlYVNGzfin3/y/hYHBASgT58+sLKykrgyafEdRSWXkQRkPgAgA1zrS10NERFRtXHr1i1ERUUhNTUVcrkcoaGhaNmypVm0nJY3hlkqufxWWcfagEr61jYiIqLq4saNG0hNTYWTkxMGDhwIT09PqUuqNBhmqeTy+8vW8Je2DiIiomqmbdu2AICgoCBYWlpKXE3lYiF1AWRGDGGW/WWJiIjKU3x8PH755Rfk5uYCAGQyGdq1a8cgWwiGWSo5XvxFRERUroQQ2L9/P5YvX44rV65g//79UpdU6bGbAZUcuxkQERGVm4yMDKxbtw5XrlwBADRt2hTt27eXuKrKj2GWSiYrBXiUmPczRzIgIiIqU9evX0d0dDQePXoEhUKBHj16oHnz5hytoAQYZqlkki7mfbevCVjZS1sLERFRFXL69GmsX78eQgjUqFEDkZGRcHNzk7oss8EwSyXD/rJERETlws/PD2q1GvXr10d4eDhUKpXUJZkVhlkqGfaXJSIiKjMPHjyAi4sLAMDOzg4TJkyAnZ2dxFWZJ45mQCWT3zLL/rJERESlptfrsXv3bvzwww84d+6cYTqDbOmxZZZKhi2zREREzyQtLQ1r167FjRs3AOTdorZhw4YSV2X+GGbp6XIeAak3835mn1kiIiKTXb58GevWrUNmZiZUKhV69+6Nxo0bS11WlcAwS0+XP5KBjRtg7SxtLURERGZEp9Nh9+7dOHjwIADAw8MDkZGRhv6y9OwYZunpqvltbIUQyBW5yNJmQQNNuW8rW6sv122URGauDpDl3UIxS5sFyOQlXjZLm1VeZRERmZ0bN24YgmzLli0RGhoKhYLxqyxxb9LTGYblqn79ZYUQGBU7CqdST2HW77OkLqdC2f33cIesmSZtIUREZqxOnTp44YUX4Onpyf6x5YSjGdDTVeOW2SxtFk4lnZK6DLPUwq0F1Aq11GUQEVUonU6HXbt2IS0tzTCtS5cuDLLliC2z9HS8YQIAYEfEDthZld/QKVkaHQI/3gEA2P9eJ1irSn5qv7xYKSxKfStFtULN2zASUbWSkpKCqKgo3L59G/Hx8Rg+fDh/D1YAhlkqniYLSMkbQqQ6djN4nFqhhrXSuvw2ILSAyLvri4u1LaxV/HgSEZmL8+fPY+PGjcjOzoaVlRVat27NIFtB+NeSivfgMiD0gNoJsKkhdTVERESVilarRWxsLI4ePQoAqFWrFgYMGABHR0dpC6tGGGapeI/fLIH/YRIRERmkpaXht99+Q0JCAgCgbdu26Ny5M+Ry6buJVScMs1Q89pclIiIqlJWVFbRaLdRqNfr164f69XnLdykwzFLxqvGwXERERE/SarWQy+WQyWRQqVQYPHgwlEol7O3tpS6t2uLQXFS8/G4Grvxvk4iIqrekpCQsXrzYcBMEAHBxcWGQlRhbZqlo2lzg4dW8n9kyS0RE1djp06exefNmaDQaZGRkIDg4GCqVSuqyCAyzVJyHVwG9FlDZAfZeUldDRERU4TQaDbZu3YqTJ08CAHx9fREREcEgW4kwzFLRHr/4iyMZEBFRNXP//n1ERUXh3r17AICOHTuiQ4cOsLBgL83KhGGWivb4sFxERETVSE5ODpYuXYrs7GzY2toiIiICfn5+UpdFhWCYpaJxWC4iIqqmLC0t0alTJ1y4cAH9+/eHra2t1CVRERhmqWhJF/O+M8wSEVE1cPfuXQgh4OHhAQBo2bIlWrZsydvSVnLs9EGF02mBpEt5PzPMEhFRFSaEwIkTJ7B48WL8/vvvyMnJAQDIZDIGWTPAllkqXMoNQJcDKNSAQ22pqyEiIioXOTk52Lx5M/7++28AeePG6nQ6iasiUzDMUuEM/WXrA7xqk4iIqqDExESsWbMGDx8+hEwmQ+fOndGuXTu2xpoZhtmqTqcFtNmmL5eY9x+quY5kIIRAljar2OeztfqnrufxdWTmaqGQa8ukvsJk5rIlgIioIgghcPz4ccTExECn08He3h6RkZHw9vaWujQqBYbZqiw9EZjfDshMKv06zLC/rBACw7YOQ9z9uDJdb+vP9gKCg2QTEVUFFy9ehE6nQ/369dG3b19YW1tLXRKVEsNsVXbzz2cLspb2wHOhZVdPBcnSZpV5kNVm+gBCWabrLEqQjxPUSnmFbIuIqDqSyWTo168fzp07h6CgIHYrMHMMs1VZ2p287wG9gYgfTV/eQgnIzfstsmfQHqgVaqNpWRodAj/eAQDY/14nWKuKD44ajQZ7d+xD92FdoFSWf6BVK+X8xUpEVIaEEPjzzz+RlJSEXr16AQBsbGzQsmVLiSujsmDeSYWKlx9mHWoDSnXx81ZRaoUa1sonTh0JraG7gIu1LaxVxX8MNBoNrBQyWKsUUCr5kSEiMidZWVnYuHEj/vkn78LmRo0a8U5eVQz/Mldl+WHW3kvaOoiIiCRw69YtREVFITU1FXK5HKGhofD19ZW6LCpjDLNVGcMsERFVQ0IIHD58GDt37oRer4eTkxMiIyPh5cW/h1URw2xVlnY777t9TWnrICIiqkAbN25EXFwcgLxuBb169YKVlZW0RVG5YZitqvR6ID0h72e2zBIRUTXSqFEj/P333wgLC0NgYCAvqq3iGGarqswHgC4XgAyw85C6GiIionIjhMCDBw/g6uoKAKhXrx7efPNN2NraSlwZVQTep7Sqyu9iYOsGyCtmfFQiIqKKlpGRgZUrV2Lx4sVITk42TGeQrT7YMltV8eIvIiKq4q5fv47o6Gg8evQICoUC9+7dg5OTk9RlUQVjmK2q0vPDLC/+IiKiqkWv12P//v3Yu3cvhBBwdXXFwIED4ebmJnVpJAGG2aqKLbNERFQFPXr0CGvXrsW1a9cAAM2bN0d4eDhUKpXElZFUGGarKoZZIiKqgo4cOYJr165BqVSiZ8+eaNasmdQlkcQYZqsqjjFLRERVUEhICNLT09G+fXvD6AVUvXE0g6qKLbNERFQFpKWlYfv27dDr9QAAhUKB/v37M8iSAVtmqyIh/hdm7TylrYWIiKiULl++jHXr1iEzMxOWlpbo2LGj1CVRJcQwWxVlpwCazLyf2TJLRERmRqfTYffu3Th48CAAwMPDA40bN5a4KqqsGGarorT/3sZW7Qwo1cXOKoRAljarAooqW0IIZGv1hT73+OvJ0ugAoTV6PjNXV661ERFR6aWmpiI6Oho3b94EAAQFBSEsLAwKBSMLFY7vDHPy50Jg1yeAXlv8fPr/hrWnXPwlhMCwrcMQdz+ubOqrhAI/3gEIDtdCRGQOrl69iqioKGRlZcHS0hK9e/dGo0aNpC6LKjmGWXNy6jcgJ63k8/u1L/bpLG1WlQ6y2kwfQBR9K98gHyeolfIKrIiIiIpja2sLjUYDT09PREZGwtnZWeqSyAwwzJqT/Iu6XloNuPkXP6+FwqRhufYM2gO1ovguCZVFlkaX1+IKYP97nWCtKjyQWsmtIJPJilyPWikv9nkiIip/ubm5hhseuLm5YdiwYfD09GS3AioxvlPMhU4DPLqb93PNQMC2RpmuXq1Qw1ppXabrLDdCa+g64GJtC2sV38ZERObon3/+waZNm/Diiy/C29sbAAzfiUqKKcBcpCcCEIBcBVi7SF0NERFRqWm1WsTGxuLo0aMA8u7qxRBLpSX5TRPmzZsHPz8/WFlZITAwEPv37y92/pUrV6JZs2awtraGp6cnRo4ciQcPHlRQtRIyjBvrAVhIftiIiIhK5eHDh1i6dKkhyLZp0wYRERESV0XmTNJUtHr1akyePBkffvghTp48ifbt2yM8PBzx8fGFzn/gwAEMGzYMo0ePxtmzZ7FmzRocO3YMY8aMqeDKJcDb0xIRkZk7d+4cFi5ciISEBKjVarz00ksIDQ2FXM6Lcan0JA2zc+bMwejRozFmzBgEBARg7ty58Pb2xvz58wud/8iRI/D19cWkSZPg5+eHF154AePHj8fx48cruHIJ8Pa0RERkxtLT07F+/Xrk5ubC29sb48ePR/369aUui6oAyfrM5ubm4sSJE3j//feNpoeGhuLQoUOFLtO2bVt8+OGH2LJlC8LDw3Hv3j1ERUWhZ8+eRW4nJycHOTk5hsdpaXlDW2k0Gmg0mjJ4JU+Xv51n2Z5Fyk3IAehsPaAvo7q1Wq3RzxpUzP54VhqN9rGfNdDIRDlv79mPH0mLx9D88RiaN41GA1tbW9SvXx+urq7o0KEDLCwseDzNSEV/Bk3ZjmRhNikpCTqdDu7u7kbT3d3dkZiYWOgybdu2xcqVKzF48GBkZ2dDq9WiT58++O6774rczuzZszFz5swC07dv3w5r64q9ej82NrbUywZd+ws1AZy7mYKrW7aUST25Itfwc0xMDFQy87i5QI4OyH/rxsRsh2UFnZ16luNHlQOPofnjMTQvKSkpsLOzg1yeNxSiWq1GZmYmtm3bJnVpVEoV9RnMzMws8bySj2bw5DifQogix/48d+4cJk2ahGnTpiEsLAwJCQl45513MGHCBCxZsqTQZaZOnYopU6YYHqelpcHb2xuhoaGwt7cvuxdSDI1Gg9jYWHTr1g1KZdGD+BdHvvw7IAUIaN0V/v49yqSuLG0WZv0+CwAQFhZmNuPMZuZq8e7RXQCAsLDQch+aqyyOH0mLx9D88Rial/zjdf36dQQEBCAsLAw7duxAaGgoj5+ZqujPYP6Z9JKQLMy6urpCLpcXaIW9d+9egdbafLNnz0a7du3wzjvvAACaNm0KGxsbtG/fHp988gk8PT0LLGNpaQlLS8sC05VKZYV/oJ5pm4/y9pPCqTZQRnU/3q1AoVCYzS8YpfjfPzt5+7Ri3sZSvGeobPEYmj8ew8rv/v37iIqKwr179wAANWrUMBwzHj/zV1HH0JRtSHYBmEqlQmBgYIHm6tjYWLRt27bQZTIzM2HxxLBU+VdAClG+/SYlpdcB6Ql5P/MCMCIiqqTi4uLw448/4t69e7CxscHQoUPRqVMn3m2RypWk3QymTJmCoUOHIigoCG3atMGiRYsQHx+PCRMmAMjrInD79m2sWLECANC7d2+MHTsW8+fPN3QzmDx5MoKDg+HlVYVDXsZ9QK8FZHLAtvBWayIiIqnk5uZiy5YtOHXqFADAz88PERERsLW1lbgyqg4kDbODBw/GgwcPMGvWLCQkJKBx48bYsmULfHx8AAAJCQlGY86OGDEC6enp+P777/H222/D0dERnTt3xueffy7VS6gY+WPM2nkAFhyLj4iIKheNRoMrV65AJpMhJCQEL7zwQoEzqUTlRfILwCZOnIiJEycW+tzy5csLTHvjjTfwxhtvlHNVlYzh7l8F+wQTERFJzcbGBpGRkRBCwNfXV+pyqJqRPMxSCfCGCUREVInk5OTgjz/+QL169dC0aVMAMJxVJapoDLPmgLeyJSKiSiIxMRFr1qzBw4cPcenSJTRo0KDQUYOIKgrDrDl4hpZZIQSytFmFPlfU9MpECIEsjc5oWmauroi5iYiovAghcPz4ccTExECn08He3h4DBgxgkCXJMcyag1KGWSEEhm0dhrj7cWVfUwUQQiBywWGcuJEsdSlERNVadnY2Nm3ahHPnzgEA6tevj759+1b4nTSJCsMwaw6yU/O+WzubtFiWNqtEQbaFW4tKefevLI2u2CAb5OMEtZKjOxARlafc3FwsWrQIycnJsLCwQNeuXdG6dWuOHUuVBsOsWSn9L449g/YUGVjVCnWl/6V0/KOusFYZB1e1Ul7p6yYiMncqlQoBAQE4e/YsIiMjUatWLalLIjLCMFtNqBVqWCvN93SQtUoOaxXfrkREFSErKwsajQb29vYAgM6dO+OFF16AWl35zuIRcURjIiIiMrh16xYWLlyI33//HTpd3gW3crmcQZYqLTZ1EREREYQQOHz4MHbu3Am9Xg8LCwukp6fD0dFR6tKIisUwS0REVM1lZmZiw4YNuHjxIgCgYcOG6N27N6ysrCSujOjpGGaJiIiqsfj4eERHRyMtLQ1yuRzdu3dHYGAgL7Als8EwS0REVE0JIRATE4O0tDQ4Oztj4MCB8PDwkLosIpMwzBIREVVTMpkMEREROHjwIMLCwng3LzJLHM2AiIioGrl+/TqOHDlieOzi4oI+ffowyJLZYsssERFRNaDX67F//37s3bsXQgh4enrCx8dH6rKInhnDLBERURX36NEjrF27FteuXQMANGvWDJ6enhJXRVQ2GGaJiIiqsKtXr2Lt2rXIyMiAUqlEjx490Lx5c6nLIiozDLNERERV1P79+7Fr1y4AgJubGyIjI1GjRg2JqyIqWwyzREREVZSNjQ0AoEWLFggPD4dSqZS4IqKyxzBLRERUheTm5kKlUgHIC7Gurq6oXbu2xFURlR8OzUVERFQF6PV67NixA/PmzUNWVhaAvHFkGWSpqmPLbBUlhECWNkvqMoolhECWRlfk85m5RT9HRET/k5qaiujoaNy8eRMAcO7cOQQGBkpcFVHFYJitzI4vA06tAh5cMWkxIQSGbR2GuPtx5VNXGRBCIHLBYZy4kSx1KUREZu3ixYtYv349srKyYGlpid69e6NRo0ZSl0VUYRhmK7N9XwJpt//32L5miRbL0mYZBdkWbi2gVqjLuLhnk6XRlTjIBvk4Qa2Ul3NFRETmRafTYefOnTh8+DAAwNPTE5GRkXB2dpa4MqKKxTBbmen/e5o9bDbg+wJQo77Jq9gzaA+crZwhk8nKuLiyc/yjrrBWFR1W1Up5pa6fiEgKe/bsMQTZ4OBgdOvWDQoF/6xT9cN3vTnwaw94NCnVomqFutIHQWuVHNYqvhWJiEzRtm1bXLp0CR07dkRAQIDU5RBJhqMZEBERmQGtVotTp05BCAEAUKvVGD9+PIMsVXtsDiMiIqrkkpOTsWbNGiQkJECr1RpGKqjsZ96IKgLDLBERUSV27tw5bNy4ETk5OVCr1bCzs5O6JKJKhWGWiIioEtJqtYiJicHx48cBAN7e3hgwYAAcHBwkroyocmGYJSIiqmQePHiAqKgoJCYmAgDatWuHTp06QS7nMIVET2KYJSIiqmTS0tKQmJgIa2tr9O/fH/Xq1ZO6JKJKi2GWiIioEhBCGC7o8vPzQ79+/eDn5wd7e3uJKyOq3Dg0FxERkcTu37+PZcuW4cGDB4ZpzZo1Y5AlKgGGWSIiIgnFxcXhxx9/xM2bN7F161apyyEyO+xmQEREJIHc3Fxs2bIFp06dAvC/rgVEZBqGWSIiogp27949rFmzBklJSZDJZOjYsSPat28PCwueMCUyFcMsERFRBbp16xZ++uknaLVa2NraYsCAAfD19ZW6LCKzxTBLRERUgTw9PeHh4QFLS0v0798fNjY2UpdEZNYYZomIiMrZ/fv34ezsDLlcDrlcjiFDhsDKysowFBcRlR475xAREZUTIQSOHz+OhQsXYteuXYbparWaQZaojLBltjLY+wWQcKrg9Kzkiq+FiIjKRE5ODjZt2oSzZ88CAJKSkqDX63mRF1EZY5iVWuotYPe/i5lBBqidK6wcIiJ6dnfu3EFUVBSSk5NhYWGBLl26oE2bNmyNJSoHDLNS0+XmfZdbAuGfF3zetT7gULNiayIiolIRQuDo0aOIjY2FTqeDg4MDIiMjUatWLalLI6qyGGYrC7kKCBopdRVERPQM0tPTsWvXLuh0Ovj7+6NPnz5Qq9VSl0VUpTHMEhERlRF7e3v07t0bGRkZCA4OZrcCogrAMEtERFRKQggcOXIEHh4e8PPzAwA0btxY4qqIqheGWSIiolLIysrC+vXrcfHiRdja2mLixInsUkAkAYZZIiIiE928eRNRUVFIS0uDXC5Hhw4dYGVlJXVZRNUSwywREVEJCSFw8OBB7Nq1C0IIODs7Y+DAgfDw8JC6NKJqi2GWiIioBDQaDX7//XdcvnwZQF7f2F69esHS0lLiyoiqN4ZZIiKiElAoFLCysoJCoUD37t3x/PPPc7QCokqAYZaIiKgIer0eWq0WKpUKMpkMvXr1Qvv27eHm5iZ1aUT0X7xBNBERUSEePXqElStXYt26dRBCAAAsLS0ZZIkqGbbMEhERPeHatWtYu3YtHj16BKVSiaSkJNSoUUPqsoioEAyzRERE/6XX67F3717s27cPAFCjRg0MHDiQQZaoEmOYJSIiApCeno61a9fi+vXrAIAWLVogPDwcSqVS2sKIqFgMs0REVO0JIfDbb7/hzp07UCqV6NWrF5o2bSp1WURUAqW6AEyr1WLHjh1YuHAh0tPTAQB37tzBo0ePyrQ4IiKiiiCTydC9e3d4enpi/PjxDLJEZsTkltkbN26ge/fuiI+PR05ODrp16wY7Ozt88cUXyM7OxoIFC8qjTiohIQSytFlSl2FECIEsjc5oWmauroi5iYgqRlpaGhITE1G/fn0AgLe3N8aOHcuxY4nMjMlh9s0330RQUBBOnToFFxcXw/T+/ftjzJgxZVocmUYIgWFbhyHufpzUpRgIIRC54DBO3EiWuhQiIoNLly5h3bp10Gg0GDt2rGG4LQZZIvNjcpg9cOAADh48CJVKZTTdx8cHt2/fLrPCyHRZ2iyjINvCrQXUCrV0BQHI0uiKDbJBPk5QK+UVWBERVWc6nQ67du3CoUOHAACenp5QKHj5CJE5M/kTrNfrodMVPEV869Yt2NnZlUlR9Oz2DNoDZyvnStXKcPyjrrBWGQdXtVJeqWokoqorJSUF0dHRuHXrFgAgODgY3bp1Y5glMnMmf4K7deuGuXPnYtGiRQDyTsk8evQI06dPR48ePcq8QCodtUJd6UKitUoOaxX/aBBRxfvnn3+wYcMGZGdnw9LSEn379kVAQIDUZRFRGTA5WXz99dfo1KkTGjZsiOzsbAwZMgSXLl2Cq6srVq1aVR41EhERPZOEhARkZ2ejZs2aGDBgAJycnKQuiYjKiMlh1svLC3Fxcfjtt99w4sQJ6PV6jB49Gi+//DLUamn7ZxIREeUTQhjOUHXs2BE2NjYIDAyEXM5++kRViclhdt++fWjbti1GjhyJkSNHGqZrtVrs27cPHTp0KNMCiYiITHXu3DkcO3YML7/8MhQKBSwsLBAcHCx1WURUDky+aUKnTp3w8OHDAtNTU1PRqVOnMimKiIioNLRaLbZs2YI1a9bg+vXrOHr0qNQlEVE5M7ll9vHTNo978OABbGxsyqQoIiIiUz148ABRUVFITEwEALRr1w6tWrWSuCoiKm8lDrMREREA8kYvGDFiBCwtLQ3P6XQ6nD59Gm3bti37ComIiJ7i77//xqZNm5Cbmwtra2v069cPzz33nNRlEVEFKHGYdXBwAJDXMmtnZ2d0sZdKpULr1q0xduzYsq+QiIioGIcOHUJsbCwAoHbt2hgwYADs7e0lroqIKkqJw+yyZcsAAL6+vvjXv/7FLgVERFQpNGzYEAcOHEBQUBBCQkJgYWHy5SBEZMZM7jM7ffr08qiDiIioxBISEuDp6QkAcHR0xBtvvMHhIYmqqVL9+xoVFYVBgwahdevWeP75542+TDVv3jz4+fnBysoKgYGB2L9/f7Hz5+Tk4MMPP4SPjw8sLS1Rt25dLF26tDQvg4iIzExubi42bNiARYsW4dKlS4bpDLJE1ZfJYfbbb7/FyJEj4ebmhpMnTyI4OBguLi64evUqwsPDTVrX6tWrMXnyZHz44Yc4efIk2rdvj/DwcMTHxxe5zKBBg7Bz504sWbIEFy5cwKpVq+Dv72/qyyAiIjOTlZWF5cuXIy4uDjKZDElJSVKXRESVgMndDObNm4dFixbhpZdewk8//YR3330XderUwbRp0wodf7Y4c+bMwejRozFmzBgAwNy5cxETE4P58+dj9uzZBebftm0b9u7di6tXr8LZ2RlAXh9eIiKquoQQOHXqFC5evAghBGxtbTFgwAD+/iciAKUIs/Hx8YYhuNRqNdLT0wEAQ4cORevWrfH999+XaD25ubk4ceIE3n//faPpoaGhOHToUKHLbNy4EUFBQfjiiy/w888/w8bGBn369MHHH39c5CmmnJwc5OTkGB6npaUBADQaDTQaTYlqfVb52yl0e1otlAAEBLTPWI9WqzX6WYOKeX3F0Wi0j/2sgUYmJKymdIo9fmQWeAzNV25uLrZt24a///4bQF4DRt++fWFjY8PjaUb4GTR/FX0MTdmOyWHWw8MDDx48gI+PD3x8fHDkyBE0a9YM165dgxAlDypJSUnQ6XRwd3c3mu7u7m4Y8PpJV69exYEDB2BlZYV169YhKSkJEydOxMOHD4vsNzt79mzMnDmzwPTt27fD2tq6xPWWhfyhYx5nnXMX3fC/u9Y8i1yRa/g5JiYGKpnqmdZXFnJ0QP7bLCZmOyzN+JbohR0/Mi88huYnJSUF169fBwB4enrCwcEBe/fulbYoKjV+Bs1fRR3DzMzMEs9rcpjt3LkzNm3ahOeffx6jR4/GW2+9haioKBw/ftxwYwVTPHk3saLuMAYAer0eMpkMK1euNIx7O2fOHERGRuKHH34otHV26tSpmDJliuFxWloavL29ERoaWmHjEGo0GsTGxqJbt25QKpXGTyZfA84BCoUCPXr0eKbtZGmzMOv3WQCAsLAwqBXSXxCRmavFu0d3AQDCwkJhrTL5LSe5Yo8fmQUeQ/O2Z88e+Pj44Pz58zyGZoqfQfNX0ccw/0x6SZicLBYtWgS9Xg8AmDBhApydnXHgwAH07t0bEyZMKPF6XF1dIZfLC7TC3rt3r0BrbT5PT0/UrFnTEGQBICAgAEII3Lp1q9C7vVhaWhrdrSyfUqms8A+UQqGARhgHdZmQIb+KJ58zhRACaZpsw2ONkEHxDOsrKxrxv2sM8/a5+YXZfFK8Z6hs8RhWfjk5OdixYwdCQkIM45l369YNGo0G58+f5zE0czx+5q+ijqEp2zA5WVhYWBgNSD1o0CAMGjQIAHD79m3UrFmzROtRqVQIDAxEbGws+vfvb5geGxuLvn37FrpMu3btsGbNGjx69Ai2trYAgIsXL8LCwgK1atUy9aVUKCGAFxcfw1/xKUbTa8vuYp8l8ChHhybTYkq7dlj7LIDc+oZhSuDHOwAhfTcDIqKSSkhIwJo1a5CcnIy0tDS89NJLUpdERGagTG6TkpiYiDfeeAP16tUzabkpU6Zg8eLFWLp0Kc6fP4+33noL8fHxhhbeqVOnYtiwYYb5hwwZAhcXF4wcORLnzp3Dvn378M4772DUqFGVfozBXD0KBNkyI9MYBVltpg8gKtd/vkE+TlArzbjDLBGVGyEEjh49iiVLliA5ORkODg544YUXpC6LiMxEiVtmU1JS8Nprr2H79u1QKpV4//338frrr2PGjBn46quv0KhRI5NvXjB48GA8ePAAs2bNQkJCAho3bowtW7bAx8cHQN5/6Y+POWtra4vY2Fi88cYbCAoKgouLCwYNGoRPPvnEpO1K7fhHXWGtygt2suRrwHzA1lKOcx+GlWp9WdoshKyZBgDY2n8nnCydiux3LBW1Ul7paiIi6WVnZ2Pjxo04f/48AKBBgwbo27dvpW+gIKLKo8Rh9oMPPsC+ffswfPhwbNu2DW+99Ra2bduG7OxsbN26FR07dixVARMnTsTEiRMLfW758uUFpvn7+5v91ZDWKvn/LoT6b2ulDLLSXxwl+1+Lp7PaFtbsj0REZiApKQkrV65ESkoKLCws0K1bN7Rq1Yr/+BKRSUqcnv744w8sW7YMXbt2xcSJE1GvXj3Ur18fc+fOLcfyiIioqrKzs4OFhQUcHR0RGRlZ4msuiIgeV+Iwe+fOHTRs2BAAUKdOHVhZWRnu3EVERFQSOTk5UKlUkMlksLS0xEsvvQRbW1tYWVlJXRoRmakSXwCm1+uNhkmQy+WGYVOIiIie5ubNm5g3bx6OHj1qmObq6sogS0TPpMQts0IIjBgxwjBma3Z2NiZMmFAg0K5du7ZsKyQiIrMmhMChQ4ewc+dOCCFw4sQJBAUFQS7nCCdE9OxKHGaHDx9u9PiVV14p82KIiKhqycjIwPr163H58mUAQOPGjdGrVy8GWSIqMyUOs8uWLSvPOoiIqIq5ceMGoqOjkZ6eDoVCge7du+P555/naAVEVKbM996iRERUaaWnp+Pnn3+GTqeDi4sLBg4cWOStyomIngXDLBERlTk7OzuEhITg/v376NmzJ1Qq3l6biMoHwywREZWJa9euwcbGBm5ubgCAdu3aAQC7FRBRuSrx0FxERESF0ev12LNnD1asWIGoqCjk5uYCyAuxDLJEVN7YMktERKWWnp6OtWvX4vr16wCAmjVrMsASUYUqVcvszz//jHbt2sHLyws3btwAAMydOxcbNmwo0+KIiKjyunLlChYuXIjr169DqVSif//+6Nu3r9ENdoiIypvJYXb+/PmYMmUKevTogZSUFOh0OgCAo6Mj5s6dW9b1ERFRJaPX67Fr1y788ssvyMjIgLu7O8aNG4emTZtKXRoRVUMmh9nvvvsOP/74Iz788EOjQa+DgoJw5syZMi2OiIgqp/j4eABAYGAgRo8eDVdXV4krIqLqyuQ+s9euXUOLFi0KTLe0tERGRkaZFEWFE0IgS5tVYHph04iIypoQAjKZDBYWFhgwYADi4+PRqFEjqcsiomrO5DDr5+eHuLg4+Pj4GE3funUrGjZsWGaFVRt6fd73p1wwIYTAsK3DEHc/rvxrIiJ6jE6nw65du6DVahEeHg4gbxxZBlkiqgxMDrPvvPMOXnvtNWRnZ0MIgaNHj2LVqlWYPXs2Fi9eXB41Vm0Z9/K+2xR/ii5Lm/XUINvCrQXUCnUZFUZEBKSmpiIqKgq3bt0CALRo0QIeHh4SV0VE9D8mh9mRI0dCq9Xi3XffRWZmJoYMGYKaNWvim2++wYsvvlgeNVZtaXfyvtvXLPEiewbtKTS0qhVqDolDRGXmwoULWL9+PbKzs2FpaYk+ffowyBJRpVOqcWbHjh2LsWPHIikpCXq93nC3FyqFtNt53+08S7yIWqGGtdK6nAoioupOp9MhNjYWf/75JwDAy8sLkZGRcHJykrgyIqKCTB7NYObMmbhy5QoAwNXVlUH2WRlaZr2krYOICHn981etWmUIsq1bt8aoUaMYZImo0jI5zEZHR6N+/fpo3bo1vv/+e9y/f7886qo+8ltmTehmQERUXmQyGQIDA2FlZYUXX3wRYWFhRsMwEhFVNiaH2dOnT+P06dPo3Lkz5syZg5o1a6JHjx749ddfkZmZWR41Vm1smSUiiWm1Wty9e9fwOCAgAG+++SYaNGggYVVERCVTqtvZNmrUCJ9++imuXr2K3bt3w8/PD5MnT+aFAaXBMEtEEnr48CGWLFmCFStWIC0tzTDdyspKwqqIiEquVBeAPc7GxgZqtRoqlQrp6ellUVP1odMA6Yl5P7ObARFVsL///hubNm1Cbm4u1Go1UlJSYG9vL3VZREQmKVWYvXbtGn799VesXLkSFy9eRIcOHTBjxgwMHDiwrOur2h7dBSAACwVgU0PqaoiomtBoNIiJicGJEycAALVr18aAAQMYZInILJkcZtu0aYOjR4+iSZMmGDlypGGcWSqFtIS873ZegEWpenwQEZkkKSkJUVFRhj6y7du3R0hICCz4O4iIzJTJYbZTp05YvHgxb2NYFgwjGbC/LBFVjD///BN3796FjY0N+vfvj7p160pdEhHRMzE5zH766aflUUf1xIu/iKiCdevWDXq9HiEhIbCzs5O6HCKiZ1aiMDtlyhR8/PHHsLGxwZQpU4qdd86cOWVSWLXAllkiKmf37t3DiRMn0L17d8hkMqhUKvTu3VvqsoiIykyJwuzJkyeh0WgMP1MZMbTMss8xEZUtIQTi4uKwZcsWaLVaODk5oXXr1lKXRURU5koUZnfv3l3oz/SMDGHWU9o6iKhKyc3NxR9//IHTp08DAOrWrYsmTZpIXBURUfkw+fLVUaNGFTqebEZGBkaNGlUmRVUbbJklojJ29+5dLFq0CKdPn4ZMJkPnzp3x8ssvw8bGRurSiIjKhclh9qeffkJWVlaB6VlZWVixYkWZFFUt6PVAOi8AI6Ky8/fff2Px4sV48OAB7OzsMHz4cLRv3x4ymUzq0oiIyk2JRzNIS0uDEAJCCKSnpxvd6lCn02HLli1wc3MrlyKrBCEQKLsAV1kq5P/kAvosQK8FZBYQNm7I0mQWu3iWtuA/EEREj3N2doYQAvXq1UP//v1hbW0tdUlEROWuxGHW0dERMpkMMpkM9evXL/C8TCbDzJkzy7S4qsT90d+Itvwy78Ha/00Xth4Ytn004u7HSVIXEZm37OxsQ+OCl5cXRo8eDQ8PD7bGElG1UeIwu3v3bggh0LlzZ0RHR8PZ2dnwnEqlgo+PD7y8eLq8KGpNMgAgVVjD1rsJ5DIZIJMhq9lgxP1d8uHMWri1gFqhLq8yichMCCFw7Ngx7Nq1C8OHD4enZ96FpPnfiYiqixKH2Y4dOwIArl27htq1a/O//lI6oa+P1sO2wFr1312vyQT+G2b3DNrz1KCqVqi574mquezsbGzatAnnzp0DAMTFxTHEElG1VaIwe/r0aTRu3BgWFhZITU3FmTNnipy3adOmZVZcdaNWqGGtZB83Iira7du3ERUVhZSUFFhYWKBbt25o1aqV1GUREUmmRGG2efPmSExMhJubG5o3bw6ZTAYhRIH5ZDIZdDpdmRdJRFTdCSHw559/IjY2Fnq9Ho6OjoiMjETNmhzaj4iqtxKF2WvXrqFGjRqGn4mIqGKdP38eMTExAICAgAD06dPHaFQZIqLqqkRh1sfHp9CfiYioYgQEBKBBgwaoU6cOWrZsyb7zRET/VaqbJvzxxx+Gx++++y4cHR3Rtm1b3Lhxo0yLIyKqroQQOHHiBDQaDYC8blyDBw9GcHAwgywR0WNMDrOffvop1Oq8K+4PHz6M77//Hl988QVcXV3x1ltvlXmBRETVTWZmJlatWoXNmzdjy5YthukMsUREBZV4aK58N2/eRL169QAA69evR2RkJMaNG4d27dohJCSkrOsjIqpWbty4gejoaKSnp0OhUKBWrVoQQjDIEhEVweQwa2triwcPHqB27drYvn27oTXWysoKWVm85SoRUWkIIXDgwAHDDWpcXFwwcOBAuLu7S10aEVGlZnKY7datG8aMGYMWLVrg4sWL6NmzJwDg7Nmz8PX1Lev6iIiqvIyMDKxbtw5XrlwBkDded8+ePaFSqSSujIio8jO5z+wPP/yANm3a4P79+4iOjoaLiwsA4MSJE3jppZfKvEAioqpOp9MhISEBCoUCffr0Qb9+/RhkiYhKyOSWWUdHR3z//fcFps+cObNMCiIiqg4e7wdrb2+PgQMHwtraGm5ubhJXRkRkXkwOswCQkpKCJUuW4Pz585DJZAgICMDo0aPh4OBQ1vUREVU5jx49wtq1a9GyZUsEBAQAALtpERGVksndDI4fP466devi66+/xsOHD5GUlISvv/4adevWxV9//VUeNRIRVRlXr17FggULcO3aNWzbto23ACciekYmt8y+9dZb6NOnD3788UcoFHmLa7VajBkzBpMnT8a+ffvKvEgiInOn1+uxZ88e7N+/HwDg7u6OyMhIyOVyiSsjIjJvJofZ48ePGwVZAFAoFHj33XcRFBRUpsUREVUFaWlpiI6ORnx8PAAgMDAQYWFhUCqVEldGRGT+TA6z9vb2iI+Ph7+/v9H0mzdvws7OrswKqw6EEMjScmxeoqosIyMDCxcuRGZmJlQqFXr37o3GjRtLXRYRUZVhcpgdPHgwRo8eja+++gpt27aFTCbDgQMH8M4773BoLhMIITBs6zDE3Y+TuhQiKkc2NjZo1KgRbt68icjISMNwhkREVDZMDrNfffUVZDIZhg0bBq1WCwBQKpV49dVX8dlnn5V5gVVVljbLKMi2cGsBtUItXUFEVGZSU1NhYWFhOFsVGhoKAEbds4iIqGyY/JtVpVLhm2++wezZs3HlyhUIIVCvXj1YW1uXR33Vwp5Be+Bs5cx7rxNVARcuXMD69evh7u6OYcOGwcLCgiGWiKgclXhorszMTLz22muoWbMm3NzcMGbMGHh6eqJp06YMss9IrVAzyBKZOZ1Oh5iYGPz222/Izs6GRqNBVhb7xBMRlbcSNxdMnz4dy5cvx8svvwwrKyusWrUKr776KtasWVOe9RERVXrJycmIjo7G7du3AQCtW7dG165dOewWEVEFKHGYXbt2LZYsWYIXX3wRAPDKK6+gXbt20Ol0/IVNRNXW+fPnsWHDBuTk5MDKygr9+vVDgwYNpC6LiKjaKHGYvXnzJtq3b294HBwcDIVCgTt37sDb27tciiMiqsx0Oh12796NnJwc1KpVCwMGDICjo6PUZRERVSslDrM6nQ4qlcp4YYXCMKIBEVF1I5fLERkZiTNnziAkJIRnqYiIJFDiMCuEwIgRI2BpaWmYlp2djQkTJsDGxsYwbe3atWVbIRFRJXL27FlkZGQgODgYAODm5oYuXbpIXBURUfVV4jA7fPjwAtNeeeWVMi2GiKiy0mg0iImJwYkTJyCTyeDt7Q1PT0+pyyIiqvZKHGaXLVtWnnUQEVVaSUlJiIqKwt27dwEAL7zwAtzd3SWuioiIgFLcNIGIqDo5ffo0Nm/eDI1GAxsbG/Tv3x9169aVuiwiIvovhlkioiL88ccfOH78OADA19cXERERhlvUEhFR5cAwS0RUBFdXVwBAx44d0aFDB1hYlPimiUREVEEYZomIHpOVlQW1Wg0gbzxtHx8feHh4SFwVEREVhc0MREQAcnNzsX79eixevBg5OTkAAJlMxiBLRFTJlSrM/vzzz2jXrh28vLxw48YNAMDcuXOxYcOGMi2OiKgi3L17Fz/++CNOnTqF5ORkXLt2TeqSiIiohEwOs/Pnz8eUKVPQo0cPpKSkQKfTAQAcHR0xd+7csq6PiKjcCCFw4sQJLF68GElJSbCzs8Pw4cPh7+8vdWlERFRCJofZ7777Dj/++CM+/PBDo1s3BgUF4cyZM2VaHBFRecnJycHatWuxefNmaLVa1KtXDxMmTICPj4/UpRERkQlMvgDs2rVraNGiRYHplpaWyMjIKJOiqiIZhNQlENFjtm/fjr///hsymQxdunRB27ZtIZPJpC6LiIhMZHLLrJ+fH+Li4gpM37p1Kxo2bGhyAfPmzYOfnx+srKwQGBiI/fv3l2i5gwcPQqFQoHnz5iZvUwoWeg0AIAdKiSshIgDo3LkzatWqhZEjR6Jdu3YMskREZsrkltl33nkHr732GrKzsyGEwNGjR7Fq1SrMnj0bixcvNmldq1evxuTJkzFv3jy0a9cOCxcuRHh4OM6dO4fatWsXuVxqaiqGDRuGLl26GG4vWdkp9VkAgEdCLXElRNWTTqfDyZMnERwcDACwsbHBqFGjGGKJiMycyWF25MiR0Gq1ePfdd5GZmYkhQ4agZs2a+Oabb/Diiy+atK45c+Zg9OjRGDNmDIC8ERFiYmIwf/58zJ49u8jlxo8fjyFDhkAul2P9+vWmvgRJKHXZAIBHYJglqmgJCQm4cOECzpw5A7VajSZNmgAAgywRURVQqpsmjB07FmPHjkVSUhL0ej3c3NxMXkdubi5OnDiB999/32h6aGgoDh06VORyy5Ytw5UrV/DLL7/gk08+eep2cnJyDGNGAkBaWhoAQKPRQKPRmFx3aWg0Gij+2zKbDjU0Gg1kFlrD81qtFhpUTC1kuvz3SUW9X6jsCCFw7Ngx7Nq1C3q9Hg4ODrC3t+exNEP8HJo3Hj/zV9HH0JTtPNMdwPJv9VgaSUlJ0Ol0cHd3N5ru7u6OxMTEQpe5dOkS3n//fezfvx8KRclKnz17NmbOnFlg+vbt22FtbW164aXUTPe/bgYxMdshs8g1PBcTEwOVTFVhtVDpxMbGSl0CmUCr1eLmzZtITU0FADg4OMDb2xtxcXGF9vsn88DPoXnj8TN/FXUMMzMzSzyvyWHWz8+v2FNzV69eNWl9T65LCFHo+nU6HYYMGYKZM2eifv36JV7/1KlTMWXKFMPjtLQ0eHt7IzQ0FPb29ibVWloajQbJC+YCAB7BGmFhoZBZaDDr91kAgLCwMKgV7H5QWWk0GsTGxqJbt25QKnkBnzm4ffs21q9fj9TUVMjlcoSEhCApKQmhoaE8hmaKn0PzxuNn/ir6GOafSS8Jk8Ps5MmTjR5rNBqcPHkS27ZtwzvvvFPi9bi6ukIulxdohb13716B1loASE9Px/Hjx3Hy5Em8/vrrAAC9Xg8hBBQKBbZv347OnTsXWM7S0hKWlpYFpiuVygr9QCkea5lVKpWA7H9DdSkUCn64zUBFv2eo9DQaDVJTU+Hk5ISBAwfC1dUVW7Zs4TGsAngMzRuPn/mrqGNoyjZMDrNvvvlmodN/+OEHHD9+vMTrUalUCAwMRGxsLPr372+YHhsbi759+xaY397evsBNGebNm4ddu3YhKioKfn5+Jd62FJSP9ZklorL3+Fmd5557DhEREahfvz4sLS3ZT4+IqAozeZzZooSHhyM6OtqkZaZMmYLFixdj6dKlOH/+PN566y3Ex8djwoQJAPK6CAwbNiyvUAsLNG7c2OjLzc0NVlZWaNy4MWxsbMrqpZQLpY5DcxGVl/j4eCxYsAApKSmGaU2aNCn0rAwREVUtz3QB2OOioqLg7Oxs0jKDBw/GgwcPMGvWLCQkJKBx48bYsmWL4XaSCQkJiI+PL6sSJaXQc2guorImhMCBAwewe/duCCGwe/duozM9RERU9ZkcZlu0aGF0gZYQAomJibh//z7mzZtncgETJ07ExIkTC31u+fLlxS47Y8YMzJgxw+RtSiG/ZZbdDIjKRkZGBtatW4crV64AAJo2bYqePXtKXBUREVU0k8Nsv379jB5bWFigRo0aCAkJgb+/f1nVVbXotVCIvKG42M2A6Nldv34d0dHRePToERQKBXr06IHmzZvzJghERNWQSWFWq9XC19cXYWFh8PDwKK+aqp6cR4YfM9gyS/RMLl26hFWrVkEIgRo1aiAyMrJUN24hIqKqwaQwq1Ao8Oqrr+L8+fPlVU/VlJsOAMgWSmjKrpsyUbXk5+cHd3d3eHh4IDw8HCoVbzhCRFSdmZysWrVqhZMnTxou0qISyMkLs+wvS1Q6t27dgpeXFywsLKBQKDBixAiOVEBERABKEWYnTpyIt99+G7du3UJgYGCBIbGaNm1aZsVVFbL/hln2lyUyjV6vx549e7B//3507NgRISEhAMAgS0REBiUOs6NGjcLcuXMxePBgAMCkSZMMz8lkMsOA5TqdruyrNHf5YZYts0QllpaWhrVr1+LGjRsAgEePHhV5u2siIqq+Shxmf/rpJ3z22We4du1aedZTNeXmt8xaS1wIkXm4fPky1q1bh8zMTKhUKvTu3RuNGzeWuiwiIqqEShxmhRAAwL6ypcGWWaIS0el02L17Nw4ePAgA8PDwQGRkJFxcXCSujIiIKiuT+szy9F7pyHgBGFGJJCcn488//wQAtGzZEqGhoVAoOAIIEREVzaS/EvXr139qoH348OEzFVQl8QIwohJxdXVFr169oFQq0bBhQ6nLISIiM2BSmJ05cyYcHBzKq5aqK5fdDIgKo9PpsGvXLvj7+8Pb2xsA0KxZM4mrIiIic2JSmH3xxRd5p51SkP33DmBsmSX6n5SUFERFReH27ds4e/YsXn/9dXYpICIik5X4Lwf7yz4DwwVgVhIXQlQ5nD9/Hhs3bkR2djasrKzQvXt3BlkiIioVk0czoFLIZZ9ZIgDQarWIjY3F0aNHAQC1atXCgAED4OjoKG1hRERktkocZvV6fXnWUbVxaC4iZGVl4eeff0ZCQgIAoG3btujcuTPkcrnElRERkTnjeb0KIMvN6zObDt40gaovKysr2NvbIyUlBf369UP9+vWlLomIiKoAhtmKwKG5qJrSarXQ6/VQqVSQyWTo27cvNBoN7O3tpS6NiIiqCAupC6gW2M2AqqEHDx5g8eLF2LRpk6HPvVqtZpAlIqIyxZbZ8iYEW2ap2jlz5gw2b96M3NxcpKenIz09nSGWiIjKBcNsecvNgAx5rVK8nS1VdRqNBlu3bsXJkycBAL6+voiIiICdnZ3ElRERUVXFMFve/tsqq4cFsqGSuBii8nP//n1ERUXh3r17AICOHTuiQ4cOsLBgbyYiIio/DLPl7b9hViNXA+CNJ6hq0uv1WLVqFZKTk2Fra4uIiAj4+flJXRYREVUDDLPl7b9hVmvBLgZUdVlYWKB37944ePAg+vXrB1tbW6lLIiKiaoJhtrzlpAHIb5klqjru3r2L1NRUw3ixfn5+8PX15a2viYioQjHMlrf8bgYWVhIXQlQ2hBA4efIktm7dCgsLC4wbNw4uLi4AwCBLREQVjmG2vOV3M2DLLFUBOTk5+OOPP3DmzBkAQL169WBlxX/UiIhIOgyz5c3QMsswS+YtMTERa9aswcOHDyGTydClSxe0bduWrbFERCQphtnypssBAOgtuKvJfB0/fhzbtm2DTqeDvb09IiMj4e3tLXVZREREDLMVRXBYLjJjDx8+hE6nQ/369dG3b19YW1tLXRIREREAhlkiKoIQwtCFoEuXLvDw8ECTJk3YrYCIiCoV3pqHiIwIIXDkyBH89NNP0Ol0AAC5XI6mTZsyyBIRUaXDllkiMsjKysLGjRvxzz//AAD+/vtvNGvWTOKqiIiIisYwS0QAgFu3biEqKgqpqamQy+UIDQ1F06ZNpS6LiIioWAyzRNWcEAKHDx/Gzp07odfr4eTkhMjISHh5eUldGhER0VMxzBJVc7GxsTh8+DAAoFGjRujduzcsLS0lroqIiKhkGGaJqrnnn38ep06dQqdOnRAYGMiLvIiIyKwwzBJVM0II3Lx5E7Vr1wYAuLq64s0334RKpZK4MiIiItNxaC6iaiQjIwMrV67E8uXLcf36dcN0BlkiIjJXbJklqiauX7+O6OhoPHr0CAqFAunp6VKXRERE9MwYZomqOL1ej/3792Pv3r0QQsDV1RUDBw6Em5ub1KURERE9M4ZZoirs0aNHWLt2La5duwYAaN68OcLDw9mtgIiIqgyGWaIq7NKlS7h27RqUSiV69uzJu3kREVGVwzBLVIU1b94cycnJaNKkCWrUqCF1OURERGWOoxkQVSHp6elYu3YtsrKyAAAymQydO3dmkCUioiqLLbNEVcTly5exbt06ZGZmAgAiIiIkroiIiKj8McwSmTm9Xo9du3bh4MGDAAAPDw907NhR4qqIiIgqBsMskRlLTU1FdHQ0bt68CQAICgpCWFgYFAp+tImIqHrgXzwiM3Xr1i38+uuvyMrKgqWlJfr06YOGDRtKXRYREVGFYpglMlMuLi5QKpVwcnJCZGQknJycpC6JiIiowjHMEpmRjIwMWFtbQyaTQa1WY9iwYXBwcGC3AiIiqrY4NBeRmTh//jy+//57nDx50jDNxcWFQZaIiKo1hlmiSk6r1WLr1q34/fffkZ2djTNnzkAIIXVZRERElQKbdIgqsYcPHyIqKgoJCQkAgDZt2qBLly6QyWQSV0ZERFQ5MMwSVVJnz57Fpk2bkJOTA7VajX79+qF+/fpSl0VERFSpMMwSVUIPHjxAdHQ0hBDw9vbGgAED4ODgIHVZRERElQ7DLFEl5OLigg4dOkCn06FTp06wsGD3diIiosIwzBJVEmfOnIGXlxdcXFwAACEhIdIWREREZAbY3EMkMY1Gg40bN2Lt2rWIioqCVquVuiQiIiKzwZZZIgndv38fUVFRuHfvHgCgfv367FJARERkAoZZIonExcVhy5Yt0Gg0sLGxQUREBOrUqSN1WURERGaFYZaogmk0Gvzxxx84deoUAMDPzw8RERGwtbWVuDIiIiLzwzBLVMEsLCyQlJQEmUyGkJAQvPDCC+xaQEREVEoMs0QVIP/2szKZDHK5HJGRkUhJSYGvr6+0hREREZk5hlmicpaTk4M//vgDdnZ26NatGwDA0dERjo6O0hZGRERUBTDMEpWjxMRErFmzBg8fPoSFhQVatmzJEEtERFSGGGaJyoEQAsePH0dMTAx0Oh3s7e0xYMAABlkiIqIyxjBLVMays7OxadMmnDt3DkDe2LF9+/aFtbW1xJURERFVPQyzRGVICIHly5fj7t27sLCwQNeuXdG6dWvIZDKpSyMiIqqSOB4QURmSyWRo27YtHBwcMHLkSLRp04ZBloiIqByxZZboGWVlZSE1NRUeHh4AgKZNmyIgIABKpVLiyoiIiKo+hlmiZ3Dr1i1ERUVBp9NhwoQJsLGxAQAGWSIiogrCMEtUCkIIHD58GDt37oRer4eTkxMyMjIMYZaIiIgqBsMskYkyMzOxYcMGXLx4EQDQsGFD9O7dG1ZWVhJXRkREVP1IfgHYvHnz4OfnBysrKwQGBmL//v1Fzrt27Vp069YNNWrUgL29Pdq0aYOYmJgKrJaqu/j4eCxcuBAXL16EXC5Hjx49EBkZySBLREQkEUnD7OrVqzF58mR8+OGHOHnyJNq3b4/w8HDEx8cXOv++ffvQrVs3bNmyBSdOnECnTp3Qu3dvnDx5soIrp+rq+PHjSEtLg7OzM8aMGYOWLVtytAIiIiIJSdrNYM6cORg9ejTGjBkDAJg7dy5iYmIwf/58zJ49u8D8c+fONXr86aefYsOGDdi0aRNatGhRESVTNdezZ0/Y2NggJCQElpaWUpdDRERU7UkWZnNzc3HixAm8//77RtNDQ0Nx6NChEq1Dr9cjPT0dzs7ORc6Tk5ODnJwcw+O0tDQAgEajgUajKUXlprHQ6SB/YppGo4HMQmt4rNVqoUH510Kmu3HjBs6ePQshBDQaDZRKJTp37gwAFfL+obKRf6x4zMwXj6F54/EzfxV9DE3ZjmRhNikpCTqdDu7u7kbT3d3dkZiYWKJ1/Oc//0FGRgYGDRpU5DyzZ8/GzJkzC0zfvn17hdxetN7df9DoiWkxMdshs8h97HEMVDJVuddCJSeEwN27dw3vRW9vb8TGxkpcFT0rHkPzx2No3nj8zF9FHcPMzMwSzyv5aAZP9jcUQpSoD+KqVaswY8YMbNiwAW5ubkXON3XqVEyZMsXwOC0tDd7e3ggNDYW9vX3pCy8hi8NXgDvG08LCQiGz0GDW77P++zgMaoW63Guhknn06BE2btxoCLKNGjWChYUFunXrxvFjzZRGo0FsbCyPoRnjMTRvPH7mr6KPYf6Z9JKQLMy6urpCLpcXaIW9d+9egdbaJ61evRqjR4/GmjVr0LVr12LntbS0LLRvo1KprJgPlPzJTgb/HVBfJgyPFQoFP9yVxNWrV7F27VpkZGRAqVSiR48eaNSoEbZs2VJx7xkqNzyG5o/H0Lzx+Jm/ijqGpmxDstEMVCoVAgMDCzRXx8bGom3btkUut2rVKowYMQK//vorevbsWd5lUjVy5MgR/Pzzz8jIyICbmxvGjh2L5s2bS10WERERFUPSbgZTpkzB0KFDERQUhDZt2mDRokWIj4/HhAkTAOR1Ebh9+zZWrFgBIC/IDhs2DN988w1at25taNVVq9VwcHCQ7HVQ1VCzZk3IZDI0b94c4eHhbD0gIiIyA5KG2cGDB+PBgweYNWsWEhIS0LhxY2zZsgU+Pj4AgISEBKMxZxcuXAitVovXXnsNr732mmH68OHDsXz58ooun6qAR48ewdbWFkDeRV4TJ06Eq6urxFURERFRSUl+AdjEiRMxceLEQp97MqDu2bOn/AuiakGv12PXrl04evQoxowZY7iIkEGWiIjIvEgeZokqWmpqKqKjo3Hz5k0AwMWLF4sdEYOIiIgqL4ZZqlYuXryI9evXIysrC5aWlujduzcaNXpyJGAiIiIyFwyzVC3odDrs3LkThw8fBgB4enoiMjKy2LvHEVFBOp1Okrs4aTQaKBQKZGdnQ6fTVfj26dnw+Jm/8jiGKpUKFhbPPrAWwyxVCydPnjQE2eDgYHTr1g0KBd/+RCUlhEBiYiJSUlIk276Hhwdu3rxZohvrUOXC42f+yuMYWlhYwM/PDyrVs90FlX/NqVp4/vnnceXKFTRt2hQBAQFSl0NkdvKDrJubG6ytrSs8kOj1esPoI2XRkkMVi8fP/JX1MdTr9bhz5w4SEhJQu3btZ/qdwjBLVZJOp8ORI0fQqlUrKBQKWFhYYPDgwVKXRWSWdDqdIci6uLhIUoNer0dubi6srKwYhswQj5/5K49jWKNGDdy5cwdarfaZxnZnmKUqJzk5GVFRUbhz5w5SU1PRo0cPqUsiMmv5fWStra0lroSIqpL87gU6nY5hlijfuXPnsHHjRuTk5ECtVqNevXpSl0RUZbCvIxGVpbL6ncIwS1WCVqtFTEwMjh8/DiDvbl4DBgzgbY6JiIiqOHZcIbP38OFDLFmyxBBk27Vrh+HDhzPIElGF8PX1xdy5c0u9/PLly+Ho6Fhm9VQlISEhmDx5coVs6//+7/8wbty4CtlWdREZGYk5c+aU+3YYZsnsyWQyJCcnw9raGi+//DK6du0KuVwudVlEVAmMGDEC/fr1K9dtHDt2rMQhqLDgO3jwYFy8eLHU21++fDlkMpnhy93dHb1798bZs2dLvc7KYu3atfj444/LfTt3797FN998gw8++KDAc4cOHYJcLkf37t0LPLdnzx7IZLJCh6xr3rw5ZsyYYTTt5MmTGDhwINzd3WFlZYX69etj7Nixz3T8S2LevHnw8/ODlZUVAgMDsX///qcus3LlSjRr1gzW1tbw9PTEqFGj8PDhQ8PzGo0Gs2bNQt26dWFlZYVmzZph27ZtRuuYNm0a/v3vfyMtLa3MX9PjGGbJLOn1esPPTk5OGDx4MMaPH88+skRU4WrUqPFMF8ep1epnvqW2vb09EhIScOfOHfzxxx/IyMhAz549kZub+0zrfZryvoGGs7Mz7OzsynUbALBkyRK0adMGvr6+BZ5bunQp3njjDRw4cADx8fGl3sbmzZvRunVr5OTkYOXKlTh//jx+/vlnODg44P/+7/+eofrirV69GpMnT8aHH36IkydPon379ggPDy/2tRw4cADDhg3D6NGjcfbsWaxZswbHjx/HpEmTDPN89NFHWLhwIb777jucO3cOEyZMQP/+/XHy5EnDPE2bNoWvry9WrlxZbq8PYJglM3T//n0sWrQIly9fNkzz8/ODvb29hFURkTnau3cvgoODYWlpCU9PT7z//vvQarWG59PT0/Hyyy/DxsYGnp6e+Prrrwuc+n6ytXXGjBmoXbs2LC0t4eXlZQgAISEhuHHjBt566y1DKypQeDeDjRs3IigoCFZWVnB1dUVERESxr0Mmk8HDwwOenp4ICgrCW2+9hRs3buDChQuGeQ4dOoQOHTpArVbD29sbkyZNQkZGhuH5hIQE9OzZE2q1Gn5+fvj1118LvDaZTIYFCxagb9++sLGxwSeffAIA2LRpEwIDA2FlZYU6depg5syZRvtx5syZaNy4MdRqtdE+AfJaDZ977jlYWVnB3d0dkZGRhuee3NfJyckYNmwYnJycYG1tjfDwcFy6dMnwfP6+jImJQUBAAGxtbdG9e3ckJCQUu/9+++039OnTp8D0jIwM/P7773j11VfRq1cvLF++vNj1FCUzMxMjR45Ejx49sHHjRnTt2hV+fn5o1aoVvvrqKyxcuLBU6y2JOXPmYPTo0RgzZgwCAgIwd+5ceHt7Y/78+UUuc+TIEfj6+mLSpEnw8/PDCy+8gHHjxhkF1Z9//hkffPABevTogTp16uDVV19FWFgY/vOf/xitq0+fPli1alW5vT6AYZbMzKlTp/Djjz/i7t27iI2NhRBC6pKIqiUhBDJztRX6lZWrK9PP/O3bt9GjRw+0bNkSp06dwvz587FkyRJDQAOAKVOm4ODBg9i4cSNiY2Oxf/9+/PXXX0WuMyoqCl9//TUWLlyIS5cuYf369WjSpAmAvFPmtWrVwqxZs5CQkFBkwPrjjz8QERGBnj174uTJk9i5cyeCgoJK/LpSUlLw66+/AoBhuKMzZ84gLCwMEREROH36NFavXo0DBw7g9ddfNyw3bNgw3LlzB3v27EF0dDQWLVqEe/fuFVj/9OnT0bdvX5w5cwajRo1CTEwMXnnlFUyaNAnnzp3DwoULsXz5cvz73/827JO5c+fi66+/xoULF4z2SX5r36xZs3DhwgVs27YNHTp0KPK1jRgxAsePH8fGjRtx+PBhCCHQo0cPoxbizMxMfPXVV/j555+xb98+xMfH41//+leR60xOTsbff/9d6D5evXo1GjRogAYNGuCVV17BsmXLSvUejImJQVJSEt59991Cny+uz/SECRNga2tb7FdRray5ubk4ceIEQkNDjaaHhobi0KFDRW6zbdu2uHXrFrZs2QIhBO7evYvo6Gij9eTk5MDKyspoObVajQMHDhhNCw4OxtGjR5GTk1Pk9p4VRzMgs5Cbm4utW7ciLi4OQF5LbEREBIcKIpJIlkaHhtNiKny7f8/oBtsy6hM/b948eHt74/vvv4dMJoO/vz/u3LmD9957D9OmTUNGRgZ++ukn/Prrr+jSpQsAYNmyZfDy8ipynfHx8fDw8EDXrl2hVCpRu3ZtBAcHA8g7ZS6Xy2FnZwcPD48i1/Hvf/8bL774ImbOnGmY1qxZs2JfS2pqKmxtbfP+ycjMBJDXIubv7w8A+PLLLzFkyBBDK+dzzz2Hb7/9Fh07dsT8+fNx/fp17NixA8eOHTOEusWLF+O5554rsK0hQ4Zg1KhRhsdDhw7F+++/j+HDhwMA6tSpg48//hjvvvsupk+fbtgnISEhcHFxga+vr2GfxMfHw8bGBr169YKdnR18fHzQokWLQl/jpUuXsHHjRhw8eBBt27YFkNev09vbG+vXr8fAgQMB5HV9WLBgAerWrQsAeP311zFr1qwi992NGzcghCj0uC5ZsgSvvPIKAKB79+549OgRdu7cia5duxa5vqJqB2A4HqaYNWtWsWEcQJHvyaSkJOh0Ori7uxtNd3d3R2JiYpHra9u2LVauXInBgwcjOzsbWq0WvXv3xhdffGGYJywsDHPmzEGHDh1Qt25d7Ny5Exs2bIBOpzNaV82aNZGTk4PExET4+Pg87eWWCltmqdK7d+8efvzxR8TFxUEmkyEkJASvvPIKbG1tpS6NiMzY+fPn0aZNG6N/itu1a4dHjx7h1q1buHr1KjQajSF4AYCDgwMaNGhQ5DoHDhyIrKws1KlTB2PHjsW6deuMTreXRFxcnCE8l5SdnR3i4uJw4sQJQ5BbsGCB4fkTJ05g+fLlRq15YWFh0Ov1uHbtGi5cuACFQoHnn3/esEy9evXg5ORUYFtPtmCeOHECs2bNMlr32LFjkZCQgMzMTMM+ad68OcaNG2e0T7p16wYfHx/UqVMHQ4cOxcqVKw1h/Ennz5+HQqFAq1atDNNcXFzQoEEDnD9/3jDN2traEGQBwNPTs9AW5nxZWVkAUKCV8cKFCzh69ChefPFFAIBCocDgwYOxdOnSItdVlGc5o+Dm5oZ69eoV+6VQFN82+WTDjxCi2Magc+fOYdKkSZg2bRpOnDiBbdu24fr165gyZYphnm+++QbPPfcc/P39oVKp8Prrr2PkyJEFLsBWq9UAUORxLQtsmaVKLTk5GT/++CO0Wi1sbW0xYMCAQjvoE1HFUivlODcrrMK2p9frkZ6WDrWy7EYqKewPen7okMlkRj8XNk9hvL29ceHCBcTGxmLHjh2YOHEivvzyS+zdu7fEdzjK/+NvCgsLC8MFsP7+/khMTMTgwYOxb98+AHn7b/z48UZ9VfPVrl3bqG/t4wp7rTY2NkaP9Xo9Zs6cWWi/XisrK3h7e+P8+fPYsGEDDh8+bLRP7Ozs8Ndff2HPnj3Yvn07pk2bhhkzZuDYsWMFTr0Xtd+fPI5P7ufHj2VhXF1dAeT9valRo4Zh+pIlS6DValGzZk2jbSmVSiQnJ8PJyclwrUZqamqBelNSUgxDRNavXx8A8M8//6BNmzZF1lKYCRMm4Jdffil2nnPnzqF27dqFvja5XF6gFfbevXsFWmsfN3v2bLRr1w7vvPMOgLwLudRqNTp27IjPPvsMNWvWRI0aNbB+/XpkZ2fjwYMH8PLywvvvvw8/Pz+jdeWPgPD4vi1rbJmlSs3JyQmNGzdG3bp1MWHCBAZZokpCJpPBWqWo0C+1Sl6mXYsaNmyIQ4cOGQWdQ4cOwc7ODjVr1kTdunWhVCpx9OhRw/NpaWlGFxwVRq1Wo0+fPvj222+xZ88eHD58GGfOnAGQd/vOJ0/DPqlp06bYuXPnM7wy4K233sKpU6ewbt06AMDzzz+Ps2fPFtqqp1Kp4O/vD61Wa3SBz+XLlwsdcupJzz//PC5cuFDoui0s8mKGWq1Gjx498M033xTYJwqFAl27dsUXX3yB06dP4/r169i1a1eB7TRs2BBarRZ//vmnYdqDBw9w8eJFBAQElHpf1a1bF/b29jh37pxhmlarxYoVK/Cf//wHcXFxhq9Tp07Bx8fHcHX+c889BwsLCxw7dsxonQkJCbh9+7ahFT80NBSurq5Gp+kfV9x+njVrllENhX0V1c1ApVIhMDAQsbGxRtNjY2MNXTUKk5mZaTh2+fJbXJ/8x8DKygo1a9aEVqtFdHQ0+vbta/T833//jVq1ahn+aSgPbJmlSicxMRF2dnaG//579uwJubxs/4gRUfWRmppq6G+fz9nZGRMnTsTcuXPxxhtv4PXXX8eFCxcwffp0TJkyBRYWFrCzs8Pw4cPxzjvvwNnZGW5ubpg+fTosLCyK/H20fPly6HQ6tGrVCtbW1vj555+hVqsNfQV9fX2xb98+vPjii7C0tCz0D/z06dPRpUsX1K1bFy+++CK0Wi22bt1a5MVDhbG3t8eYMWMwffp09OvXD++99x5at26N1157DWPHjoWNjQ3Onz+P2NhYfPfdd/D390fXrl0xbtw4zJ8/H0qlEm+//TbUavVTf/dOmzYNvXr1gre3NwYOHAgLCwucPn0aZ86cwSeffILly5dDo9GgUaNGcHNzM9onmzdvxtWrV9GhQwc4OTlhy5Yt0Ov1hXbleO6559C3b1+MHTsWCxcuhJ2dHd5//33UrFmzQIAyhYWFBbp27YoDBw4YxiTevHkzkpOTMXr06AI34ImMjMSSJUvw+uuvw87ODuPHj8fbb78NhUKBZs2a4c6dO/jwww8REBBguGDKxsYGixcvxsCBA9GnTx9MmjQJ9erVQ1JSEn7//XfEx8fjt99+K7Q+Nze3Zxq6bcqUKRg6dCiCgoLQpk0bLFq0CPHx8ZgwYYJhnqlTp+L27dtYsWIFAKB3794YO3Ys5s+fj7CwMCQkJGDy5MkIDAw0BOc///wTt2/fRvPmzXH79m3MmDEDer2+wPt0//79BS5AK3OimklNTRUARGpqasVs8MBcIabbi6tzewif9zYLn/c2i4wcjcjIzRCNlzcWjZc3Fhm5GRVTSyWn1+vFsWPHxMcffyx++eUXodfrpS5JCCFEbm6uWL9+vcjNzZW6FColHsNnk5WVJc6dOyeysrIkq0Gn04nk5GSh0+lMWm748OECQIGv4cOHCyGE2LNnj2jZsqVQqVTCw8NDvPfee0Kj0RiWT0tLE0OGDBHW1tbCw8NDzJkzRwQHB4v333/fMI+Pj4/4+uuvhRBCrFu3TrRq1UrY29sLGxsb0bp1a7Fjxw7DvIcPHxZNmzYVlpaWIv9P8LJly4SDg4NR3dHR0aJ58+ZCpVIJV1dXERERUeRrLGx5IYS4ceOGUCgUYvXq1UIIIY4ePSq6desmbG1thY2NjWjatKn497//bZj/zp07Ijw8XFhaWgofHx/x66+/Cjc3N7FgwQLDPADEunXrCmxr27Ztom3btkKtVgt7e3sRHBwsFi1aZLRP7OzsCuyT/fv3i44dOwonJyehVqtF06ZNDfUKIUTHjh3Fm2++aXj88OFDMXToUOHg4CDUarUICwsTFy9eLHZfrFu3Tjwt7mzbtk3UrFnT8P7q1auX6NGjR6HznjhxQgAQJ06cEEIIkZ2dLWbNmiUCAgKEWq0WPj4+YsSIESIhIaHAsseOHRMRERGiRo0awtLSUtSrV0+MGzdOXLp0qdj6ntUPP/wgfHx8hEqlEs8//7zYu3ev0fPDhw8XHTt2NJr27bffioYNGwq1Wi08PT3FkCFDxNmzZw37aM+ePSIgIEBYWloKFxcXMXToUHH79m2jdWRlZQl7e3tx+PDhQusq7neLKXlNJkT1GtsoLS0NDg4OSE1NrZhxSQ9+A8ROwzWnF9ApYSIA5PUzk+Wi1a95ndj/HPInrJWlH3C7KsjOzsbmzZsNd6x57rnnEBkZCZVKJXFleVfGbtmyBT169ChxnzeqXHgMn012djauXbtmuIOQFPR6PdLS0mBvb1/g9GdFysjIQM2aNfGf//wHo0ePlqyOinDr1i14e3tjx44dJl+Q9qTKcvyKIoRA69atMXnyZLz00ktSl1MpleYY/vDDD9iwYQO2b99e6PPF/W4xJa+xmwFJ7s6dO4iKikJycjIsLCzQpUuXAlcYExFJ4eTJk/jnn38QHByM1NRUwxBPz3Jau7LatWsXHj16hCZNmiAhIQHvvvsufH19ix33taqQyWRYtGgRTp8+LXUpVYpSqcR3331X7tthmCXJCCFw9OhRxMbGQqfTwcHBAZGRkahVq5bUpRERGXz11Ve4cOGC4WKa/fv3l+vFLFLRaDT44IMPcPXqVdjZ2RnGGq0uZzOaNWv21PF8yTTjxo2rkO0wzJJkNBoN/vzzT+h0OjRo0AB9+/Yt1ZA0RETlpUWLFjhx4oTUZVSIsLAwhIVV3HBrRGWFYZYko1KpEBkZifj4eLRq1YrdCoiIiMhkDLNUYYQQOHLkCJRKpeEOMl5eXsXeGpKIiIioOAyzVCGysrKwfv16XLx4EXK5HHXq1IGzs7PUZREREZGZY5ilcnfz5k1ERUUhLS0NcrkcYWFhhd7vm4iIiMhUDLNUboQQOHjwIHbt2gUhBJydnTFw4EB4eHhIXRoRERFVEQyzVC6EEPjtt99w8eJFAEDjxo3Rq1cvWFpaSlwZERERVSWV7zYcVCXIZDLUqlULCoUCvXv3RkREBIMsEVU7vr6+mDt3rtRlmJ3c3FzUq1cPBw8elLqUKuPevXuoUaMGbt++LXUpZY5hlsqMXq/Ho0ePDI9feOEFvPrqq3j++ec57BYRSWLEiBGQyWSQyWRQKBSoXbs2Xn31VSQnJ0tdWrmaMWOG4XU//rVjxw7Javrss8/w/PPPl2jeRYsWwcfHB+3atSvw3Lhx4yCXy/Hbb78VeG7EiBHo169fgelxcXGQyWS4fv26YZoQAosWLUKrVq1ga2sLR0dHBAUFYe7cucjMzCzx6zJVcnIyhg4dCgcHBzg4OGDo0KFISUkpdpm7d+9ixIgR8PLygrW1Nbp3745Lly4ZzZOYmIihQ4fCw8MDNjY2eP755xEVFWV43s3NDUOHDsX06dPL42VJimGWysSjR4+wcuVKrFixAhqNBkBe6yxHLCAiqXXv3h0JCQm4fv06Fi9ejE2bNmHixIlSl1XuGjVqhISEBKOv0t6aNjc3t4yrK953332HMWPGFJiemZmJ1atX45133sGSJUueaRtDhw7F5MmT0bdvX+zevRtxcXH4v//7P2zYsAHbt29/pnUXZ8iQIYiLi8O2bduwbds2xMXFYejQoUXOL4RAv379cPXqVWzYsAEnT56Ej48PunbtioyMDKPXc+HCBWzcuBFnzpxBREQEBg8ejJMnTxrmGTlyJFauXFnl/pljmKVndu3aNSxYsABXr15FSkoKEhISpC6JiMqbEEBuRsV+aTLztmsiS0tLeHh4oFatWggNDcXgwYONwopOp8Po0aPh5+cHtVqNBg0a4JtvvjFaR36L31dffQVPT0+4uLjgtddeM/zzDuSdxu3duzfUajX8/PywcuXKArXEx8ejb9++sLW1hb29PQYNGoS7d+8anp8xYwaaN2+OpUuXonbt2rC1tcWrr74KnU6HL774Ah4eHnBzc8O///3vp75uhUIBDw8Poy+VSgUAOHPmDDp37gy1Wg0XFxeMGzfO6Mxa/uudPXs2vLy8UL9+fQDA7du3MXjwYDg5OcHFxQV9+/Y1au3cs2cPgoODYWNjA0dHR7Rr1w43btzA8uXL8fnnn+PUqVOGVuLly5cXWvdff/2Fy5cvo2fPngWeW7NmDRo2bIipU6fi4MGDRts2xe+//46VK1di1apV+OCDD9CyZUv4+vqib9++2LVrFzp16lSq9T7N+fPnsW3bNixevBht2rRBmzZt8OOPP2Lz5s24cOFCoctcunQJR44cwfz589GyZUs0aNAA8+bNw6NHj7Bq1SrDfIcPH8Ybb7yB4OBg1KlTBx999BEcHR3x119/GeZp0qQJPDw8sG7dunJ5fVLhBWBUanq9Hnv37sW+ffsAADVq1MDAgQNRo0YNiSsjonKnyQQ+rbgbnlgAcASgf/8WILcr9XquXr2Kbdu2QalUGqbp9XrUqlULv//+O1xdXXHo0CGMGzcOnp6eGDRokGG+3bt3w9PTE7t378bly5cxePBgNG/eHGPHjgWQFwBv3ryJXbt2QaVSYdKkSbh3755h+fwWNhsbG+zduxdarRYTJ07E4MGDsWfPHsN8V65cwdatW7Ft2zZcuXIFkZGRuHbtGurXr4+9e/fi0KFDGDVqFLp06YLWrVubvA8yMzPRvXt3tG7dGseOHcO9e/cwZswYvP7660YBc+fOnbC3t0dsbCyEEMjMzESnTp3Qvn177Nu3DwqFAp988gm6d++O06dPw8LCAv369cPYsWOxatUq5Obm4ujRo5DJZIYWwt27dxu6Ojg4OBRa3759+1C/fn3Y29sXeG7JkiV45ZVX4ODggB49emDZsmWYOXOmyftg5cqVhtuoP0kmkxVZGwDY2toWu+727dtj69athT53+PBhODg4oFWrVoZprVu3hoODAw4dOoQGDRoUWCYnJwcAYGVlZZgml8uhUqlw4MABQwv2Cy+8gNWrV6Nnz55wdHTE77//jpycHISEhBitLzg4GPv378eoUaOKfR3mhGGWSiU9PR1r1641/FfcokULhIeHG/2BICKqDDZv3gxbW1vodDpkZ2cDAObMmWN4XqlUGgUiPz8/HDp0CL///rtRmHVycsL3338PuVwOf39/9OzZEzt37sTYsWNx8eJFbN26FUeOHDEElSVLliAgIMCw/I4dO3D69Glcu3YN3t7eAICff/4ZjRo1wrFjx9CyZUsAeeF66dKlsLOzQ8OGDdGpUydcuHABW7ZsgYWFBRo0aIDPP/8ce/bsKTbMnjlzxih4NWzYEEePHsXKlSuRlZWFFStWwMbGBgDw/fffo3fv3vj888/h7u4OALCxscHixYsNrblLly6FhYUFFi9ebLgOYtmyZXB0dMSePXsQFBSE1NRU9OrVC3Xr1gUAw+vX6/WwsbExtBYX5/r164XeGTK/hXLt2rUAgFdeeQWTJk3C9OnTYWFh2onmS5cuFRocSyIuLq7Y59VqdZHPJSYmws3NrcB0Nzc3JCYmFrqMv78/fHx8MHXqVCxcuBA2NjaYM2cOEhMTjc6Erl69GoMHD4aLiwsUCgWsra2xbt06w7HIV7NmTaOuB1UBwyyVytatW3H9+nUolUr06tULTZs2lbokIqpISmvggzsVtjm9Xo+09HTYK61NXrZTp06YP38+MjMzsXjxYly8eBFvvPGG0TwLFizA4sWLcePGDWRlZSE3NxfNmzc3mqdRo0aQy+WGx56enjhz5gyAvNPHCoXCcKtuIC+EODo6Gh6fP38e3t7ehiAL5AVMR0dHnD9/3hBmfX19YWf3v9Znd3d3yOVyo8Dm7u5u1OpbmAYNGmDjxo2Gx/kjypw/fx7NmjUzBFkAaNeuHfR6PS5cuGAIs02aNDEEWQA4ceIELl++bFQbAGRnZ+PKlSsIDQ3FiBEjEBYWhm7duqFr164YNGgQPD09i63zSVlZWUatkPmWLFmCsLAwuLq6AgB69OiB0aNHY8eOHQgNDTVpG0KIUl+YXK9evVItl6+w7RZXj1KpRHR0NEaPHg3n/2/vvsOauv4/gL8TkkAYAUVkDxERHCiiIFhFLCKlKioqKnUvqhZHrdUuUGt96kCtdX0tggMU2wr6rVTFFhTHFxlSBwqICA6sdTBkh5zfH/64NYJoUEbw83qePJpzz733c3IgfHJy7rlt20JFRQXu7u744IMP5Op99dVXePLkCU6ePIl27dohOjoaY8aMQUJCArp3787VE4vFjXqBW3OgZLYJMADlkAG8ZxPoy6Rl3P+VlaenJ8rLy+Hl5cW9sRBC3iE8HiDSeHW9t0UmA4TVz86rIA0NDS4B+eGHH+Dm5obly5dj5cqVAJ7Nn1y4cCHWr18PZ2dnaGlpYe3atUhMTJQ7zovfPPF4PMhkMgDPkpGaspd5WcLyYnld56nv3C8jEonqTLzqS5yeL38+2QWefaBwcHCocy5wzfSy0NBQBAQE4NixY4iMjMRXX32F2NhYODo61hvr89q1a8d9SKhRXV2NPXv24P79+xAIBHLlISEhXDIrkUiQm5tb65g1qwXUTB+wtrbGtWvXXjum573JNAMDAwO5OdI1/vnnH+5DRF0cHByQlpaGwsJCVFZWQk9PD05OTtyHp+zsbPz444+4cuUKunbtCgDo0aMHEhISsGXLFmzfvp071uPHj1vddEBKZhsZYwyTDPWRpnYHWjrfAAAG/vxNM0eluKKiIly/fp17Q5JIJJg0aVIzR0UIIYoLDAzEBx98gI8//hhGRkZISEiAi4uL3AoH2dnZCh3T1tYWUqkUycnJ3PtkRkaG3JJLXbp0QV5eHm7fvs2Nzqanp6OwsFBuOkJj69KlC3bv3o2SkhIuYT179iz4fD53oVddevXqhcjISLRv377O+aw17O3tYW9vj2XLlsHZ2RkRERFwdHSEUChEdXX1K+Ozt7fHtm3b5JLumJgYFBcX4+LFi3Kj49evX4efnx8ePXoEXV1d2NjYYP/+/SgvL5cb3U1KSoKenh53K/UJEyZg3LhxOHz4cK15s4wxFBUVvXTe7JtMM3B2dkZhYSEuXLjA/ZwkJiaisLAQLi4u9R4X+DcZz8rKQnJyMveBrGak9cXpFioqKrU+9Fy5cqXWPFplR6sZNLIyJkWa2stvFmDf3h5iwct/8FuCrKwsbN++Hb///juuX7/e3OEQQsgbGThwILp27YrvvvsOwLOvjZOTk3H8+HFkZmbi66+/RlJSkkLH7Ny5Mzw9PTFz5kwkJiYiJSUFM2bMkEts3N3dYWdnBz8/P6SmpuLChQuYNGkSXF1d5aYnNDY/Pz+oqalh8uTJuHLlCuLi4vDJJ59g4sSJ9Y4O+vn5oV27dvD29kZCQgJycnJw6tQpzJ8/H3fu3EFOTg6WLVuG8+fPIzc3FydOnEBmZiaXqJuZmSEnJwdpaWl4+PAhd2HTi9zc3FBSUoKrV69yZSEhIfjwww/Ro0cPdOvWjXv4+PhAT08P+/bt42IUCASYOHEikpOTkZ2djX379mH16tX47LPPuOONHTsWvr6+GD9+PFavXo3k5GTk5ubit99+g7u7O+Li4l76OlhZWdX7MDY2fum+tra23M/J//73P/zvf//DzJkzMXToULk5vDY2NnIrDvz888+Ij4/nlucaPHgwRowYwY1I29jYwMrKCrNnz8aFCxeQnZ2N9evXIzY2Vm7d3dLSUqSkpCg8LaOlo2S2CT3N/ArF11cgfsw5JE5IROKEROz23N1ibyhQXV2N2NhYREREoKysDIaGhnVOXCeEEGWzaNEi7Ny5E7dv34a/vz+3JqeTkxMePXrUoHVoQ0NDYWpqCldXV4waNQqzZs2Se8/k8XiIjo5GmzZtMGDAALi7u8PS0hKRkZFvs2mvpK6ujuPHj+Px48fo06cPRo8ejffffx8//vjjK/c7ffo0zMzMMGrUKNja2mLatGkoKyuDRCKBuro6rl+/Dh8fH1hbW2PWrFmYN28eZs+eDQAYPnw4hgwZAjc3N+jp6cktK/U8XV1djBo1ipvO8Pfff+Po0aPw8fGpVZfH42HUqFHcmrPa2tpISEjgVo7o0aMH1qxZg5UrV+LTTz+V2y8iIgLBwcGIioqCq6sr7OzsEBQUBG9vbwwZMqRBr+3rCA8PR/fu3eHh4QEPDw/Y2dlh7969cnUyMjJQWFjIPc/Pz8fEiRNhY2ODgIAATJw4Ue71EwqFiImJgZ6eHoYNGwY7Ozvs2bMHu3fvhpeXF1fv8OHDMDMzQ//+/Rutfc2Bx1gDFu1TYjVfHRQWFtb7NcnbUpqwDk43dwMAiq+vAJgI6SuGQF3Usmd4FBQU4Ndff8WdO3cAPFvKY/DgwXJzld4VVVVViImJgZeXF63WoKSoD99MeXk5cnJy0KFDhzovzGkKMpkMRUVFkEgkCl+5Tpqfov13+fJluLu713nBGWk4R0dHLFiwABMmTFB438b4HazvvUWRfO3dy0zIK2VmZiIqKgrl5eVQVVWFt7d3k87nIoQQ8m7r3r071qxZg1u3bsldiU8a7sGDBxg9ejTGjx/f3KG8dZTMklqkUinKy8thbGwMHx8fbsI8IYQQ0lQmT57c3CG0Ku3bt8eSJUuaO4xGQcksAfDs64Oarw26dOmCsWPHwtraWu6qUUIIIYSQloYmHhGkp6djy5YtKC4u5spsbW0pkSWEEEJIi0fJ7DtMKpXi6NGj+Pnnn/H48WOcPXu2uUMihBBCCFEITTN4Rz169Ai//PILdy/ofv36wc3NrZmjIoQQQghRDCWz76ArV67gv//9LyorK6Guro4RI0agU6dOzR0WIYQQQojCKJl9x/z111+Ijo4G8OxuLD4+Pk2y3i4hhBBCSGOgZPYdY2tri7Nnz8LGxgYDBw6kxccJIYQQotQok3kHZGdno+ZGbyKRCLNmzcKgQYMokSWEkHdARkYGDAwM5FasIW/mt99+g729PWQyWXOHQkDJbKtWWVmJw4cPY9++fTh37hxX/i7ekpYQ8u6prq6Gi4sLfHx85MoLCwthamqKr776Sq78119/xaBBg9CmTRuoq6ujc+fOmDZtGi5evMjVCQsLA4/H4x6amppwcHDAoUOHmqRNNQYOHIgFCxa8Vt0vv/wSc+fOrfO2sJ07d4ZIJMLdu3drbbOwsMDGjRtrlW/cuBEWFhZyZUVFRfjyyy9hY2MDNTU1GBgYwN3dHYcOHeIGUxrD5cuX4erqCrFYDGNjY6xYseKV50tNTcXgwYOho6MDXV1dzJo1C0+fPuW2P3r0CJ6enjAyMoKqqipMTU0xb948FBUVcXWGDh0KHo+HiIiIRmsbeX2UzLZSDx48wE8//YS0tDTweDz69EgIeeeoqKhg9+7dOHbsGMLDw7nyTz75BG3btsU333zDlX3++efw9fVFz549ceTIEVy9ehX/+c9/0LFjR3zxxRdyx5VIJMjPz0d+fj4uXryIIUOGYOzYscjIyGiytr2uO3fu4MiRI5g6dWqtbWfOnEF5eTnGjBmDsLCwBp+joKAALi4u2LNnD5YtW4bU1FScPn0avr6+WLJkCQoLC9+gBS9XVFSEwYMHw8jICElJSdi8eTPWrVuH4ODgl+5z7949uLu7w8rKComJiTh27BiuXr2KKVOmcHX4fD68vb1x5MgRZGZmIiwsDCdPnoS/v7/csaZOnYrNmzc3StuIgtg7prCwkAFghYWFTXK+ktNrWbewbqxbWDdmvvQQM//8N1ZSUdVo55PJZCw1NZV9++23LCgoiK1bt47l5OQ02vneBZWVlSw6OppVVlY2dyikgagP30xZWRlLT09nZWVlXJlMJmMllSVN9iguL2b3/rnHpFKpwvFv2rSJtWnTht29e5dFR0czoVDILl68yG0/f/48A8A2bdpU5/4ymYz7f2hoKNPW1pbbXl1dzYRCITt48CBX9vjxYzZx4kSmo6PDxGIx8/T0ZJmZmXL7/fLLL6xLly5MJBIxc3Nztm7dOrntW7ZsYVZWVkxVVZW1b9+e+fj4MMYYmzx5MgMg93jZ+/z69etZ796969w2ZcoUtnTpUvb7778zS0tLuXYyxpi5uTnbsGFDrf02bNjAzM3Nuecff/wx09DQYHfv3q1Vt7i4mFVVVbHq6mr25MkTVl1dXWcsDbF161amra3NysvLubLVq1czIyOjWm2psWPHDta+fXu5OC5evMgAsKysrJeea9OmTczExESu7NatWwwAy87OfsOWKIfG6MO63ltqKJKv0ffNrUhlZSV+++03XL58GQDQsWNHjBw5EhoaGs0cGSGktSmTlsEpwqnJz3t+3HloqmgqtM8nn3yCqKgoTJo0CZcvX8Y333yDnj17ctv3798PTU1NzJkzp879eTzeS49dXV2NPXv2AAB69erFlU+ZMgVZWVk4cuQIJBIJPv/8c3h5eSE9PR1CoRApKSkYO3YsgoKC4Ovri3PnzmHOnDnQ1dXFlClTkJycjICAAOzduxcuLi54/PgxEhISAACbNm1CZmYmunXrhhUrVgAA9PT06ozv9OnT6N27d63y4uJi/Pzzz0hMTISNjQ1KSkoQHx+v8HrjMpkMBw4cgJ+fH4yMjGpt19TU5Oq9KCEhAR988EG9x//iiy9qjYzXOH/+PFxdXaGqqsqVDRkyBMuWLcOtW7fQoUOHWvtUVFRAJBLJXTMiFosBPBuptrKyqrXPvXv3cOjQIbi6usqVm5ubo3379khISIClpWW97SCNi5LZVuTRo0e4evUqeDwe3Nzc8N5779X7JkwIIe8CHo+Hbdu2wdbWFt27d8fSpUvltmdmZsLS0lLueoLg4GC5aQh3796FtrY2gGdzbmuStLKyMgiFQm5KAgAuiT179ixcXFwAAOHh4TA1NUV0dDTGjBmD4OBgvP/++/j6668BANbW1khPT8fatWsxZcoU5OXlQUNDA0OHDoWWlhbMzc1hb28PANDW1oZIJIK6ujoMDAzqbfutW7fg4OBQq/zAgQPo1KkTunbtCgAYN24cQkJCFE5mHz58iCdPnsDGxkah/QCgd+/eSEtLq7dO27ZtX7rt/v37tebu6uvrc9vqSmYHDRqERYsWYe3atZg/fz5KSkq4ZDk/P1+u7vjx43H48GGUlZVh2LBh+Omnn2odz9jYGLdu3aq3DaTxUTLbihgaGmLo0KHQ1dWFmZlZc4dDCGnFxAIxEickNtn5ZDIZiouLIRaIG7T/rl27oK6ujpycHNy5c6dWEvTiB/9p06Zh+PDhSExMxEcffSR3UZGWlhZSU1MBAKWlpTh58iRmz54NXV1dDBs2DNeuXYNAIICT078j17q6uujcuTOuXbsGALh27Rq8vb3lztmvXz9s3LgR1dXVGDx4MMzNzWFpaQlPT094enpi5MiRUFdXV6jdZWVlUFNTq1UeEhKCjz76iHv+0UcfYcCAASgoKICOjs5rH7/mdWnIwIlYLK5zJFQRL573VfF07doVu3fvxqJFi7Bs2TKoqKggICAA+vr6UFFRkau7YcMGBAYGIiMjA1988QUWLVqErVu31mpDaWnpG7WBvDm6AEyJVVRUICoqSu7TpL29PSWyhJBGx+PxoC5Ub9KHWCBuUNJ0/vx5bNiwAYcPH4azszOmT58ul5x26tQJ2dnZqKqq4sp0dHRgZWUFY2PjWsfj8/mwsrKClZUV7OzssGjRIri5ueH7778HgJdeTc8Y4+J//v/Pb69RkzDv378fhoaG+Oabb9CjRw8UFBQo1PZ27drhyZMncmXp6elITEzEkiVLIBAIIBAI0LdvX5SVlWH//v1cPYlEUufFWwUFBdwotZ6eHtq0acMl6YpISEiApqZmvY/vvvvupfsbGBhwt2Sv8eDBAwD/jtDWZcKECbh//z7u3r2LR48eISgoCP/880+tkVwDAwPY2NjA29sbO3bswLZt22qN3j5+/PilUzxI06FkVknl5+djx44duHTpEg4dOkSrFRBCSB3KysowefJkzJ49G+7u7vjpp5+QlJSEHTt2cHXGjx+Pp0+f1hp1U4SKigrKysoAAF26dIFUKkVi4r8j148ePUJmZiZsbW25OmfOnJE7xrlz52Btbc2NEAoEAri7u2PNmjW4dOkSbt26hT///BPAszXDq6urXxmXvb090tPT5cpCQkIwYMAA/PXXX0hLS+MeS5YsQUhICFfPxsYGSUlJtY6ZlJSEzp07A3iW2Pv6+iI8PBz37t2rVbekpARSqbTO2GqmGdT3eHEFgec5Ozvj9OnTqKys5MpOnDgBIyOjWiPvddHX14empiYiIyOhpqaGwYMHv7RuzQeNiooKrqy8vBzZ2dnc9A/SjN7aJWlKQtlXM5DJZCwxMZGtXLmSBQUFsQ0bNrC8vLy3GDF5EV0Jr/yoD99MfVccN5WGXkkdEBDAOnbsyJ4+fcqV/ec//2GamppyKwB8+umnTEVFhS1cuJAlJCSwW7dusfPnz7OPPvqI8Xg87m9GaGgok0gkLD8/n+Xn57ObN2+yHTt2MBUVFbZ8+XLueN7e3qxLly4sISGBpaWlMU9PT2ZlZcX9DKakpDA+n89WrFjBMjIyWFhYGBOLxSw0NJQxxth///tftmnTJnbx4kV269YttnXrVsbn89mVK1cYY4zNnDmT9enTh+Xk5LB//vnnpa/LkSNHWPv27blVICorK5menh7btm1brbqZmZkMAEtLS2OMPVvlgc/ns+XLl7OrV6+yq1evshUrVjA+n8/+97//cfs9fvyY2djYMBMTE7Z792529epVlpmZyUJCQpiVlRXXb2/7SviCggKmr6/Pxo8fzy5fvswOHTrEJBKJ3KoQiYmJrHPnzuzOnTtc2ebNm1lKSgrLyMhgP/74IxOLxXIrWRw9epTt2rWLXb58meXk5LCjR4+yrl27sn79+smdPy4ujmlqarKSkpK31qaWrCWvZkDJbCN7m8lsWVkZi4yMZEFBQSwoKIjt37+flZaWvuWIyYsoEVJ+1IdvRlmT2fj4eKaiosISEhJqbfPw8GCDBg2SW8IpMjKSDRw4kGlrazOhUMhMTEzYhAkT5BK30NBQuSWxVFVVmbW1NVu1apXcsmE1S3Npa2szsVjMhgwZ8tKluYRCITMzM2Nr167ltiUkJDBXV1fWpk0bJhaLmZ2dHYuMjOS2Z2RksL59+zKxWFzv0lxSqZQZGxuzY8eOcefk8/ns/v37ddbv3r07++STT7jnsbGxrH///qxNmzasTZs27L333mOxsbG19isoKGBLly5lnTp1YiKRiOnr6zN3d3cWFRXFZDJZoyRCjDF26dIl1r9/f6aqqsoMDAxYUFCQXJ/GxcXVen0mTpzI2rZty0QiEbOzs2N79uyRO+aff/7JnJ2dmba2NlNTU2OdOnVin3/+OXvy5IlcvVmzZrHZs2e/1fa0ZC05meUx1oi35miBioqKoK2tjcLCQkgkkkY/X2nCOjjd3A0AKL6+AmAipK8YAnWRYtfeFRUVITQ0FAUFBeDz+Rg8eDCcnJxotYImUFVVhZiYGHh5eUEoFDZ3OKQBqA/fTHl5OXJyctChQ4c6LyZqCjKZDEVFRZBIJHQrbgVt3boVhw8fxvHjx5sthtbWf//88w9sbGyQnJxc56oJrVFj9GF97y2K5Gu0moGS0NLS4pYoGT16dJ0XJRBCCCEvmjVrFp48eYLi4uI6b2lLFJeTk4OtW7e+M4lsS0fJbAtWVlYGgUAAoVAIHo8HHx8f8Pn8ZhsZIYQQonwEAgG+/PLL5g6jVXF0dISjo2Nzh0H+n/KP9bdSt2/fxvbt2/H7779zZerq6pTIEkIIIYQ8h0ZmWxjGGM6dO4c//vgDjDHk5uaivLycklhCCCGEkDpQMtuClJSUIDo6Gjdu3AAAdOvWDUOHDpW77zQhhBBCCPkXJbMtRG5uLn799VcUFxdDIBDA09MTvXr1otUKCCGEEELqQclsC1BVVYWff/4ZJSUl0NXVxZgxY+q9FR8hhBBCCHmGktkWQCgUwtvbG1euXMGHH34IkUjU3CERQgghhCgFSmabSU5ODqRSKTp16gQA6NSpE/d/QgghhBDyemhpribGA8PZhNPYs2cPDh06hMLCwuYOiRBCSCOxsLDAxo0bmzuMFissLAw6OjrNHQZRcs2ezNbcQUNNTQ0ODg5ISEiot/6pU6fg4OAANTU1WFpaYvv27U0U6ZsTowpDRJk4d+ZZG21sbKCurt7MURFCSOs1ZcoU8Hg88Hg8CAQCmJmZ4eOPP8aTJ0+aO7RGV1RUhK+//hpdu3aFWCyGrq4u+vTpgzVr1rSY9vv6+iIzM7O5wyBKrlmnGURGRmLBggXYunUr+vXrhx07duCDDz5Aeno6zMzMatXPycmBl5cXZs6ciX379uHs2bOYM2cO9PT04OPj0wwteH36ZfrorZoJMa8aQqEQQ4cOhZ2dXXOHRQghrZ6npydCQ0MhlUqRnp6OadOmoaCgAPv372/u0BrN48eP8d5776GoqAgrV66Eg4MDRCIRbty4gYiICERERGDu3LnNHSbEYjHEYnFzh0GUXLOOzAYHB2P69OmYMWMGbG1tsXHjRpiammLbtm111t++fTvMzMywceNG2NraYsaMGZg2bRrWrVvXxJG/PpmMoeuTrnjv7/cg5lXjsUyMSVOnUyJLCGkVKisrX/qQSqWvXbeqquqVdRtKVVUVBgYGMDExgYeHB3x9fXHixAlue3V1NaZPn44OHTpALBajc+fO2LRpk9wxpkyZghEjRmDdunUwNDSErq4u5s6dKxf3gwcPMGzYMIjFYnTo0AHh4eG1YsnLy4O3tzc0NTUhkUgwduxY/P3339z2oKAg9OzZE7t27YKZmRk0NTXx8ccfo7q6GmvWrIGBgQHat2+PVatW1dvmL774Anl5eUhMTMTUqVNhZ2cHGxsbDB06FBEREZgzZw5Xl8fjITo6Wm5/HR0dhIWFcc/v3r0LX19ftGnTBrq6uvD29satW7e47fHx8XB0dISGhgZ0dHTQr18/5ObmAgD++usvvP/++zA1NYWOjg4cHByQnJwMoPY0g5r27927FxYWFtDW1sa4ceNQXFzM1SkuLoafnx80NDRgaGiIDRs2YODAgViwYEG9rwlpvZptZLayshIpKSlYunSpXLmHhwfOnTtX5z7nz5+Hh4eHXNmQIUMQEhKCqqoqCIXCWvtUVFSgoqKCe15UVATg2XJYL755NoZKqRQimQg88HBd2hYXqiywQiJpknOTt6Omr6jPlBf14ZupqqoCYwwymQwymUxu2+rVq1+6n5WVFcaPH889X7du3Uv7wNzcHJMmTeKeb9q0CaWlpXJ15s+fz8XxuhhjcvvcvHkTx44dg1Ao5MqkUimMjY1x4MABtGvXDufOnYO/vz/09fUxduxY7jhxcXEwMDDAH3/8gRs3bmD8+PGws7PDzJkzAQCTJ0/GnTt3cPLkSYhEIixYsAAPHjzgzs8Yw4gRI6ChoYG4uDhIpVLMmzcPvr6++PPPP7nzZGdnIyYmBjExMcjOzsbYsWNx8+ZNWFtbIy4uDufOncOMGTPg5uaGvn371mqzTCZDZGQk/Pz8YGho+NLXizEmt8+L9WrKSktL4ebmhvfeew/x8fEQCARYtWoVPD09kZaWBj6fjxEjRmDGjBkIDw9HZWUlLly4wLXbz88PPXv2xPfffw+JRIK//voLKioqcues+bem/VFRUThy5AiePHmCcePGYfXq1fj2228BAAsXLsTZs2cRHR0NfX19BAYGIjU1FT169FDoZ4MopubnRdHfwfrU/F5UVVVBRUVFbpsi79fNlsw+fPgQ1dXVtdZT1dfXx/379+vc5/79+3XWl0qlePjwIQwNDWvts3r1aixfvrxW+YkTJ5pkvqrOw4f4q81fuC++j8y8j9FBi4e42BOgeyEon9jY2OYOgbwh6sOGEQgEMDAwwNOnTxUaIZVKpdwAAiCfPDW07vMjdK+jqqoKR48ehUQiQXV1NcrLywEAq1atkjvfokWLuP8PGzYMp06dwv79++Hp6ckdR1tbG6tWrYKKigqMjIzg4eGB48ePw9fXFzdu3MCxY8cQGxuLrl27AgA2bNgAJycnlJeXo6ioCHFxcbh06RLS0tJgYmICANiyZQucnZ0RHx+PXr16oaKiAjKZDBs2bICWlhZMTEzQv39/XL9+Hfv37wefz4ePjw++//57HD9+HF26dKnV5gcPHqCgoADm5uZybRw4cCB3h8magaAaZWVltV7/mrj37dsHAFi/fj13I5+NGzfCwsICMTExsLe3R2FhIdzc3KCnpwcAGDlyJIBnA0h5eXmYO3curK2tAQAGBgbctvLycjDGuHPXtH/Tpk3Q0tKCmZkZxowZg9jYWCxZsgTFxcXYs2cPdu7ciT59+nCxdOnSBZWVlXJtII1D0d/B+lRWVqKsrAynT5+u9U3Oix9m69PsS3O9eIcrxli9d72qq35d5TWWLVsm9yZVVFQEU1NTeHh4QCKRNDTs1yaTeeK34s9x6tQpDBn3PrQ11OiuXkqmqqoKsbGxGDx4cJ2j/6Tloz58M+Xl5bh9+zY0NTWhpqYmt+3zzz9/6X58Ph8Cwb9/Zj799NOX1uXxeHJ9ExAQILedMYaKigpoaWkp9B4qFAoxcOBAbN26FaWlpQgJCUFmZiYWL14sF9v27duxa9cu5ObmoqysDJWVlejZsyf3d0IoFKJbt25o06YNt4+pqSmuXLkCiUSC27dvQyAQwNXVlRth6t27N3R0dKCmpgaJRIK8vDyYmprKJaCOjo7Q0dFBXl4eBg4cCFVVVVhYWMDY2JirY2RkBJFIJPd1vKGhIYqKiur8O1ZWVgbg2XzU57dHR0ejsrISS5cuhVQqldv2Yl0ej8fFnZ6ejps3b8LU1FTuPOXl5cjPz8eIESMwefJk+Pj4wN3dHe7u7hgzZgw3wLRw4UIEBAQgMjISHh4eGDNmDDp27AgAUFN79jex5tx1td/CwgK//fYbJBIJcnJyUFVVBVdXV24fiUSCzp07QyQSNcnf9XcVYwzFxcUK/w7Wp7y8HGKxGAMGDKj13qLIB5NmS2bbtWsHFRWVWqOwDx48eOndrwwMDOqsLxAIoKurW+c+qqqqUFVVrVUuFAqb7I+aAb8NtAQi6GiK6Q+pEmvKnxnSOKgPG6a6uho8Hg98Ph98vvylFi/+AarPm9SVyWSoqKjg4nhdPB4Pmpqa3Kjg5s2b4ebmhpUrV2LlypUAgIMHD+LTTz/F+vXr4ezsDC0tLaxduxaJiYncuXg8HkQikdy5+Xw+ZDIZ+Hw+98ddRUWlVnzPx1xX/Iwxbr+apP7F87x4bh6PB8ZYna+Fvr4+dHR0kJGRIbfdwsICwLPkr6CgQC6mF+Oqqqri+psxBgcHhzrnAOvp6YHP5yMsLAzz58/HsWPHcPDgQXz99deIjY1F3759sXz5cowfPx6HDh1CXFwcli9fjgMHDmDkyJHcOZ+Ppa72v+p1rhkIU+RngyimZmrB23ydn/+Zf/G9WZH36mbrdZFIBAcHh1pf+8XGxsLFxaXOfZydnWvVP3HiBHr37k1/oAghhLyWwMBArFu3Dvfu3QMAJCQkwMXFBXPmzIG9vT2srKyQnZ2t0DFtbW0hlUq5C5sAICMjAwUFBdzzLl26IC8vD7dv3+bK0tPTUVhYCFtb2zdr1HP4fD7Gjh2Lffv24e7du6+sr6enh/z8fO55VlaW3Fe8vXr1QlZWFtq3bw8rKyu5h7a2NlfP3t4ey5Ytw7lz59CtWzdERERw26ytrTFnzhwcP34co0aNQmhoaIPa1rFjRwiFQly4cIErKyoqQlZWVoOOR1qHZv0Is2jRIvz000/YtWsXrl27hoULFyIvLw/+/v4Ank0ReP6CAH9/f+Tm5mLRokW4du0adu3ahZCQECxevLi5mkAIIUTJDBw4EF27dsV3330H4NmFasnJyTh+/DgyMzPx9ddfIykpSaFjdu7cGZ6enpg5cyYSExORkpKCGTNmyC075e7uDjs7O/j5+SE1NRUXLlzApEmT4Orqit69e7/VNn733XcwNjaGk5MTdu3ahUuXLnEXVp0/f17uYptBgwbhxx9/RGpqKpKTk+Hv7y83QOTn54d27drB29sbCQkJyMnJwalTpzB//nzcuXMHOTk5WLZsGc6fP4/c3FycOHECmZmZsLW1RVlZGebNm4f4+Hjk5eXh7NmzSEpKanDyrqWlhcmTJ+Ozzz5DXFwcrl69imnTpsmN2pJ3T7Mms76+vti4cSNWrFiBnj174vTp04iJiYG5uTkAID8/H3l5eVz9Dh06ICYmBvHx8ejZsydWrlyJH374ocWvMUsIIaRlWbRoEXbu3Inbt2/D398fo0aNgq+vL5ycnPDo0SO5pateV2hoKExNTeHq6opRo0Zh1qxZaN++Pbe9ZgmsNm3aYMCAAXB3d4elpSUiIyPfZtMAALq6ulyyvHbtWjg6OqJ79+4ICgqCr68vdu7cydVdv349TE1NMWDAAEyYMAGLFy+Wu0BaXV0dp0+fhpmZGUaNGgVbW1tMmzYNZWVlkEgkUFdXx/Xr1+Hj4wNra2vMmjUL8+bNw+zZs6GiooJHjx5hypQp6NOnD8aNG4cPPvigzguzX1dwcDCcnZ0xdOhQuLu7o1+/frC1tVVoGgtpXXisvstLW6GioiJoa2ujsLCwySaKV1VVISYmBl5eXjQdQglR/yk/6sM3U15ejpycHO5ujc1BJpNxFzzRvEjl05j9V1JSAmNjY6xfvx7Tp09/q8cm/2qMPqzvvUWRfK3ZVzMghBBCCHldFy9exPXr1+Ho6IjCwkKsWLECAODt7d3MkZHmQsksIYQQQpTKunXrkJGRwV1MnpCQgHbt2jV3WKSZUDJLCCGEEKVhb2+PlJSU5g6DtCA08YgQQgghhCgtSmYJIYS8lnfsemFCSCN7W+8plMwSQgipV80KEIrcK50QQl6lsrISAOTWPW4ImjNLCCGkXioqKtDR0cGDBw8APFt3tKkXqJfJZKisrER5eTktzaWEqP+U39vuQ5lMhn/++Qfq6uoQCN4sHaVklhBCyCsZGBgAAJfQNjXGGMrKyiAWi+lOT0qI+k/5NUYf8vl8mJmZvfHxKJklhBDySjweD4aGhmjfvj2qqqqa/PxVVVU4ffo0BgwYQDe+UELUf8qvMfpQJBK9lVFeSmYJIYS8NhUVlTee39bQ80qlUqipqVEypISo/5RfS+5DmrhCCCGEEEKUFiWzhBBCCCFEaVEySwghhBBClNY7N2e2ZoHeoqKiJjtnVVUVSktLUVRU1OLmmZBXo/5TftSHyo/6ULlR/ym/pu7DmjztdW6s8M4ls8XFxQAAU1PTZo6EEEIIIYTUp7i4GNra2vXW4bF37P6EMpkM9+7dg5aWVpOtdVdUVARTU1Pcvn0bEomkSc5J3h7qP+VHfaj8qA+VG/Wf8mvqPmSMobi4GEZGRq9cvuudG5nl8/kwMTFplnNLJBL6JVZi1H/Kj/pQ+VEfKjfqP+XXlH34qhHZGnQBGCGEEEIIUVqUzBJCCCGEEKVFyWwTUFVVRWBgIFRVVZs7FNIA1H/Kj/pQ+VEfKjfqP+XXkvvwnbsAjBBCCCGEtB40MksIIYQQQpQWJbOEEEIIIURpUTJLCCGEEEKUFiWzhBBCCCFEaVEy+xZs3boVHTp0gJqaGhwcHJCQkFBv/VOnTsHBwQFqamqwtLTE9u3bmyhS8jKK9OGhQ4cwePBg6OnpQSKRwNnZGcePH2/CaEldFP09rHH27FkIBAL07NmzcQMkr6RoH1ZUVODLL7+Eubk5VFVV0bFjR+zatauJoiUvUrT/wsPD0aNHD6irq8PQ0BBTp07Fo0ePmiha8qLTp09j2LBhMDIyAo/HQ3R09Cv3aTH5DCNv5MCBA0woFLKdO3ey9PR0Nn/+fKahocFyc3PrrH/z5k2mrq7O5s+fz9LT09nOnTuZUChkv/zySxNHTmoo2ofz589n33//Pbtw4QLLzMxky5YtY0KhkKWmpjZx5KSGon1Yo6CggFlaWjIPDw/Wo0ePpgmW1KkhfTh8+HDm5OTEYmNjWU5ODktMTGRnz55twqhJDUX7LyEhgfH5fLZp0yZ28+ZNlpCQwLp27cpGjBjRxJGTGjExMezLL79kv/76KwPAoqKi6q3fkvIZSmbfkKOjI/P395crs7GxYUuXLq2z/pIlS5iNjY1c2ezZs1nfvn0bLUZSP0X7sC5dunRhy5cvf9uhkdfU0D709fVlX331FQsMDKRktpkp2oe///4709bWZo8ePWqK8MgrKNp/a9euZZaWlnJlP/zwAzMxMWm0GMnre51ktiXlMzTN4A1UVlYiJSUFHh4ecuUeHh44d+5cnfucP3++Vv0hQ4YgOTkZVVVVjRYrqVtD+vBFMpkMxcXFaNu2bWOESF6hoX0YGhqK7OxsBAYGNnaI5BUa0odHjhxB7969sWbNGhgbG8Pa2hqLFy9GWVlZU4RMntOQ/nNxccGdO3cQExMDxhj+/vtv/PLLL/jwww+bImTyFrSkfEbQpGdrZR4+fIjq6mro6+vLlevr6+P+/ft17nP//v0660ulUjx8+BCGhoaNFi+prSF9+KL169ejpKQEY8eObYwQySs0pA+zsrKwdOlSJCQkQCCgt8Hm1pA+vHnzJs6cOQM1NTVERUXh4cOHmDNnDh4/fkzzZptYQ/rPxcUF4eHh8PX1RXl5OaRSKYYPH47Nmzc3RcjkLWhJ+QyNzL4FPB5P7jljrFbZq+rXVU6ajqJ9WGP//v0ICgpCZGQk2rdv31jhkdfwun1YXV2NCRMmYPny5bC2tm6q8MhrUOT3UCaTgcfjITw8HI6OjvDy8kJwcDDCwsJodLaZKNJ/6enpCAgIwDfffIOUlBQcO3YMOTk58Pf3b4pQyVvSUvIZGpJ4A+3atYOKikqtT54PHjyo9WmlhoGBQZ31BQIBdHV1Gy1WUreG9GGNyMhITJ8+HT///DPc3d0bM0xSD0X7sLi4GMnJybh48SLmzZsH4FlixBiDQCDAiRMnMGjQoCaJnTzTkN9DQ0NDGBsbQ1tbmyuztbUFYwx37txBp06dGjVm8q+G9N/q1avRr18/fPbZZwAAOzs7aGhooH///vj222/pW0ol0JLyGRqZfQMikQgODg6IjY2VK4+NjYWLi0ud+zg7O9eqf+LECfTu3RtCobDRYiV1a0gfAs9GZKdMmYKIiAia49XMFO1DiUSCy5cvIy0tjXv4+/ujc+fOSEtLg5OTU1OFTv5fQ34P+/Xrh3v37uHp06dcWWZmJvh8PkxMTBo1XiKvIf1XWloKPl8+BVFRUQHw7+geadlaVD7T5JectTI1y5GEhISw9PR0tmDBAqahocFu3brFGGNs6dKlbOLEiVz9mqUsFi5cyNLT01lISAgtzdXMFO3DiIgIJhAI2JYtW1h+fj73KCgoaK4mvPMU7cMX0WoGzU/RPiwuLmYmJiZs9OjR7OrVq+zUqVOsU6dObMaMGc3VhHeaov0XGhrKBAIB27p1K8vOzmZnzpxhvXv3Zo6Ojs3VhHdecXExu3jxIrt48SIDwIKDg9nFixe55dVacj5DyexbsGXLFmZubs5EIhHr1asXO3XqFLdt8uTJzNXVVa5+fHw8s7e3ZyKRiFlYWLBt27Y1ccTkRYr0oaurKwNQ6zF58uSmD5xwFP09fB4lsy2Don147do15u7uzsRiMTMxMWGLFi1ipaWlTRw1qaFo//3www+sS5cuTCwWM0NDQ+bn58fu3LnTxFGTGnFxcfX+bWvJ+QyPMRrPJ4QQQgghyonmzBJCCCGEEKVFySwhhBBCCFFalMwSQgghhBClRcksIYQQQghRWpTMEkIIIYQQpUXJLCGEEEIIUVqUzBJCCCGEEKVFySwhhBBCCFFalMwSQgiAsLAw6OjoNHcYDWZhYYGNGzfWWycoKAg9e/ZskngIIaSpUDJLCGk1pkyZAh6PV+tx48aN5g4NYWFhcjEZGhpi7NixyMnJeSvHT0pKwqxZs7jnPB4P0dHRcnUWL16MP/74462c72VebKe+vj6GDRuGq1evKnwcZf5wQQhpOpTMEkJaFU9PT+Tn58s9OnTo0NxhAQAkEgny8/Nx7949REREIC0tDcOHD0d1dfUbH1tPTw/q6ur11tHU1ISuru4bn+tVnm/n0aNHUVJSgg8//BCVlZWNfm5CyLuHkllCSKuiqqoKAwMDuYeKigqCg4PRvXt3aGhowNTUFHPmzMHTp09fepy//voLbm5u0NLSgkQigYODA5KTk7nt586dw4ABAyAWi2FqaoqAgACUlJTUGxuPx4OBgQEMDQ3h5uaGwMBAXLlyhRs53rZtGzp27AiRSITOnTtj7969cvsHBQXBzMwMqqqqMDIyQkBAALft+WkGFhYWAICRI0eCx+Nxz5+fZnD8+HGoqamhoKBA7hwBAQFwdXV9a+3s3bs3Fi5ciNzcXGRkZHB16uuP+Ph4TJ06FYWFhdwIb1BQEACgsrISS5YsgbGxMTQ0NODk5IT4+Ph64yGEtG6UzBJC3gl8Ph8//PADrly5gt27d+PPP//EkiVLXlrfz88PJiYmSEpKQkpKCpYuXQqhUAgAuHz5MoYMGYJRo0bh0qVLiIyMxJkzZzBv3jyFYhKLxQCAqqoqREVFYf78+fj0009x5coVzJ49G1OnTkVcXBwA4JdffsGGDRuwY8cOZGVlITo6Gt27d6/zuElJSQCA0NBQ5Ofnc8+f5+7uDh0dHfz6669cWXV1NQ4ePAg/P7+31s6CggJEREQAAPf6AfX3h4uLCzZu3MiN8Obn52Px4sUAgKlTp+Ls2bM4cOAALl26hDFjxsDT0xNZWVmvHRMhpJVhhBDSSkyePJmpqKgwDQ0N7jF69Og66x48eJDp6upyz0NDQ5m2tjb3XEtLi4WFhdW578SJE9msWbPkyhISEhifz2dlZWV17vPi8W/fvs369u3LTExMWEVFBXNxcWEzZ86U22fMmDHMy8uLMcbY+vXrmbW1NausrKzz+Obm5mzDhg3ccwAsKipKrk5gYCDr0aMH9zwgIIANGjSIe378+HEmEonY48eP36idAJiGhgZTV1dnABgANnz48Drr13hVfzDG2I0bNxiPx2N3796VK3///ffZsmXL6j0+IaT1EjRvKk0IIW+Xm5sbtm3bxj3X0NAAAMTFxeG7775Deno6ioqKIJVKUV5ejpKSEq7O8xYtWoQZM2Zg7969cHd3x5gxY9CxY0cAQEpKCm7cuIHw8HCuPmMMMpkMOTk5sLW1rTO2wsJCaGpqgjGG0tJS9OrVC4cOHYJIJMK1a9fkLuACgH79+mHTpk0AgDFjxmDjxo2wtLSEp6cnvLy8MGzYMAgEDX8b9/Pzg7OzM+7duwcjIyOEh4fDy8sLbdq0eaN2amlpITU1FVKpFKdOncLatWuxfft2uTqK9gcApKamgjEGa2trufKKioommQtMCGmZKJklhLQqGhoasLKykivLzc2Fl5cX/P39sXLlSrRt2xZnzpzB9OnTUVVVVedxgoKCMGHCBBw9ehS///47AgMDceDAAYwcORIymQyzZ8+Wm7Naw8zM7KWx1SR5fD4f+vr6tZI2Ho8n95wxxpWZmpoiIyMDsbGxOHnyJObMmYO1a9fi1KlTcl/fK8LR0REdO3bEgQMH8PHHHyMqKgqhoaHc9oa2k8/nc31gY2OD+/fvw9fXF6dPnwbQsP6oiUdFRQUpKSlQUVGR26apqalQ2wkhrQcls4SQVi85ORlSqRTr168Hn//sUoGDBw++cj9ra2tYW1tj4cKFGD9+PEJDQzFy5Ej06tULV69erZU0v8rzSd6LbG1tcebMGUyaNIkrO3funNzop1gsxvDhwzF8+HDMnTsXNjY2uHz5Mnr16lXreEKh8LVWSZgwYQLCw8NhYmICPp+PDz/8kNvW0Ha+aOHChQgODkZUVBRGjhz5Wv0hEolqxW9vb4/q6mo8ePAA/fv3f6OYCCGtB10ARghp9Tp27AipVIrNmzfj5s2b2Lt3b62vvZ9XVlaGefPmIT4+Hrm5uTh79iySkpK4xPLzzz/H+fPnMXfuXKSlpSErKwtHjhzBJ5980uAYP/vsM4SFhWH79u3IyspCcHAwDh06xF34FBYWhpCQEFy5coVrg1gshrm5eZ3Hs7CwwB9//IH79+/jyZMnLz2vn58fUlNTsWrVKowePRpqamrctrfVTolEghkzZiAwMBCMsdfqDwsLCzx9+hR//PEHHj58iNLSUlhbW8PPzw+TJk3CoUOHkJOTg6SkJHz//feIiYlRKCZCSCvSnBN2CSHkbZo8eTLz9vauc1twcDAzNDRkYrGYDRkyhO3Zs4cBYE+ePGGMyV9wVFFRwcaNG8dMTU2ZSCRiRkZGbN68eXIXPV24cIENHjyYaWpqMg0NDWZnZ8dWrVr10tjquqDpRVu3bmWWlpZMKBQya2trtmfPHm5bVFQUc3JyYhKJhGloaLC+ffuykydPcttfvADsyJEjzMrKigkEAmZubs4Yq30BWI0+ffowAOzPP/+ste1ttTM3N5cJBAIWGRnJGHt1fzDGmL+/P9PV1WUAWGBgIGOMscrKSvbNN98wCwsLJhQKmYGBARs5ciS7dOnSS2MihLRuPMYYa950mhBCCCGEkIahaQaEEEIIIURpUTJLCCGEEEKUFiWzhBBCCCFEaVEySwghhBBClBYls4QQQgghRGlRMksIIYQQQpQWJbOEEEIIIURpUTJLCCGEEEKUFiWzhBBCCCFEaVEySwghhBBClBYls4QQQgghRGn9H14aCGrjhbdVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(models, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "47a368f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failure Counts:\n",
      "0    1068\n",
      "1     101\n",
      "Name: failure, dtype: int64\n",
      "\n",
      "Failure Percentages:\n",
      "0    0.857873\n",
      "1    0.081128\n",
      "Name: failure, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "failure_counts = df1[\"failure\"].value_counts()\n",
    "failure_percentages = failure_counts / len(data) * 100\n",
    "\n",
    "# Print the results\n",
    "print(\"Failure Counts:\")\n",
    "print(failure_counts)\n",
    "print(\"\\nFailure Percentages:\")\n",
    "print(failure_percentages)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c35b8021",
   "metadata": {},
   "source": [
    "## handling imbalanced dataset\n",
    "when encounter with imbalanced dataset, we always re-sample the data in order to mitigate the effect caused by class imbalance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d485acd3",
   "metadata": {},
   "source": [
    "### Using under sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "f591dcc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Define RandomUnderSampler\n",
    "undersampler = RandomUnderSampler(random_state=42)\n",
    "\n",
    "# Undersample the majority class\n",
    "X_resampled, y_resampled = undersampler.fit_resample(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a4d464c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 31)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "64243601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "===================\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.83      0.90       271\n",
      "           1       0.28      0.82      0.41        22\n",
      "\n",
      "    accuracy                           0.83       293\n",
      "   macro avg       0.63      0.82      0.66       293\n",
      "weighted avg       0.93      0.83      0.86       293\n",
      "\n",
      "TP: 18\n",
      "FN: 4\n",
      "FP: 47\n",
      "TN: 224\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "=============\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.89      0.93       271\n",
      "           1       0.38      0.82      0.51        22\n",
      "\n",
      "    accuracy                           0.88       293\n",
      "   macro avg       0.68      0.85      0.72       293\n",
      "weighted avg       0.94      0.88      0.90       293\n",
      "\n",
      "TP: 18\n",
      "FN: 4\n",
      "FP: 30\n",
      "TN: 241\n",
      "\n",
      "\n",
      "\n",
      "XGBoost\n",
      "=======\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.80      0.88       271\n",
      "           1       0.25      0.82      0.38        22\n",
      "\n",
      "    accuracy                           0.80       293\n",
      "   macro avg       0.62      0.81      0.63       293\n",
      "weighted avg       0.93      0.80      0.84       293\n",
      "\n",
      "TP: 18\n",
      "FN: 4\n",
      "FP: 54\n",
      "TN: 217\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Call evaluate_models function\n",
    "evaluate_models(X_resampled, y_resampled, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "23d7e063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(models, X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87037534",
   "metadata": {},
   "source": [
    "### Using SMOTE\n",
    "SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. This algorithm helps to overcome the overfitting problem posed by random oversampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "dbc10210",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "9f5679d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply SMOTE to balance the dataset\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9c7c2cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After SMOTE:\n",
      "0    1068\n",
      "1    1068\n",
      "Name: failure, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check the class distribution after resampling\n",
    "print(\"After SMOTE:\")\n",
    "print(y_resampled.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "c3b65f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the resampled data into train and test sets\n",
    "X_train_resampled, X_test_resampled, y_train_resampled, y_test_resampled = train_test_split(X_resampled, y_resampled, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "e59124df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1602, 31), (534, 31))"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_resampled.shape, X_test_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "cc4830f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "===================\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.86      0.89       274\n",
      "           1       0.86      0.92      0.89       260\n",
      "\n",
      "    accuracy                           0.89       534\n",
      "   macro avg       0.89      0.89      0.89       534\n",
      "weighted avg       0.89      0.89      0.89       534\n",
      "\n",
      "TP: 240\n",
      "FN: 20\n",
      "FP: 39\n",
      "TN: 235\n",
      "\n",
      "\n",
      "\n",
      "Random Forest\n",
      "=============\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97       274\n",
      "           1       0.97      0.97      0.97       260\n",
      "\n",
      "    accuracy                           0.97       534\n",
      "   macro avg       0.97      0.97      0.97       534\n",
      "weighted avg       0.97      0.97      0.97       534\n",
      "\n",
      "TP: 252\n",
      "FN: 8\n",
      "FP: 9\n",
      "TN: 265\n",
      "\n",
      "\n",
      "\n",
      "XGBoost\n",
      "=======\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.97       274\n",
      "           1       0.95      0.98      0.97       260\n",
      "\n",
      "    accuracy                           0.97       534\n",
      "   macro avg       0.97      0.97      0.97       534\n",
      "weighted avg       0.97      0.97      0.97       534\n",
      "\n",
      "TP: 255\n",
      "FN: 5\n",
      "FP: 13\n",
      "TN: 261\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "evaluate_models(X_train_resampled, y_train_resampled, X_test_resampled, y_test_resampled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "5bb407fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(models, X_test_resampled, y_test_resampled)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f7dc98",
   "metadata": {},
   "source": [
    "## Applying hyperparameter tunning on random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "19538901",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def tune_random_forest(X_train, y_train):\n",
    "    # Define the parameter grid\n",
    "    param_grid = {\n",
    "        'n_estimators': [100, 200, 300],\n",
    "        'max_depth': [None, 10, 20],\n",
    "        'min_samples_split': [2, 5, 10],\n",
    "        'min_samples_leaf': [1, 2, 4],\n",
    "        'max_features': ['auto', 'sqrt', 'log2']\n",
    "    }\n",
    "\n",
    "    # Instantiate the RandomForestClassifier\n",
    "    rf = RandomForestClassifier()\n",
    "\n",
    "    # Instantiate GridSearchCV\n",
    "    grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "    # Fit GridSearchCV to the data\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    # Print the best parameters found\n",
    "    print(\"Best parameters found:\")\n",
    "    print(grid_search.best_params_)\n",
    "\n",
    "    # Return the best estimator\n",
    "    return grid_search.best_estimator_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "6d04fe35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
      "Best parameters found:\n",
      "{'max_depth': None, 'max_features': 'log2', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    " best_rf_model = tune_random_forest(X_train_resampled, y_train_resampled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ea9b54f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_tuned_model(model, X_test, y_test):\n",
    "    # Make predictions\n",
    "    predictions = model.predict(X_test)\n",
    "\n",
    "    # Print classification report\n",
    "    print(\"Classification Report:\")\n",
    "    print(classification_report(y_test, predictions))\n",
    "\n",
    "    # Calculate TP, FN, FP, TN\n",
    "    TP = sum((predictions == 1) & (y_test == 1))\n",
    "    FN = sum((predictions == 0) & (y_test == 1))\n",
    "    FP = sum((predictions == 1) & (y_test == 0))\n",
    "    TN = sum((predictions == 0) & (y_test == 0))\n",
    "\n",
    "    # Print TP, FN, FP, TN\n",
    "    print('TP:', TP)\n",
    "    print('FN:', FN)\n",
    "    print('FP:', FP)\n",
    "    print('TN:', TN)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "f99cbc49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97       274\n",
      "           1       0.96      0.98      0.97       260\n",
      "\n",
      "    accuracy                           0.97       534\n",
      "   macro avg       0.97      0.97      0.97       534\n",
      "weighted avg       0.97      0.97      0.97       534\n",
      "\n",
      "TP: 256\n",
      "FN: 4\n",
      "FP: 11\n",
      "TN: 263\n"
     ]
    }
   ],
   "source": [
    "evaluate_tuned_model(best_rf_model, X_test_resampled, y_test_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09b7d40",
   "metadata": {},
   "source": [
    "### Applying hyperparameter tunning on xgboost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3e57fce8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "def tune_xgboost(X_train, y_train):\n",
    "    # Define the parameter grid\n",
    "    param_grid = {\n",
    "        'n_estimators': [100, 200, 300],\n",
    "        'max_depth': [3, 5, 7],\n",
    "        'learning_rate': [0.01, 0.1, 0.3],\n",
    "        'min_child_weight': [1, 3, 5],\n",
    "        'subsample': [0.5, 0.7, 0.9],\n",
    "        'colsample_bytree': [0.5, 0.7, 0.9]\n",
    "    }\n",
    "\n",
    "    # Instantiate the XGBClassifier\n",
    "    xgb_model = xgb.XGBClassifier()\n",
    "\n",
    "    # Instantiate GridSearchCV\n",
    "    grid_search = GridSearchCV(estimator=xgb_model, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "    # Fit GridSearchCV to the data\n",
    "    grid_search.fit(X_train, y_train)\n",
    "\n",
    "    # Print the best parameters found\n",
    "    print(\"Best parameters found:\")\n",
    "    print(grid_search.best_params_)\n",
    "\n",
    "    # Return the best estimator\n",
    "    return grid_search.best_estimator_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e2f1285b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 729 candidates, totalling 3645 fits\n",
      "Best parameters found:\n",
      "{'colsample_bytree': 0.9, 'learning_rate': 0.3, 'max_depth': 5, 'min_child_weight': 1, 'n_estimators': 200, 'subsample': 0.9}\n"
     ]
    }
   ],
   "source": [
    "best_xgb_model = tune_xgboost(X_train_resampled, y_train_resampled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e301fb9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.95      0.97       274\n",
      "           1       0.95      0.98      0.97       260\n",
      "\n",
      "    accuracy                           0.97       534\n",
      "   macro avg       0.97      0.97      0.97       534\n",
      "weighted avg       0.97      0.97      0.97       534\n",
      "\n",
      "TP: 255\n",
      "FN: 5\n",
      "FP: 13\n",
      "TN: 261\n"
     ]
    }
   ],
   "source": [
    "evaluate_tuned_model(best_xgb_model, X_test_resampled, y_test_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f222eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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