TVS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: imbalanced-learn in c:\\users\\asus\\appdata\\roaming\\python\\python37\\site-packages (0.6.2)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (0.13.2)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.16.5)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn>=0.22 in c:\\users\\asus\\appdata\\roaming\\python\\python37\\site-packages (from imbalanced-learn) (0.22.2.post1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.3.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install -U imbalanced-learn --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs = pd.read_csv(r'C:\\Users\\ASUS\\Desktop\\TVS.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>V29</th>\n",
       "      <th>V30</th>\n",
       "      <th>V31</th>\n",
       "      <th>V32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>46500.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>TIER 1</td>\n",
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       "      <td>2496.0</td>\n",
       "      <td>MO</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>1707.0</td>\n",
       "      <td>38900.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1346.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>38900.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2128.0</td>\n",
       "      <td>42900.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1375.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>40900.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4140.0</td>\n",
       "      <td>MC</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   V1  V2  V3    V4   V5      V6       V7    V8      V9 V10  ...  V23  \\\n",
       "0   1   0   0  24.0  0.0  2432.0  46500.0  24.0  1385.0  SC  ...  NaN   \n",
       "1   2   0   1  24.0  1.0  1495.0  28168.0  24.0  2496.0  MO  ...  NaN   \n",
       "2   3   0   0  26.0  0.0  1707.0  38900.0  30.0  1346.0  SC  ...  NaN   \n",
       "3   4   0   0  24.0  0.0  2128.0  42900.0  24.0  1375.0  SC  ...  NaN   \n",
       "4   5   0   0  27.0  0.0  1824.0  40900.0  30.0  4140.0  MC  ...  NaN   \n",
       "\n",
       "        V24      V25   V26   V27 V28  V29  V30     V31  V32  \n",
       "0       NaN  46500.0   NaN   NaN   0    0    0  TIER 1    0  \n",
       "1       NaN  28168.0   NaN   NaN   0    0    0  TIER 1    0  \n",
       "2  105000.0  38900.0  34.0  31.0  31   31   16  TIER 1    0  \n",
       "3       NaN  42900.0   NaN   NaN   0    0    0  TIER 1    0  \n",
       "4       NaN  40900.0   NaN   NaN   0    0    0  TIER 1    0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119528, 32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1          0\n",
       "V2          0\n",
       "V3          0\n",
       "V4      34480\n",
       "V5      34480\n",
       "V6      34480\n",
       "V7      34480\n",
       "V8      34480\n",
       "V9      34480\n",
       "V10     34480\n",
       "V11     34480\n",
       "V12     34480\n",
       "V13     34480\n",
       "V14     34480\n",
       "V15     35397\n",
       "V16     34480\n",
       "V17     34480\n",
       "V18         0\n",
       "V19         0\n",
       "V20         0\n",
       "V21     82902\n",
       "V22         0\n",
       "V23    100247\n",
       "V24    100500\n",
       "V25     15061\n",
       "V26    106097\n",
       "V27     99095\n",
       "V28         0\n",
       "V29         0\n",
       "V30         0\n",
       "V31         0\n",
       "V32         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V32', hue='V13', data=tvs,palette='PiYG_r')\n",
    "fig.set(xlabel='defaulter=1', ylabel='count')\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1200, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V2', hue='V13', data=tvs,palette='PuBu')\n",
    "fig.set(xlabel='Bounced=1', ylabel='count')\n",
    "\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tmum2IiJSPjS8eSWn4c1FpLRpeHMREflbFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYIiIiCkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTyiwwzp49y7333stPP/3E8ePHGThwIIMGDSImJgar1QrA/Pnz6du3LwMGDGD//v0AN9RWRETKT5kERl5eHtHR0VSrVg2A2NhYRo8ezUcffYRhGGzatImDBw/y9ddfk5SURHx8PNOmTbvhtiIiUn7KJDBmzZrFgAEDqF27NgAHDx6kTZs2AHTq1IkdO3awd+9eOnTogMViwdfXl4KCAjIyMm6orYiIlB+n0l7hypUr8fb2pmPHjrz77rsAGIaBxWIBwM3NjUuXLpGZmYmXl5dtuavzb6Stt7f3NWvJyckhNTW1tDexwggODrZ3CTesKr8fIlVdqQfGihUrsFgs7Ny5k9TUVCIjIwsdDWRlZeHp6Ym7uztZWVmF5nt4eODg4GC67fW4urpWyg/Vqkzvh0jFV9IXu1Lvklq8eDGLFi1i4cKFBAcHM2vWLDp16sTu3bsBSE5OJjQ0lJCQELZv347VaiU9PR2r1Yq3tzdNmzY13VZERMpPqR9hFCcyMpIpU6YQHx9P/fr16dq1K46OjoSGhhIWFobVaiU6OvqG24qISPmxGIZh2LuIspKamlrlu0B+nt7C3iWYFhD9vb1LEBETSvrs1A/3RETEFAWGiIiYosAQERFTFBgiImKKAkNEREwxFRhJSUmFphMTE8ukGBERqbiu+TuMzz77jM2bN7N792527doFQEFBAf/7v//LkCFDyqVAERGpGK4ZGB07duS2227j/PnzhIWFAeDg4IC/v3+5FCciIhXHNQOjRo0atG3blrZt23L27FlycnKAK0cZIiJyczE1NMi0adPYtm0btWvXto0mu2TJkrKuTUREKhBTgfHdd9+xcePGQiPJiojIzcVUAtStW9fWHSUiIjcnU0cYp06d4v7776du3boA6pISEbkJmQqM1157razrEBGRCs5UYKxatarIvOeff77UixERkYrLVGDUqlULuHJv7h9++AGr1VqmRYmISMVjKjAGDBhQaHr48OFlUoyIiFRcpgLj6NGjtv+fOXOGU6dOlVlBIiJSMZkKjD/eQ9vV1ZXx48eXWUEiIlIxmQqMhQsXcu7cOdLS0vDz88Pb27us6xIRkQrG1A/31q9fz4ABA0hISCAsLIzVq1eXdV0iIlLBmDrCWLBgAStXrsTNzY3MzEwef/xxHnnkkbKuTUREKhBTRxgWiwU3NzcA3N3dcXV1LdOiRESk4jF1hBEQEEBcXByhoaHs3buXgICAsq5LREQqGFOB0b9/f/bs2cOOHTtYu3Yt77///jXbFxQUMHnyZI4ePYqjoyOxsbEYhkFUVBQWi4WGDRsSExODg4MD8+fPZ+vWrTg5OTFx4kRatmzJ8ePHTbcVEZHyYSow4uLiiIuLIygoiCeeeIKoqCgWL15cYvstW7YAsGTJEnbv3m0LjNGjR9O2bVuio6PZtGkTvr6+fP311yQlJXHq1ClGjRrFihUriI2NNd1WRETKh6nAcHJyIigoCAB/f//r3hfjwQcf5L777gMgPT2dWrVqsXXrVtq0aQNAp06dSElJITAwkA4dOmCxWPD19aWgoICMjAwOHjxouq0u8RURKR+mAsPX15f4+Hhat27N/v37qV279vVX7OREZGQkGzZs4I033mDLli1YLBYA3NzcuHTpEpmZmXh5edmWuTr/6l39zLS9VmDk5OSQmppqZhMrpeDgYHuXcMOq8vshUtWZCozY2Fg+/vhjtm3bRoMGDXjuuedMrXzWrFmMHTuW/v37F7oBU1ZWFp6enri7u5OVlVVovoeHR6EjmOu1vRZXV9dK+aFalen9EKn4SvpiZ+qyWldXV4YOHcq0adMYMmQILi4u12z/ySef8M477wBQvXp1LBYLzZs3Z/fu3QAkJycTGhpKSEgI27dvx2q1kp6ejtVqxdvbm6ZNm5puKyIi5cPUEcaNeuihh5gwYQKDBw8mPz+fiRMn0qBBA6ZMmUJ8fDz169ena9euODo6EhoaSlhYGFar1TZmVWRkpOm2IiJSPiyGYRj2LqKspKamVvkukJ+nt7B3CaYFRH9v7xJExISSPjtNdUmJiIgoMERExBQFhoiImKLAEBERUxQYIiJiigJDRERMUWCIiIgpCgwRETFFgSEiIqYoMERExBQFhoiImKLAEBERUxQYIiJiigJDRERMUWCIiIgpCgwRETFFgSEiIqYoMERExBQFhoiImKLAEBERUxQYIiJiigJDRERMUWCIiIgpCgwRETHFqbRXmJeXx8SJEzl58iS5ubmMGDGCoKAgoqKisFgsNGzYkJiYGBwcHJg/fz5bt27FycmJiRMn0rJlS44fP266rYiIlJ9SD4w1a9bg5eXFnDlzOHfuHI8++ihNmjRh9OjRtG3blujoaDZt2oSvry9ff/01SUlJnDp1ilGjRrFixQpiY2NNtxURkfJT6oHRrVs3unbtapt2dHTk4MGDtGnTBoBOnTqRkpJCYGAgHTp0wGKx4OvrS0FBARkZGTfU1tvbu7TLFxGREpR6YLi5uQGQmZnJCy+8wOjRo5k1axYWi8X2+KVLl8jMzMTLy6vQcpcuXcIwDNNtrxcYOTk5pKamlvYmVhjBwcH2LuGGVeX3Q6SqK/XAADh16hQjR45k0KBB9OzZkzlz5tgey8rKwtPTE3d3d7KysgrN9/DwwMHBwXTb63F1da2UH6pVmd4PkYqvpC92pX6V1G+//cawYcMYN24cffv2BaBp06bs3r0bgOTkZEJDQwkJCWH79u1YrVbS09OxWq14e3vfUFsRESk/pX6EkZCQwMWLF3nrrbd46623AJg0aRIzZswgPj6e+vXr07VrVxwdHQkNDSUsLAyr1Up0dDQAkZGRTJkyxVRbEREpPxbDMAx7F1FWUlNTq3wXyM/TW9i7BNMCor+3dwkiYkJJn5364Z6IiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMERExRYEhIiKmKDBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMERExRYEhIiKmKDBERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFg3IBVq1bRsmVLWrduTefOnfnpp5/IyMggLCyMxo0bExISwrx584osd/ToUby9vfnmm28KzTcMg8cff5xXX321vDZBROQvU2CYlJ2dTXh4OCtXrmTfvn307NmTF154gRdffBF3d3d++OEHdu3axfr16/nss89sy12+fJnw8HByc3MLrS81NZUHHniA5cuXl/emSCVS3JcUgLfeeouQkBCCg4MJDw8nJycHgO+++4727dvTvHlz2rVrx+bNm23rWrt2LS1btqRx48b069ePixcv2mWbpPJSYJhUUFCAYRhcuHABgMzMTKpVq8bevXuJiIjA0dERFxcXevToUSgERo4cydChQ6lVq1ah9b355psMHz6cfv36let2SOVR0peUlStXMm/ePDZu3MjBgwfJzs5m7ty5ADzyyCMMHz6cAwcOsHLlSkaMGMEvv/zCmTNneOKJJ1ixYgU//vgj9evXJyoqys5bKJWNk70LqCzc3d1JSEigXbt23HrrrRQUFJCSkkJsbCwLFy6kffv25OTksGLFCpydnQF4//33ycvL46mnnuKVV14ptL758+cD8OWXX5b7tkjlUNKXlMTERMaMGYO3tzcACQkJ5Obm8ttvv5GWlsaQIUMA8PHxoWXLlnz++ec4Oztz991307BhQwBGjBhBq1atePPNN7FYLPbZQKl0FBgmff/990yfPp0ffviBBg0a8MYbb9CnTx+2bt3KuHHjuPPOO/Hx8aFLly7s2LGD//znPyQkJJCcnGzv0qWSKulLSq9evfj111/p1q0b6enpdOzYkdmzZ+Pm5kZgYCAffvghw4YN48iRI3z11VeEhIRgsVjw9/e3rdvPz4+LFy9y6dIlPD097biVUpmUWZfUd999R0REBADHjx9n4MCBDBo0iJiYGKxWK3DlW3bfvn0ZMGAA+/fvv+G25emLL76gffv2NGjQALjS1XTgwAEuXbrE7NmzOXDgABs3bsQwDIKCgkhMTOTixYu0a9eO1q1bk56ezuDBg1mzZk251y6V0x+/pKSnpzNp0iT69OlDXl4eGzZsYNmyZXzzzTdkZGQwadIkANasWcPy5ctp0aIF0dHR9OjRAxcXF6xWa7FHEo6OjuW9WVKJlUlgvPfee0yePNl2Ii42NpbRo0fz0UcfYRgGmzZt4uDBg3z99dckJSURHx/PtGnTbrhteQoJCWHbtm2cPn0agE8++YTAwEASEhKIjo4G4PTp07z//vsMGjSI119/nUOHDrFv3z727duHr68vixcvplevXuVeu1ROJX1JcXFx4bHHHsPT0xMXFxfCw8PZuXMnAFarlTVr1vD999+zaNEi0tLSCAoKIiAggPT0dNu6T548Sc2aNXFzc7PLtknlVCZdUgEBAcybN4/x48cDcPDgQdq0aQNAp06dSElJITAwkA4dOmCxWPD19aWgoICMjIwbanu1D7ckOTk5pKamlso21alTh4iICNq1a4ezszM1atQgPj4eX19fIiMjadiwIYZh8Mwzz+Du7l7kefPy8jh69GiRHfTChQucPn36L9UZHBz8t7bJHkrr/bgZ3HrrrWzcuJGvvvqKWrVq8eWXX3LHHXfQu3dvFixYQIcOHXB1dWXBggUEBQWRmppKREQEjz/+OF27duXbb7/lu+++w8/Pj+zsbFJSUli/fj316tUjPj6ee++9V++H3JAyCYyuXbty4sQJ27RhGLbDYTc3Ny5dukRmZiZeXl62Nlfn30jb6wWGq6trqX6ovvzyy7z88stF5m/atOm6y548ebLY+atWrfrbdVUmlTHk7CU4OJjff/+dp59+GhcXF7y9vVm/fj1NmjTBxcWFwYMHU1BQQEhICG+//Taenp4sXLiQ4cOH869//Qt3d3fWrVvHXXfdBUBiYiJRUVHk5ubSoEEDEhMTr7sPyc2ppC8S5XLS28Hh/3q+srKy8PT0xN3dnaysrELzPTw8bqitSFU3cuRIRo4cWWR+TEwMMTExReY3b96cXbt2Fbuu7t27071791KvUW4e5fI7jKZNm7J7924AkpOTCQ0NJSQkhO3bt2O1WklPT8dqteLt7X1DbUVEpPyUyxFGZGQkU6ZMIT4+nvr169O1a1ccHR0JDQ0lLCwMq9VqO3F8I21LU05eAa7OumJERKQkFsMwDHsXUVZSU1NvqM/8rnGJZVhN6ds7Zwg/T29h7zJMC4j+3t4lVBhGfg4WJ1d7l3FDKmPN8teU9NmpH+6J2IHFybVShT0o8EVjSYmIiEkKDBERMUWBISIipigwRETEFAWGiIiYosAQERFTFBgiImKKAkNERExRYMhNY9GiRbRq1YrWrVvTrl07vvnmG3Jzc3nmmWdo2rQpTZs2ZcyYMRQUFBRa7ssvv6R169ZF1mcYBo8//jivvvpqeW2CiF0pMOSm8OOPPzJu3Dg+//xz9u3bx+TJk3nssceYP38+Z86c4cCBA+zfv58dO3awbNkyALKzs5k8eTJhYWHk5+cXWl9qaioPPPAAy5cvt8fmiNiFAkNuCq6urrz//vvUqVMHgNDQUH755Reef/55li5dioODA2fPnuX8+fO2kZC/+OILsrKy+PDDD4us780332T48OH069evXLdDxJ4UGHJTqFevHj169ACudCW99NJL9OrVCxcXF5ydnYmKiqJBgwbcfvvtdOzYEYDevXszd+5cPD09i6xv/vz5DBo0qFy3QcTeFBhyU8nKyqJ///4cPnyY999/3zY/Li6Oc+fOUa9ePUaMGGHHCkUqLgWG3DR+/vln2rVrh6OjI1u2bMHLy4uUlBQOHToEgLOzM0OHDuU///mPnSsVqZgUGHJTuHTpEvfddx+PPfYYS5YsoXr16gBs3ryZF198kfz8fKxWK0YDB3MAAA5YSURBVIsXL6Zz5852rlakYlJgyE1h/vz5HD9+nFWrVtG6dWvbv6effpq6devSqlUrWrVqhZOTE7GxsfYuV4pR3GXMb731FiEhIQQHBxMeHk5OTo4dKyzqzzUXFBQwevRomjRpQlBQEAkJCUWW+eCDD+jZs2d5l2qKbqAkN4UJEyYwYcKEYh976623rrnsfffdx4EDB4p9bMGCBX+3NDEhNTWVkSNHsnv3blq0uHLjqZUrVzJv3jxSUlLw8vKiX79+zJ07l6ioKDtXe0VxNb/zzjscOnSIAwcOcOnSJe655x5CQkJo06YNGRkZTJw4kcWLF3PvvffaufriKTBEpMK7ehlzQECAbV5iYiJjxoyxXQadkJBAbm6uvUosoriaV61axdNPP42TkxM1a9ZkwIABLFq0iDZt2rBs2TJ8fX159dVX+fTTT+1YeckUGFIl5OQV4OrsaO8ypIzMnz8fuPKr+6sOHTrEr7/+Srdu3UhPT6djx47Mnj3bXiUWUVzNaWlp+Pv726b9/PzYv38/AM8++yxQsY9aFRhSJbg6O3LXuER7l2Ha3jlD7F1CpZeXl8eGDRtYvXo11apV4/HHH2fSpEm8/vrr9i6tRFarFYvFYps2DANHx8rzRUcnvUWkUvL19eWxxx7D09MTFxcXwsPD2blzp73LuqaAgADS09Nt0+np6fj5+dmxohujwBCRSqlv374sW7aM7OxsDMPgk08+4e6777Z3Wdf0yCOP8MEHH5Cfn8/58+dZsmQJvXv3tndZpqlLSkQqpeeee46MjAzuuusuCgoKCAkJ4bXXXrN3Wdc0YsQIfvrpJ1q1amUbKbmiXhFVnEoVGFarlalTp/Ljjz/i4uLCjBkzqFu3rr3LEpFy8scTwo6OjsTExBATE2O/gkz4Y81OTk7XPccydOhQhg4dWrZF/UWVqktq48aN5ObmsnTpUsaMGUNcXJy9SxIRuWlUqsDYu3evbSTR1q1bl/hjKhGRsmTkV6xflF9PadVrMQzDKJU1lYNJkybx0EMP2fr87rvvPjZu3IiTU/E9a/v27cPV1bU8SxQRqfRycnKKvctkpTqH4e7uTlZWlm3aarWWGBZAsRssIiJ/TaXqkgoJCSE5ORm4cvTQqFEjO1ckInLzqFRdUlevkjp06BCGYTBz5kwaNGhg77JERG4KlSowRETEfipVl5SIiNiPAkNERExRYIiIiCmV6rLaqiItLY3Zs2dz/vx58vLyaNKkCWPGjKFbt24kJyfj6OjIt99+y8CBA0lKSqJFixbk5OTw4IMPsm3bNoKDg3nzzTd58MEHAUhOTmbdunXExcURERFBdna27Z7VAE8++SRBQUH06tWLZs2aYRgGubm59OrVi/DwcHu9DFXG7t27GT16NEFBQQBkZWXh5+fHiy++SJ8+fWjWrFmh9gsWLOCtt95i27ZtLFmyxHZpeP/+/YmPj2fRokUcPHiQM2fOcPnyZfz9/alZsyZvvPFGuW9bWSju73/s2LG4uLhw//33m9oHWrZsyZ133llova+++iopKSnMnz+fNWvW4O7uDsCLL77IgAEDuOOOO2z7wB9dfT8+++wzateuTUFBAdWqVWPs2LE0bdq0UNuoqCgOHjyIl5cXubm5NGzYkJiYGJYsWcK2bdt4//33bW1HjRpFu3btGDhwYKF1rF+/nkWLFuHg4EB+fj5hYWG2AQiL23//9a9/ER0dbXveq2bNmsWKFSuoVasWAwcO5MKFC8yaNYvjx49TUFBAnTp1mD59Oh4eHnTu3Jk6derg4PB/xwiRkZE0b978ht47BUY5u3z5Ms899xwzZsygVatWwJW7cI0dO5YmTZqQmppK8+bN2bZtG926dWPbtm20aNGCb7/9lrvvvhsHBweqV69OXFwcISEhtruN/dGsWbOKXD124sQJgoKCWLhwIXDlXgIjR47E19eXzp07l/2GV3H/+Mc/mDt3rm16zJgxbN68udBr/mcnT57knXfeYeTIkYXmX73F6MqVKzly5Ahjx44tu8LLWUl//2PGjOGdd94xvQ/UqFGjxNc1OzubmTNnMnPmzCKPXev9GDp0qO3D/aeffmLkyJGsXr26yI9/x40bR6dOnYAr7/OmTZsIDw9n8+bNJCUl0a9fP9auXUteXl6RsNi+fTtLliwhISEBDw8PLl++zAsvvICrqysPP/wwUPz+++fnLc5LL73EgAED6NKlC3AlCKOjo21/lx988MHf/iGzuqTK2datW7n77rttOwvAo48+yrlz52jfvj3ffPMNALt27WLMmDG23518/fXXtmFR3NzceOKJJ5g6depfrsPZ2ZkhQ4awbt26v74xUqzc3Fx+/fVXPD09r9lu+PDhfPrpp/zwww/lVJn9XevvPy0tzfQ+cC29e/fmyJEjbNmy5S/X2aBBA5o1a8bevXtLbFNQUEBWVha+vr5YLBZiY2N5++23OXz4MAkJCcUG1sKFCxk7diweHh4AVKtWjcjISBYvXvyXa4UrXz5+++03W1jAlaOV6dOn/631/pmOMMpZWlpaoXv8XuXn54eTkxN79uzhf/7nf6hevTr+/v4YhkFGRgZ79uwp9G1l0KBBbNq0iU8//ZQaNWoUWldkZGShQ9p//vOfxdZSq1Ytzp07V0pbdnPbtWsXERERnD17FgcHB/r3788999xDbGwsERERtnbNmjWzHUHccsstzJgxg6ioKJYvX26v0svVtf7+09PTadeuHW+++eZ194ELFy4Uel1r165tG9rc0dGRuLg4nnrqqSKjPRw+fLjE9+PPbr311mL3jzlz5vDee+/x66+/4uHhQWBgIAA+Pj688MILhIWFER8fX+zRf3Hb7+/vX+imSn/cf3v16kW/fv0KPS9Au3btGDFihG2ZX3/9tciNmBwdHW3BBDBs2DBbl5SDgwMffvhhsdt9LQqMcnb77bfb7uH7R8eOHWP06NEkJSXx1Vdf2b5JdejQgd27d5Obm8ttt91ma2+xWJg5cyaDBw8u9IcDxR/S/v7770We8+TJk/j4+JTGZt30rnZJnTt3jmHDhtl23mt1gQCEhobSrl27EkO9qrnW37+vry9+fn4cO3bsuvvAtbqkAOrVq8eQIUOYNm1aoVuiXu/9+KP09HQeeuihIvP/2DX0z3/+k7i4OF555RXgytHNnDlzSrzHxe23387JkycLfck7duwYderUsU3/lS4pX19ffvnll0Lz8vLy+Pzzz+nZsyegLqlK6YEHHmDHjh2FdpqkpCS8vb0JCAigcePGJCUl2f4wOnXqRGJiIm3atCmyLh8fH0aNGvWXbhqTm5tLYmIiPXr0+OsbI0XUrFmTOXPmMHnyZM6cOWNqmRdffJHk5GSOHz9extXZ37X+/v39/bFYLDe0D1xLeHg458+fZ9euXTdc56FDhzh8+PB1x6OrU6cOeXl5ptcbERHB7NmzyczMBK5cIDF79mwGDx58wzX+0e23307NmjXZuHGjbV5iYmKh6dKgI4xy5ubmZuvfPH/+PAUFBTRu3Jj4+HgA2rdvz7x582xX3LRs2ZIjR47w4osvFru+3r17s2HDhkLz/twl9fDDD9OpUyfb4bjFYiE/P5+ePXvSrl27MtrSm1dQUBARERH8+9//LtIFAhTp23Z1dWXmzJkMGDCgPMu0i+v9/YO5feDPXVJw5aTvH109Cr/6DRuKdknB/70fCxYsYN26dTg4OODk5MQbb7xR7OCmV7uGHBwcsFqtxZ6rKEnnzp3JzMxk+PDhWCwWrFYrffv2pXv37qbXUZLZs2czffp0PvjgA/Ly8ggICGDGjBm2x//YJQUwZMiQQuc8zNDQICIiYoq6pERExBQFhoiImKLAEBERUxQYIiJiigJDRERMUWCIlIHk5GSWLl1aZH7//v05ceKEHSoS+fv0OwyRMnCtQeJEKisFhsh1rFy5khUrVmC1WomIiODDDz/EwcGBu+66i7Fjx7J3715mzZqFk5MTnp6evPrqq3z55Ze2kWbnzp3LV199hY+Pj21sokuXLjFp0iTb9OTJk2ncuDEPPfQQISEhHD16lFtvvZV58+aRl5fHhAkTSE9PJy8vjylTptC8eXNiYmI4fvw4VquV0aNH07ZtW3u+THITUGCImODp6UlsbCyDBg1ixYoVVK9enXHjxpGSksL27dvp0qULTz75JJs3b+bixYu25Q4dOsSePXtYvnw5v//+u21sooSEBP7xj38waNAgjh07xoQJE/j4449JS0vjww8/pE6dOgwYMIDvv/+effv2cccddzB37lwOHTrEjh07SE1NpWbNmsycOZNz584RHh7O2rVr7fXyyE1CgSFiQmBgID///DMZGRk8/fTTwJVxgNLS0nj22WdJSEjg8ccf5/bbb6dly5a25Q4fPkzz5s1xcHDA3d2dRo0aAVeCZNeuXaxfvx7AFjI1a9a0DURXp04dcnJyOHLkiK2Lq1GjRjRq1IipU6eyd+9e25hM+fn5nDt3jpo1a5bPCyI3JQWGiAkODg74+flRp04dPvjgA5ydnVm5ciXBwcF8+umnPProo0RGRvLOO++wbNkyfH19gStBk5iYiNVq5fLlyxw+fBiA+vXr06tXL3r27MnZs2dJSkoCKDSy6lUNGjTg+++/58EHHyQtLY3XX3+dVq1a4ePjw7PPPsvly5d5++23iwxzL1LaFBgiJnl7ezN06FAiIiIoKCjgjjvu4OGHHyY3N5eoqChuueUWnJ2dmT59Onv27AEgODiYbt260bdvX2rXrs2tt94KwLPPPsukSZNYtmwZmZmZPP/88yU+74ABA5g4cSLh4eEUFBQwceJEGjduzOTJkwkPDyczM5NBgwYVGlhOpCxo8EERETFFX0lERMQUBYaIiJiiwBAREVMUGCIiYooCQ0RETFFgiIiIKQoMEREx5f8BMWBTKGLv8U0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V15', hue='V13', data=tvs)\n",
    "fig.set(xlabel='residence', ylabel='count')\n",
    "\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.07, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V10', hue='V13', data=tvs,palette='YlOrRd')\n",
    "fig.set(xlabel='vehicle', ylabel='count')\n",
    "\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()-.04, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V14', hue='V13', data=tvs,palette='BuGn')\n",
    "fig.set(xlabel='employmnt', ylabel='count')\n",
    "\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.03, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=11, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "fig=sns.countplot(x='V31', hue='V13', data=tvs,palette='GnBu_d')\n",
    "fig.set(xlabel='Tier', ylabel='count')\n",
    "for i in fig.patches:\n",
    "    # get_x pulls left or right; get_height pushes up or down\n",
    "    fig.text(i.get_x()+.04, i.get_height()+1000, str(round((i.get_height()), 2)), fontsize=12, color='black',\n",
    "                rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC       28949\n",
       "MO       24649\n",
       "MC       23168\n",
       "TL        8281\n",
       "RETOP        1\n",
       "Name: V10, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V10'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    116136\n",
       "1      3392\n",
       "Name: V2, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SELF         59290\n",
       "SAL          23360\n",
       "HOUSEWIFE     1670\n",
       "STUDENT        449\n",
       "PENS           279\n",
       "Name: V14, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V14'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIER 4    41063\n",
       "TIER 3    33178\n",
       "TIER 1    25168\n",
       "TIER 2    20119\n",
       "Name: V31, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V31'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    116914\n",
       "1      2614\n",
       "Name: V32, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V32'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OWNED               73013\n",
       "RENT                11001\n",
       "OWENED BY OFFICE      117\n",
       "Name: V15, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V15'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1          0\n",
       "V2          0\n",
       "V3          0\n",
       "V4      34480\n",
       "V5      34480\n",
       "V6      34480\n",
       "V7      34480\n",
       "V8      34480\n",
       "V9      34480\n",
       "V10     34480\n",
       "V11     34480\n",
       "V12     34480\n",
       "V13     34480\n",
       "V14     34480\n",
       "V15     35397\n",
       "V16     34480\n",
       "V17     34480\n",
       "V18         0\n",
       "V19         0\n",
       "V20         0\n",
       "V21     82902\n",
       "V22         0\n",
       "V23    100247\n",
       "V24    100500\n",
       "V25     15061\n",
       "V26    106097\n",
       "V27     99095\n",
       "V28         0\n",
       "V29         0\n",
       "V30         0\n",
       "V31         0\n",
       "V32         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f5ee4a1688>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\") \n",
    "sns.boxplot(x = 'V13', y = 'V25', data = tvs) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs['V25'] = tvs['V25'].fillna((tvs['V25'].median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs=tvs.drop(['V21', 'V23','V24','V26','V27'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs=tvs.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(84131, 27)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs = tvs.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs=tvs.drop(['V1','V9'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V10', 'V11', 'V12', 'V13',\n",
       "       'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V22', 'V25', 'V28',\n",
       "       'V29', 'V30', 'V31', 'V32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change DOB to age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30-05-1974\n",
       "1    01-09-1967\n",
       "2    04-05-1983\n",
       "3    10-12-1989\n",
       "4    24-09-1984\n",
       "Name: V16, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V16'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs['V16'] = pd.to_datetime(tvs['V16'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-03-07 23:12:35.200287')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = pd.to_datetime('now')\n",
    "now\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs['V16']=(now - tvs['V16']).dt.total_seconds() / (60*60*24*365.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    45.774037\n",
       "1    53.160759\n",
       "2    36.925303\n",
       "3    30.403743\n",
       "4    35.452340\n",
       "Name: V16, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V16'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  V10---Vehicle type----MC(motorcycle) as reference\n",
    "\n",
    "## V13------Gender-----Male(1), Female(0) \n",
    "\n",
    "## V14------Employment-----Housewife as reference\n",
    "\n",
    "## V15------Residence------Owned by Office as reference\n",
    "\n",
    "## V31------Tier-----------Tier 1 as reference\n",
    "                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V10', 'V11', 'V12', 'V13',\n",
       "       'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V22', 'V25', 'V28',\n",
       "       'V29', 'V30', 'V31', 'V32'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
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       "      <th>V8</th>\n",
       "      <th>V10</th>\n",
       "      <th>V11</th>\n",
       "      <th>V12</th>\n",
       "      <th>...</th>\n",
       "      <th>V18</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V22</th>\n",
       "      <th>V25</th>\n",
       "      <th>V28</th>\n",
       "      <th>V29</th>\n",
       "      <th>V30</th>\n",
       "      <th>V31</th>\n",
       "      <th>V32</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2432.0</td>\n",
       "      <td>46500.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.75</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1495.0</td>\n",
       "      <td>28168.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>MO</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.65</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28168.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1707.0</td>\n",
       "      <td>38900.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.65</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38900.0</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2128.0</td>\n",
       "      <td>42900.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1824.0</td>\n",
       "      <td>40900.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>MC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40900.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TIER 1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   V2  V3    V4   V5      V6       V7    V8 V10  V11    V12  ... V18 V19 V20  \\\n",
       "0   0   0  24.0  0.0  2432.0  46500.0  24.0  SC  0.0  12.75  ...   1   1   0   \n",
       "1   0   1  24.0  1.0  1495.0  28168.0  24.0  MO  0.0  13.65  ...   1   1   0   \n",
       "2   0   0  26.0  0.0  1707.0  38900.0  30.0  SC  0.0  12.65  ...   9   6   3   \n",
       "3   0   0  24.0  0.0  2128.0  42900.0  24.0  SC  2.0   9.50  ...   1   1   0   \n",
       "4   0   0  27.0  0.0  1824.0  40900.0  30.0  MC  0.0  13.50  ...   1   1   0   \n",
       "\n",
       "   V22      V25  V28  V29  V30     V31  V32  \n",
       "0    0  46500.0    0    0    0  TIER 1    0  \n",
       "1    0  28168.0    0    0    0  TIER 1    0  \n",
       "2    0  38900.0   31   31   16  TIER 1    0  \n",
       "3    0  42900.0    0    0    0  TIER 1    0  \n",
       "4    0  40900.0    0    0    0  TIER 1    0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SC       28483\n",
       "MO       24445\n",
       "MC       23024\n",
       "TL        8178\n",
       "RETOP        1\n",
       "Name: V10, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V10'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vehc = pd.get_dummies(tvs['V10'],prefix='vehc',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   vehc_MO  vehc_RETOP  vehc_SC  vehc_TL\n",
       "0        0           0        1        0\n",
       "1        1           0        0        0\n",
       "2        0           0        1        0\n",
       "3        0           0        1        0\n",
       "4        0           0        0        0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vehc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MALE      72863\n",
       "FEMALE    11268\n",
       "Name: V13, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V13'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs['V13'].replace(to_replace=['MALE','FEMALE'], value=[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SELF         58918\n",
       "SAL          22856\n",
       "HOUSEWIFE     1640\n",
       "STUDENT        441\n",
       "PENS           276\n",
       "Name: V14, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V14'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Emp = pd.get_dummies(tvs['V14'],prefix='emp',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>emp_PENS</th>\n",
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      "text/plain": [
       "   emp_PENS  emp_SAL  emp_SELF  emp_STUDENT\n",
       "0         0        0         0            0\n",
       "1         0        0         1            0\n",
       "2         0        0         1            0\n",
       "3         0        1         0            0\n",
       "4         0        1         0            0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OWNED               73013\n",
       "RENT                11001\n",
       "OWENED BY OFFICE      117\n",
       "Name: V15, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V15'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res = pd.get_dummies(tvs['V15'],prefix='res',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   res_OWNED  res_RENT\n",
       "0          1         0\n",
       "1          0         1\n",
       "2          0         1\n",
       "3          0         1\n",
       "4          0         1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIER 4    28981\n",
       "TIER 3    23318\n",
       "TIER 1    17571\n",
       "TIER 2    14261\n",
       "Name: V31, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs['V31'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tier = pd.get_dummies(tvs['V31'],prefix='tier',drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tier_TIER 2</th>\n",
       "      <th>tier_TIER 3</th>\n",
       "      <th>tier_TIER 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tier_TIER 2  tier_TIER 3  tier_TIER 4\n",
       "0            0            0            0\n",
       "1            0            0            0\n",
       "2            0            0            0\n",
       "3            0            0            0\n",
       "4            0            0            0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tier.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs = pd.concat([tvs, Vehc,Emp,Res,Tier], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### V22 has all zero values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "tvs=tvs.drop(['V10','V14','V15','V22','V31'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V11', 'V12', 'V13', 'V16',\n",
       "       'V17', 'V18', 'V19', 'V20', 'V25', 'V28', 'V29', 'V30', 'V32',\n",
       "       'vehc_MO', 'vehc_RETOP', 'vehc_SC', 'vehc_TL', 'emp_PENS', 'emp_SAL',\n",
       "       'emp_SELF', 'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
       "       'tier_TIER 3', 'tier_TIER 4'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tvs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V11', 'V12', 'V13', 'V16',\n",
    "       'V17', 'V18', 'V19', 'V20', 'V25', 'V28', 'V29', 'V30', \n",
    "       'vehc_MO', 'vehc_RETOP', 'vehc_SC', 'vehc_TL', 'emp_PENS', 'emp_SAL',\n",
    "       'emp_SELF', 'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
    "       'tier_TIER 3', 'tier_TIER 4']\n",
    "dept_col=['V32']\n",
    "X=tvs[feature_cols]\n",
    "y=tvs[dept_col]\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V32'], dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  123428\n",
      "Number of no subscription in oversampled data 61714\n",
      "Number of subscription 61714\n",
      "Proportion of no subscription data in oversampled data is  0.5\n",
      "Proportion of subscription data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "os = SMOTE(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)\n",
    "columns = X_train.columns\n",
    "os_data_X,os_data_y=os.fit_sample(X_train, y_train)\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n",
    "os_data_y= pd.DataFrame(data=os_data_y,columns=['V32'])\n",
    "# we can Check the numbers of our data\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of no subscription in oversampled data\",len(os_data_y[os_data_y['V32']==0]))\n",
    "print(\"Number of subscription\",len(os_data_y[os_data_y['V32']==1]))\n",
    "print(\"Proportion of no subscription data in oversampled data is \",len(os_data_y[os_data_y['V32']==0])/len(os_data_X))\n",
    "print(\"Proportion of subscription data in oversampled data is \",len(os_data_y[os_data_y['V32']==1])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V11', 'V12', 'V13', 'V16',\n",
    "       'V17', 'V18', 'V19', 'V20', 'V25', 'V28', 'V29', 'V30', \n",
    "       'vehc_MO', 'vehc_RETOP', 'vehc_SC', 'vehc_TL', 'emp_PENS', 'emp_SAL',\n",
    "       'emp_SELF', 'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
    "       'tier_TIER 3', 'tier_TIER 4']\n",
    "\n",
    "\n",
    "\n",
    "X=os_data_X[feature_cols]\n",
    "y=os_data_y['V32']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods Used\n",
    "\n",
    "### Recursive Factor Exclusion , Linear Regression , Ridge , Lasso , Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE, f_regression\n",
    "from sklearn.linear_model import (LinearRegression, Ridge, Lasso)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dictionary to store our rankings\n",
    "ranks = {}\n",
    "# Create our function which stores the feature rankings to the ranks dictionary\n",
    "def ranking(ranks, names, order=1):\n",
    "    minmax = MinMaxScaler()\n",
    "    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]\n",
    "    ranks = map(lambda x: round(x,2), ranks)\n",
    "    return dict(zip(names, ranks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 32 features.\n",
      "Fitting estimator with 31 features.\n",
      "Fitting estimator with 30 features.\n",
      "Fitting estimator with 29 features.\n",
      "Fitting estimator with 28 features.\n",
      "Fitting estimator with 27 features.\n",
      "Fitting estimator with 26 features.\n",
      "Fitting estimator with 25 features.\n",
      "Fitting estimator with 24 features.\n",
      "Fitting estimator with 23 features.\n",
      "Fitting estimator with 22 features.\n",
      "Fitting estimator with 21 features.\n",
      "Fitting estimator with 20 features.\n",
      "Fitting estimator with 19 features.\n",
      "Fitting estimator with 18 features.\n",
      "Fitting estimator with 17 features.\n",
      "Fitting estimator with 16 features.\n",
      "Fitting estimator with 15 features.\n",
      "Fitting estimator with 14 features.\n",
      "Fitting estimator with 13 features.\n",
      "Fitting estimator with 12 features.\n",
      "Fitting estimator with 11 features.\n",
      "Fitting estimator with 10 features.\n",
      "Fitting estimator with 9 features.\n",
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Fitting estimator with 6 features.\n",
      "Fitting estimator with 5 features.\n",
      "Fitting estimator with 4 features.\n",
      "Fitting estimator with 3 features.\n",
      "Fitting estimator with 2 features.\n"
     ]
    }
   ],
   "source": [
    "# Construct our Linear Regression model\n",
    "lr = LinearRegression(normalize=True)\n",
    "lr.fit(X,y)\n",
    "#stop the search when only the last feature is left\n",
    "rfe = RFE(lr, n_features_to_select=1, verbose =3 )\n",
    "rfe.fit(X,y)\n",
    "ranks[\"RFE\"] = ranking(list(map(float, rfe.ranking_)), feature_cols, order=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Linear Regression\n",
    "lr = LinearRegression(normalize=True)\n",
    "lr.fit(X,y)\n",
    "ranks[\"LinReg\"] = ranking(np.abs(lr.coef_), feature_cols)\n",
    "\n",
    "# Using Ridge \n",
    "ridge = Ridge(alpha = 7)\n",
    "ridge.fit(X,y)\n",
    "ranks['Ridge'] = ranking(np.abs(ridge.coef_), feature_cols)\n",
    "\n",
    "# Using Lasso\n",
    "lasso = Lasso(alpha=.05)\n",
    "lasso.fit(X, y)\n",
    "ranks[\"Lasso\"] = ranking(np.abs(lasso.coef_), feature_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 50building tree 2 of 50building tree 3 of 50building tree 4 of 50\n",
      "\n",
      "\n",
      "\n",
      "building tree 5 of 50\n",
      "building tree 6 of 50\n",
      "building tree 7 of 50\n",
      "building tree 8 of 50\n",
      "building tree 9 of 50\n",
      "building tree 10 of 50\n",
      "building tree 11 of 50\n",
      "building tree 12 of 50\n",
      "building tree 13 of 50\n",
      "building tree 14 of 50\n",
      "building tree 15 of 50\n",
      "building tree 16 of 50\n",
      "building tree 17 of 50\n",
      "building tree 18 of 50\n",
      "building tree 19 of 50\n",
      "building tree 20 of 50\n",
      "building tree 21 of 50\n",
      "building tree 22 of 50\n",
      "building tree 23 of 50\n",
      "building tree 24 of 50\n",
      "building tree 25 of 50\n",
      "building tree 26 of 50\n",
      "building tree 27 of 50\n",
      "building tree 28 of 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   22.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 29 of 50\n",
      "building tree 30 of 50\n",
      "building tree 31 of 50\n",
      "building tree 32 of 50\n",
      "building tree 33 of 50\n",
      "building tree 34 of 50\n",
      "building tree 35 of 50\n",
      "building tree 36 of 50\n",
      "building tree 37 of 50\n",
      "building tree 38 of 50\n",
      "building tree 39 of 50\n",
      "building tree 40 of 50\n",
      "building tree 41 of 50\n",
      "building tree 42 of 50\n",
      "building tree 43 of 50\n",
      "building tree 44 of 50\n",
      "building tree 45 of 50\n",
      "building tree 46 of 50\n",
      "building tree 47 of 50\n",
      "building tree 48 of 50\n",
      "building tree 49 of 50\n",
      "building tree 50 of 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   48.7s finished\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_jobs=-1, n_estimators=50, verbose=3)\n",
    "rf.fit(X,y)\n",
    "ranks[\"RF\"] = ranking(rf.feature_importances_, feature_cols);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t                Lasso\tLinReg\tRF\tRFE\tRidge\tMean\n",
      "V2        \t       0.0\t0.55\t0.01\t0.71\t0.56\t0.37\n",
      "V3        \t       0.0\t0.22\t0.02\t0.52\t0.22\t0.2\n",
      "V4        \t       0.0\t0.01\t0.07\t0.16\t0.01\t0.05\n",
      "V5        \t       1.0\t0.22\t1.0\t0.55\t0.23\t0.6\n",
      "V6        \t       0.0\t0.0\t0.08\t0.06\t0.0\t0.03\n",
      "V7        \t       0.0\t0.0\t0.08\t0.03\t0.0\t0.02\n",
      "V8        \t       0.0\t0.01\t0.03\t0.13\t0.01\t0.04\n",
      "V11        \t       0.0\t0.03\t0.07\t0.29\t0.03\t0.08\n",
      "V12        \t       0.0\t0.01\t0.07\t0.19\t0.01\t0.06\n",
      "V13        \t       0.0\t0.06\t0.01\t0.48\t0.06\t0.12\n",
      "V16        \t       0.13\t0.07\t0.04\t0.39\t0.07\t0.14\n",
      "V17        \t       0.0\t0.06\t0.04\t0.35\t0.06\t0.1\n",
      "V18        \t       0.3\t0.0\t0.05\t0.1\t0.0\t0.09\n",
      "V19        \t       0.0\t0.02\t0.13\t0.26\t0.02\t0.09\n",
      "V20        \t       0.0\t0.03\t0.01\t0.32\t0.03\t0.08\n",
      "V25        \t       0.0\t0.0\t0.07\t0.0\t0.0\t0.01\n",
      "V28        \t       0.95\t0.08\t1.0\t0.42\t0.09\t0.51\n",
      "V29        \t       0.0\t0.02\t0.01\t0.23\t0.02\t0.06\n",
      "V30        \t       0.43\t0.16\t0.01\t0.45\t0.16\t0.24\n",
      "vehc_MO        \t       0.0\t0.31\t0.0\t0.58\t0.32\t0.24\n",
      "vehc_RETOP        \t       0.0\t0.71\t0.0\t0.68\t0.09\t0.3\n",
      "vehc_SC        \t       0.0\t0.33\t0.02\t0.61\t0.33\t0.26\n",
      "vehc_TL        \t       0.0\t0.42\t0.0\t0.65\t0.43\t0.3\n",
      "emp_PENS        \t       0.0\t0.82\t0.0\t1.0\t0.81\t0.53\n",
      "emp_SAL        \t       0.0\t0.81\t0.05\t0.94\t0.82\t0.52\n",
      "emp_SELF        \t       0.0\t0.5\t0.06\t0.9\t0.51\t0.39\n",
      "emp_STUDENT        \t       0.0\t1.0\t0.0\t0.97\t1.0\t0.59\n",
      "res_OWNED        \t       0.0\t0.36\t0.03\t0.74\t0.37\t0.3\n",
      "res_RENT        \t       0.0\t0.84\t0.04\t0.77\t0.86\t0.5\n",
      "tier_TIER 2        \t       0.0\t0.58\t0.04\t0.81\t0.59\t0.4\n",
      "tier_TIER 3        \t       0.0\t0.83\t0.09\t0.87\t0.85\t0.53\n",
      "tier_TIER 4        \t       0.0\t0.71\t0.07\t0.84\t0.73\t0.47\n"
     ]
    }
   ],
   "source": [
    "# Create empty dictionary to store the mean value calculated from all the scores\n",
    "r = {}\n",
    "for name in feature_cols:\n",
    "    r[name] = round(np.mean([ranks[method][name] \n",
    "                             for method in ranks.keys()]), 2)\n",
    " \n",
    "methods = sorted(ranks.keys())\n",
    "ranks[\"Mean\"] = r\n",
    "methods.append(\"Mean\")\n",
    " \n",
    "print(\"\\t                %s\" % \"\\t\".join(methods))\n",
    "for name in feature_cols:\n",
    "    print(\"%s        \\t       %s\" % (name, \"\\t\".join(map(str, \n",
    "                         [ranks[method][name] for method in methods]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put the mean scores into a Pandas dataframe\n",
    "meanplot = pd.DataFrame(list(r.items()), columns= ['Feature','Mean Ranking'])\n",
    "\n",
    "# Sort the dataframe\n",
    "meanplot = meanplot.sort_values('Mean Ranking', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f583e05e48>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 2325.6x1224 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the ranking of the features\n",
    "sns.factorplot(x=\"Mean Ranking\", y=\"Feature\", data = meanplot, kind=\"bar\", \n",
    "               size=17, aspect=1.9, palette='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factors Exclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging\n",
      "  warn(msg, LineSearchWarning)\n",
      "C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\optimize.py:204: UserWarning: Line Search failed\n",
      "  warnings.warn('Line Search failed')\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:426: LineSearchWarning: Rounding errors prevent the line search from converging\n",
      "  warn(msg, LineSearchWarning)\n",
      "C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\optimize.py:204: UserWarning: Line Search failed\n",
      "  warnings.warn('Line Search failed')\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:466: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\linesearch.py:314: LineSearchWarning: The line search algorithm did not converge\n",
      "  warn('The line search algorithm did not converge', LineSearchWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True False False False  True  True  True  True  True\n",
      " False  True  True False  True False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True]\n",
      "[1 1 2 1 6 7 3 1 1 1 1 1 4 1 1 8 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "tvs_vars=tvs.columns.values.tolist()\n",
    "y=['V32']\n",
    "X=[i for i in tvs_vars if i not in y]\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg = LogisticRegression(random_state=1, solver='newton-cg',\n",
    "                         multi_class='multinomial',max_iter=1200)\n",
    "rfe = RFE(logreg, 25)\n",
    "rfe = rfe.fit(os_data_X, os_data_y.values.ravel())\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V11', 'V12', 'V13', 'V16',\n",
    "       'V17', 'V18', 'V19', 'V20', 'V25', 'V28', 'V29', 'V30', \n",
    "       'vehc_MO', 'vehc_RETOP', 'vehc_SC', 'vehc_TL', 'emp_PENS', 'emp_SAL',\n",
    "       'emp_SELF', 'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
    "       'tier_TIER 3', 'tier_TIER 4']\n",
    "\n",
    "X=os_data_X[feature_cols]\n",
    "y=os_data_y['V32']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.303081\n",
      "         Iterations: 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Results: Logit\n",
      "======================================================================\n",
      "Model:                 Logit             Pseudo R-squared:  0.563     \n",
      "Dependent Variable:    V32               AIC:               74881.4467\n",
      "Date:                  2020-03-08 12:02  BIC:               75192.5959\n",
      "No. Observations:      123428            Log-Likelihood:    -37409.   \n",
      "Df Model:              31                LL-Null:           -85554.   \n",
      "Df Residuals:          123396            LLR p-value:       0.0000    \n",
      "Converged:             0.0000            Scale:             1.0000    \n",
      "No. Iterations:        150.0000                                       \n",
      "----------------------------------------------------------------------\n",
      "             Coef.    Std.Err.     z     P>|z|     [0.025     0.975]  \n",
      "----------------------------------------------------------------------\n",
      "V2           -2.8415     0.1006 -28.2488 0.0000     -3.0386    -2.6443\n",
      "V3           -1.1598     0.0213 -54.5746 0.0000     -1.2015    -1.1182\n",
      "V4           -0.0824     0.0045 -18.3459 0.0000     -0.0912    -0.0736\n",
      "V5            1.1700     0.0172  68.0650 0.0000      1.1363     1.2036\n",
      "V6            0.0023     0.0000  54.4310 0.0000      0.0022     0.0024\n",
      "V7           -0.0001     0.0000 -44.4393 0.0000     -0.0002    -0.0001\n",
      "V8            0.2538     0.0054  46.7432 0.0000      0.2432     0.2645\n",
      "V11           0.0188     0.0125   1.5033 0.1328     -0.0057     0.0433\n",
      "V12           0.0335     0.0037   9.0629 0.0000      0.0263     0.0408\n",
      "V13           0.0358     0.0293   1.2218 0.2218     -0.0217     0.0933\n",
      "V16          -0.0329     0.0141  -2.3290 0.0199     -0.0606    -0.0052\n",
      "V17           0.0347     0.0142   2.4390 0.0147      0.0068     0.0625\n",
      "V18           0.0118     0.0067   1.7486 0.0804     -0.0014     0.0250\n",
      "V19          -0.1640     0.0086 -19.0254 0.0000     -0.1809    -0.1471\n",
      "V20          -0.1470     0.0092 -16.0342 0.0000     -0.1650    -0.1291\n",
      "V25           0.0000     0.0000  21.0682 0.0000      0.0000     0.0000\n",
      "V28           0.5468     0.0108  50.7394 0.0000      0.5257     0.5680\n",
      "V29          -0.0090     0.0160  -0.5609 0.5749     -0.0404     0.0224\n",
      "V30          -0.7283     0.0209 -34.8395 0.0000     -0.7693    -0.6873\n",
      "vehc_MO      -0.5069     0.0310 -16.3544 0.0000     -0.5676    -0.4461\n",
      "vehc_RETOP  -18.7828 19719.7219  -0.0010 0.9992 -38668.7275 38631.1619\n",
      "vehc_SC      -1.2505     0.0272 -46.0489 0.0000     -1.3037    -1.1972\n",
      "vehc_TL      -0.9721     0.0452 -21.5000 0.0000     -1.0607    -0.8834\n",
      "emp_PENS     -6.8662     1.0417  -6.5913 0.0000     -8.9079    -4.8245\n",
      "emp_SAL      -3.8402     0.0439 -87.4187 0.0000     -3.9263    -3.7541\n",
      "emp_SELF     -2.4192     0.0358 -67.6629 0.0000     -2.4892    -2.3491\n",
      "emp_STUDENT  -6.1264     0.5319 -11.5169 0.0000     -7.1690    -5.0838\n",
      "res_OWNED    -1.9102     0.0521 -36.6926 0.0000     -2.0122    -1.8082\n",
      "res_RENT     -4.5271     0.0729 -62.1001 0.0000     -4.6700    -4.3842\n",
      "tier_TIER 2  -1.8764     0.0275 -68.2190 0.0000     -1.9303    -1.8225\n",
      "tier_TIER 3  -2.9043     0.0303 -95.6992 0.0000     -2.9638    -2.8448\n",
      "tier_TIER 4  -2.2217     0.0240 -92.6125 0.0000     -2.2687    -2.1746\n",
      "======================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit(maxiter=150)\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factors Excluded ----   V6 , V7 , V8 , V25 , Vehc_Retop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=['V2', 'V3','V4', 'V5',  'V11','V12', 'V13', 'V16',\n",
    "       'V17', 'V19', 'V20', 'V28', 'V29', 'V30', \n",
    "       'vehc_MO', 'vehc_SC', 'vehc_TL', 'emp_PENS', 'emp_SAL',\n",
    "       'emp_SELF', 'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
    "       'tier_TIER 3', 'tier_TIER 4']\n",
    "\n",
    "X=os_data_X[feature_cols]\n",
    "y=os_data_y['V32']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.325518\n",
      "         Iterations 10\n",
      "                         Results: Logit\n",
      "=================================================================\n",
      "Model:              Logit            Pseudo R-squared: 0.530     \n",
      "Dependent Variable: V32              AIC:              80408.0785\n",
      "Date:               2020-03-08 12:08 BIC:              80660.8872\n",
      "No. Observations:   123428           Log-Likelihood:   -40178.   \n",
      "Df Model:           25               LL-Null:          -85554.   \n",
      "Df Residuals:       123402           LLR p-value:      0.0000    \n",
      "Converged:          1.0000           Scale:            1.0000    \n",
      "No. Iterations:     10.0000                                      \n",
      "------------------------------------------------------------------\n",
      "              Coef.   Std.Err.     z      P>|z|    [0.025   0.975]\n",
      "------------------------------------------------------------------\n",
      "V2           -2.8804    0.1006  -28.6405  0.0000  -3.0775  -2.6833\n",
      "V3           -1.1384    0.0206  -55.3228  0.0000  -1.1787  -1.0981\n",
      "V4           -0.0188    0.0026   -7.1832  0.0000  -0.0240  -0.0137\n",
      "V5            1.1155    0.0165   67.4971  0.0000   1.0831   1.1479\n",
      "V11           0.0991    0.0110    9.0014  0.0000   0.0775   0.1207\n",
      "V12           0.1170    0.0034   34.4667  0.0000   0.1104   0.1237\n",
      "V13           0.3847    0.0273   14.0863  0.0000   0.3312   0.4383\n",
      "V16           0.2728    0.0123   22.0997  0.0000   0.2486   0.2970\n",
      "V17          -0.2466    0.0125  -19.6524  0.0000  -0.2712  -0.2220\n",
      "V19          -0.1281    0.0038  -33.4497  0.0000  -0.1356  -0.1206\n",
      "V20          -0.0857    0.0049  -17.3567  0.0000  -0.0954  -0.0760\n",
      "V28           0.5908    0.0105   56.4873  0.0000   0.5703   0.6113\n",
      "V29          -0.0737    0.0155   -4.7547  0.0000  -0.1041  -0.0433\n",
      "V30          -0.7202    0.0203  -35.4010  0.0000  -0.7601  -0.6803\n",
      "vehc_MO      -0.7888    0.0241  -32.6623  0.0000  -0.8361  -0.7414\n",
      "vehc_SC      -0.9177    0.0250  -36.6490  0.0000  -0.9668  -0.8686\n",
      "vehc_TL      -0.9489    0.0388  -24.4327  0.0000  -1.0250  -0.8728\n",
      "emp_PENS     -7.0297    1.0707   -6.5654  0.0000  -9.1282  -4.9311\n",
      "emp_SAL      -3.2582    0.0388  -84.0232  0.0000  -3.3342  -3.1822\n",
      "emp_SELF     -1.9372    0.0306  -63.3383  0.0000  -1.9972  -1.8773\n",
      "emp_STUDENT  -5.4663    0.5364  -10.1902  0.0000  -6.5177  -4.4149\n",
      "res_OWNED    -0.7577    0.0364  -20.7996  0.0000  -0.8291  -0.6863\n",
      "res_RENT     -3.2910    0.0608  -54.0996  0.0000  -3.4103  -3.1718\n",
      "tier_TIER 2  -1.7247    0.0264  -65.2554  0.0000  -1.7765  -1.6729\n",
      "tier_TIER 3  -2.8818    0.0293  -98.2100  0.0000  -2.9393  -2.8243\n",
      "tier_TIER 4  -2.2584    0.0235  -96.1268  0.0000  -2.3044  -2.2123\n",
      "=================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit(maxiter=150)\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(random_state=1, solver='lbfgs',\n",
    "                         multi_class='multinomial',max_iter=1200000)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(x_train,y_train)\n",
    "\n",
    "#\n",
    "y_pred=logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.18051596 -0.49759611 -0.0633235   0.69751432 -0.043438    0.06920571\n",
      "  -0.04352959 -1.49512947  1.43269435 -0.29565209 -0.16550892  1.36596595\n",
      "   0.02978058 -0.77654689 -0.17286496 -0.31678318 -0.12519526 -0.23733948\n",
      "  -0.79057954 -0.67804657 -0.17952563 -0.82692823 -0.92199002 -0.33688989\n",
      "  -0.54706372 -0.47540525]]\n",
      "[0.25713944]\n"
     ]
    }
   ],
   "source": [
    "print(logreg.coef_)\n",
    "print(logreg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13843,  1648],\n",
       "       [ 1967, 13399]], dtype=int64)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots()\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.89      0.88     15491\n",
      "           1       0.89      0.87      0.88     15366\n",
      "\n",
      "    accuracy                           0.88     30857\n",
      "   macro avg       0.88      0.88      0.88     30857\n",
      "weighted avg       0.88      0.88      0.88     30857\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.88\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(x_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(x_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8828466798457401\n",
      "Precision: 0.890476506944906\n",
      "Recall: 0.8719901080307172\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:\",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = logreg.predict_proba(x_test)[::,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under curve :  0.950844222690787\n"
     ]
    }
   ],
   "source": [
    "print(\"Area under curve : \", auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Neighbour Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best K is : 4 | Cross validation Accuracy : 0.9500815903552275\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "bestScore=0.0\n",
    "accList=[]\n",
    "\n",
    "for k in range(3,12):\n",
    "    \n",
    "    clf_knn = KNeighborsClassifier(n_neighbors=k,algorithm='auto')\n",
    "    \n",
    "    # using 10 fold cross validation for scoring the classifier's accuracy\n",
    "    scores = cross_val_score(clf_knn, x_train, y_train, cv=10)\n",
    "    score=scores.mean()\n",
    "    accList.append(score)\n",
    "    \n",
    "    if score > bestScore:\n",
    "        bestScore=score\n",
    "        best_clf=clf_knn\n",
    "        bestK=k\n",
    "        \n",
    "print(\"Best K is :\",bestK,\"| Cross validation Accuracy :\",bestScore)\n",
    "clf_knn=best_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(3,12),accList)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('CV Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_knn.fit(x_train,y_train)\n",
    "y_pred=best_clf.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['KNN-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['KNN-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN-jaccard': 0.9466223928608488, 'KNN-f1-score': 0.9722115359651051}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf_tree = tree.DecisionTreeClassifier()\n",
    "clf_tree = clf_tree.fit(x_train, y_train)\n",
    "\n",
    "y_pred=clf_tree.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['Tree-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['Tree-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN-jaccard': 0.9466223928608488,\n",
       " 'KNN-f1-score': 0.9722115359651051,\n",
       " 'Tree-jaccard': 1.0,\n",
       " 'Tree-f1-score': 1.0}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  *******   Decision tree graph (Use if needed, heavy lag)  ***************"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install graphviz\n",
    "!pip install pydotplus\n",
    "import graphviz \n",
    "import pydotplus\n",
    "\n",
    "dot_data = tree.export_graphviz(clf_tree, out_file=None, \n",
    "                     feature_names=['Gender', 'Customer Type', 'Age', 'Type of Travel', 'Flight Distance',\n",
    "       'Seat comfort', 'Departure/Arrival time convenient', 'Food and drink',\n",
    "       'Gate location', 'Inflight wifi service', 'Inflight entertainment',\n",
    "       'Online support', 'Ease of Online booking', 'On-board service',\n",
    "       'Leg room service', 'Baggage handling', 'Checkin service',\n",
    "       'Cleanliness', 'Online boarding', 'Departure Delay in Minutes',\n",
    "       'Arrival Delay in Minutes', 'class_Eco', 'class_Eco Plus'],  \n",
    "                     class_names='satisfaction',  \n",
    "                     filled=True, rounded=True,  \n",
    "                     special_characters=True) \n",
    "\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.set_size('\"16,16!\"')\n",
    "gvz_graph = graphviz.Source(graph.to_string())\n",
    "\n",
    "gvz_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "clf_svm = svm.LinearSVC(random_state=7,max_iter=120000)\n",
    "clf_svm.fit(x_train, y_train)  \n",
    "\n",
    "y_pred=clf_svm.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['SVM-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['SVM-f1-score']=f1_score(y_train, y_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LogReg 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_log = LogisticRegression(random_state=1, solver='newton-cg',\n",
    "                         multi_class='multinomial')\n",
    "clf_log.fit(x_train, y_train)\n",
    "\n",
    "y_pred=clf_log.predict(x_train)\n",
    "y_proba=clf_log.predict_proba(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainScores['LogReg-jaccard']=jaccard_score(y_train, y_pred)\n",
    "trainScores['LogReg-f1-score']=f1_score(y_train, y_pred, average='weighted')  \n",
    "trainScores['LogReg-logLoss']=log_loss(y_train, y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN-jaccard': 0.9466223928608488,\n",
       " 'KNN-f1-score': 0.9722115359651051,\n",
       " 'Tree-jaccard': 1.0,\n",
       " 'Tree-f1-score': 1.0,\n",
       " 'SVM-jaccard': 0.7864332947432451,\n",
       " 'SVM-f1-score': 0.882390673953295,\n",
       " 'LogReg-jaccard': 0.7917745351847494,\n",
       " 'LogReg-f1-score': 0.8852879629188553,\n",
       " 'LogReg-logLoss': 0.28077233146301517}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "testScores={}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_pred=clf_knn.predict(x_test)\n",
    "testScores['KNN-jaccard']=jaccard_score(y_test, knn_pred)\n",
    "testScores['KNN-f1-score']=f1_score(y_test, knn_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_pred=clf_tree.predict(x_test)\n",
    "testScores['Tree-jaccard']=jaccard_score(y_test, tree_pred)\n",
    "testScores['Tree-f1-score']=f1_score(y_test, tree_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pred=clf_svm.predict(x_test)\n",
    "testScores['SVM-jaccard']=jaccard_score(y_test, svm_pred)\n",
    "testScores['SVM-f1-score']=f1_score(y_test, svm_pred, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogReg Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_pred=clf_log.predict(x_test)\n",
    "proba=clf_log.predict_proba(x_test)\n",
    "testScores['LogReg-jaccard']=jaccard_score(y_test, log_pred)\n",
    "testScores['LogReg-f1-score']=f1_score(y_test, log_pred, average='weighted')  \n",
    "testScores['LogReg-logLoss']=log_loss(y_test, proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'KNN-jaccard': 0.9089089696161077,\n",
       " 'KNN-f1-score': 0.9513002956680857,\n",
       " 'Tree-jaccard': 0.9364205256570713,\n",
       " 'Tree-f1-score': 0.9670732838287489,\n",
       " 'SVM-jaccard': 0.7867217468279728,\n",
       " 'SVM-f1-score': 0.8828464870716576,\n",
       " 'LogReg-jaccard': 0.792161029455168,\n",
       " 'LogReg-f1-score': 0.8858709319099023,\n",
       " 'LogReg-logLoss': 0.2832133118614762}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Scores Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>KNN</td>\n",
       "      <td>0.908909</td>\n",
       "      <td>0.951300</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.936421</td>\n",
       "      <td>0.967073</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>SVM</td>\n",
       "      <td>0.786722</td>\n",
       "      <td>0.882846</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.792161</td>\n",
       "      <td>0.885871</td>\n",
       "      <td>0.283213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Algorithm   Jaccard  F1-score   LogLoss\n",
       "0                  KNN  0.908909  0.951300        NA\n",
       "1        Decision Tree  0.936421  0.967073        NA\n",
       "2                  SVM  0.786722  0.882846        NA\n",
       "3  Logistic Regression  0.792161  0.885871  0.283213"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jaccard = [0.9089089696161077,0.9364205256570713,0.7867217468279728,0.792161029455168]\n",
    "F1_score = [0.9513002956680857,0.9670732838287489,0.8828464870716576,0.8858709319099023]\n",
    "LogLoss = ['NA','NA','NA',0.2832133118614762]\n",
    "\n",
    "    \n",
    "df = {'Algorithm': ['KNN', 'Decision Tree', 'SVM', 'Logistic Regression'], \\\n",
    "     'Jaccard': Jaccard, 'F1-score': F1_score, 'LogLoss': LogLoss}\n",
    "\n",
    "Report = pd.DataFrame(data=df, columns=['Algorithm', 'Jaccard', 'F1-score', 'LogLoss'], index=None)\n",
    "Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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