TVS _Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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\\TVS.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
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       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <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",
       "      <td>0</td>\n",
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       "      <td>24.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>SC</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46500.0</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>1</td>\n",
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       "      <td>1495.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <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",
       "      <td>2</td>\n",
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       "      <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>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",
       "      <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>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": {
      "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='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 0x27b80a2a648>"
      ]
     },
     "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('2020-03-08 15:36:38.661055')"
      ]
     },
     "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    45.775908\n",
       "1    53.162630\n",
       "2    36.927174\n",
       "3    30.405614\n",
       "4    35.454211\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",
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       "      <th>V5</th>\n",
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       "      <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",
       "    </tr>\n",
       "  </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",
       "      <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",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <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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       "      <td>0</td>\n",
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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": 54,
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27b84008e08>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "str_list = [] # empty list to contain columns with strings (words)\n",
    "for colname, colvalue in tvs.iteritems():\n",
    "    if type(colvalue[1]) == str:\n",
    "         str_list.append(colname)\n",
    "# Get to the numeric columns by inversion            \n",
    "num_list = tvs.columns.difference(str_list) \n",
    "# Create Dataframe containing only numerical features\n",
    "house_num = tvs[num_list]\n",
    "f, ax = plt.subplots(figsize=(20, 16))\n",
    "plt.title('Pearson Correlation of features')\n",
    "# Draw the heatmap using seaborn\n",
    "#sns.heatmap(house_num.astype(float).corr(),linewidths=0.25,vmax=1.0, square=True, cmap=\"PuBuGn\", linecolor='k', annot=True)\n",
    "sns.heatmap(house_num.astype(float).corr(),linewidths=0.30,vmax=1.1, square=True, cmap=\"cubehelix\", linecolor='k', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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=tvs[feature_cols]\n",
    "y=tvs['V32']\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "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": "markdown",
   "metadata": {},
   "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": 57,
   "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": 58,
   "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": 59,
   "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": 60,
   "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": 61,
   "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 50\n",
      "\n",
      "\n",
      "building tree 4 of 50\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:   11.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 28 of 50\n",
      "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:   23.3s 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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t                Lasso\tLinReg\tRF\tRFE\tRidge\tMean\n",
      "V2        \t       0.0\t0.04\t0.04\t0.29\t0.1\t0.09\n",
      "V3        \t       0.0\t0.17\t0.18\t0.94\t0.41\t0.34\n",
      "V4        \t       0.0\t0.02\t0.43\t0.23\t0.06\t0.15\n",
      "V5        \t       0.0\t0.11\t0.19\t0.71\t0.27\t0.26\n",
      "V6        \t       0.0\t0.0\t0.88\t0.06\t0.0\t0.19\n",
      "V7        \t       0.0\t0.0\t0.55\t0.03\t0.0\t0.12\n",
      "V8        \t       0.0\t0.02\t0.14\t0.26\t0.06\t0.1\n",
      "V11        \t       0.0\t0.07\t0.16\t0.35\t0.16\t0.15\n",
      "V12        \t       0.0\t0.0\t0.65\t0.13\t0.01\t0.16\n",
      "V13        \t       0.0\t0.06\t0.05\t0.61\t0.15\t0.17\n",
      "V16        \t       0.04\t0.08\t1.0\t0.58\t0.19\t0.38\n",
      "V17        \t       0.0\t0.07\t0.41\t0.55\t0.16\t0.24\n",
      "V18        \t       0.0\t0.04\t0.41\t0.42\t0.1\t0.19\n",
      "V19        \t       0.0\t0.01\t0.13\t0.16\t0.02\t0.06\n",
      "V20        \t       0.0\t0.02\t0.13\t0.32\t0.04\t0.1\n",
      "V25        \t       0.0\t0.0\t0.47\t0.0\t0.0\t0.09\n",
      "V28        \t       1.0\t0.32\t0.46\t0.9\t0.75\t0.69\n",
      "V29        \t       0.0\t0.01\t0.22\t0.19\t0.03\t0.09\n",
      "V30        \t       0.0\t0.31\t0.12\t0.87\t0.74\t0.41\n",
      "vehc_MO        \t       0.0\t0.11\t0.03\t0.81\t0.27\t0.24\n",
      "vehc_RETOP        \t       0.0\t1.0\t0.0\t1.0\t0.3\t0.46\n",
      "vehc_SC        \t       0.0\t0.06\t0.07\t0.39\t0.14\t0.13\n",
      "vehc_TL        \t       0.0\t0.12\t0.0\t0.52\t0.29\t0.19\n",
      "emp_PENS        \t       0.0\t0.12\t0.0\t0.45\t0.28\t0.17\n",
      "emp_SAL        \t       0.0\t0.08\t0.06\t0.48\t0.19\t0.16\n",
      "emp_SELF        \t       0.0\t0.23\t0.06\t0.84\t0.53\t0.33\n",
      "emp_STUDENT        \t       0.0\t0.26\t0.0\t0.65\t0.6\t0.3\n",
      "res_OWNED        \t       0.0\t0.17\t0.04\t0.74\t0.36\t0.26\n",
      "res_RENT        \t       0.0\t0.22\t0.04\t0.77\t0.47\t0.3\n",
      "tier_TIER 2        \t       0.0\t0.42\t0.1\t0.97\t1.0\t0.5\n",
      "tier_TIER 3        \t       0.0\t0.09\t0.07\t0.68\t0.22\t0.21\n",
      "tier_TIER 4        \t       0.0\t0.0\t0.07\t0.1\t0.01\t0.04\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": 63,
   "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x27b89e36948>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1094.4x576 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=8, 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": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=[ 'V3', 'V4', 'V5', 'V6', 'V7', 'V11', 'V12', 'V13', 'V16',\n",
    "       'V17', 'V18', 'V28', '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']\n",
    "\n",
    "X=tvs[feature_cols]\n",
    "y=tvs['V32']\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: inf\n",
      "         Iterations: 150\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "Singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-4e86a7cdd248>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLogit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlogit_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m150\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m   1900\u001b[0m         bnryfit = super(Logit, self).fit(start_params=start_params,\n\u001b[0;32m   1901\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1902\u001b[1;33m                 disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m   1903\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1904\u001b[0m         \u001b[0mdiscretefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLogitResults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbnryfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, **kwargs)\u001b[0m\n\u001b[0;32m    215\u001b[0m         mlefit = super(DiscreteModel, self).fit(start_params=start_params,\n\u001b[0;32m    216\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m                 disp=disp, callback=callback, **kwargs)\n\u001b[0m\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmlefit\u001b[0m \u001b[1;31m# up to subclasses to wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m    475\u001b[0m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov_params_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxopt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    476\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mretvals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Hessian'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    478\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mskip_hessian\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    479\u001b[0m             \u001b[0mH\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhessian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxopt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\u001b[0m in \u001b[0;36minv\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m    549\u001b[0m     \u001b[0msignature\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'D->D'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'd->d'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    550\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m     \u001b[0mainv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    552\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[1;34m(err, flag)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Singular matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLinAlgError\u001b[0m: Singular matrix"
     ]
    }
   ],
   "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": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols=[ 'V3','V4', 'V5',  'V11', 'V13', 'V16',\n",
    "       'V17', 'V19', 'V20', 'V28', 'V29', 'V30', \n",
    "       'vehc_MO', 'vehc_TL', 'emp_SAL',\n",
    "    'emp_STUDENT', 'res_OWNED', 'res_RENT', 'tier_TIER 2',\n",
    "       'tier_TIER 3']\n",
    "\n",
    "X=tvs[feature_cols]\n",
    "y=tvs['V32']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.094024\n",
      "         Iterations 9\n",
      "                         Results: Logit\n",
      "=================================================================\n",
      "Model:              Logit            Pseudo R-squared: 0.107     \n",
      "Dependent Variable: V32              AIC:              15860.6015\n",
      "Date:               2020-03-08 21:31 BIC:              16047.4041\n",
      "No. Observations:   84131            Log-Likelihood:   -7910.3   \n",
      "Df Model:           19               LL-Null:          -8857.1   \n",
      "Df Residuals:       84111            LLR p-value:      0.0000    \n",
      "Converged:          1.0000           Scale:            1.0000    \n",
      "No. Iterations:     9.0000                                       \n",
      "------------------------------------------------------------------\n",
      "              Coef.   Std.Err.     z      P>|z|    [0.025   0.975]\n",
      "------------------------------------------------------------------\n",
      "V3            0.2146    0.0363    5.9075  0.0000   0.1434   0.2859\n",
      "V4            0.0189    0.0059    3.2069  0.0013   0.0074   0.0305\n",
      "V5            0.1033    0.0279    3.7053  0.0002   0.0487   0.1580\n",
      "V11          -0.0737    0.0265   -2.7862  0.0053  -0.1256  -0.0219\n",
      "V13           0.1792    0.0771    2.3251  0.0201   0.0281   0.3302\n",
      "V16          -0.2361    0.0340   -6.9362  0.0000  -0.3029  -0.1694\n",
      "V17           0.2073    0.0343    6.0364  0.0000   0.1400   0.2746\n",
      "V19          -0.1505    0.0104  -14.5015  0.0000  -0.1708  -0.1302\n",
      "V20          -0.0763    0.0125   -6.1223  0.0000  -0.1007  -0.0519\n",
      "V28           0.3175    0.0165   19.2196  0.0000   0.2851   0.3499\n",
      "V29          -0.0503    0.0261   -1.9247  0.0543  -0.1015   0.0009\n",
      "V30          -0.3269    0.0350   -9.3487  0.0000  -0.3954  -0.2584\n",
      "vehc_MO      -0.1576    0.0602   -2.6175  0.0089  -0.2757  -0.0396\n",
      "vehc_TL      -0.3709    0.1027   -3.6115  0.0003  -0.5722  -0.1696\n",
      "emp_SAL      -0.2862    0.0605   -4.7325  0.0000  -0.4047  -0.1677\n",
      "emp_STUDENT  -1.0416    0.4179   -2.4923  0.0127  -1.8608  -0.2225\n",
      "res_OWNED    -2.3725    0.1489  -15.9327  0.0000  -2.6643  -2.0806\n",
      "res_RENT     -2.4438    0.1629  -14.9978  0.0000  -2.7631  -2.1244\n",
      "tier_TIER 2   0.5700    0.0577    9.8829  0.0000   0.4569   0.6830\n",
      "tier_TIER 3  -0.2308    0.0640   -3.6084  0.0003  -0.3562  -0.1055\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": 150,
   "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\n",
    "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\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import SVR\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.703% (0.088%)\n"
     ]
    }
   ],
   "source": [
    "num_samples = 5\n",
    "test_size = 0.25\n",
    "num_instances = len(X)\n",
    "seed = 1\n",
    "kfold = model_selection.ShuffleSplit(n_splits=10, test_size=test_size, random_state=seed)\n",
    "model = LogisticRegression(random_state=1, solver='newton-cg',\n",
    "                         multi_class='multinomial',max_iter=1200000)\n",
    "results = model_selection.cross_val_score(model, X, y, cv=kfold)\n",
    "print(\"Accuracy: %.3f%% (%.3f%%)\" % (results.mean()*100.0, results.std()*100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.09800451  0.04660662  0.04902401 -0.0281283   0.05062279 -0.76051956\n",
      "   0.6306081  -0.50259031 -0.11885827  0.66439949 -0.11810547 -0.26184501\n",
      "  -0.02122534 -0.04812501 -0.04162478 -0.03759418 -0.02812056 -0.05442269\n",
      "   0.11126421 -0.04713824]]\n",
      "[-2.11029769]\n"
     ]
    }
   ],
   "source": [
    "print(logreg.coef_)\n",
    "print(logreg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20551,    29],\n",
       "       [  449,     4]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted label')"
      ]
     },
     "execution_count": 113,
     "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": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     20580\n",
      "           1       0.12      0.01      0.02       453\n",
      "\n",
      "    accuracy                           0.98     21033\n",
      "   macro avg       0.55      0.50      0.50     21033\n",
      "weighted avg       0.96      0.98      0.97     21033\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.98\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": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9772738078257975\n",
      "Precision: 0.12121212121212122\n",
      "Recall: 0.008830022075055188\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": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01646090534979424"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(x_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(x_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under curve :  0.5037104434962254\n"
     ]
    }
   ],
   "source": [
    "print(\"Area under curve : \", logit_roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Neighbour Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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": 124,
   "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": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 125,
     "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": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "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": 129,
   "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": 130,
   "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": 131,
   "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": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.0021536252692031586,\n",
       " 'SVM-f1-score': 0.9672042015943404,\n",
       " 'LogReg-jaccard': 0.015151515151515152,\n",
       " 'LogReg-f1-score': 0.9674458283280913,\n",
       " 'LogReg-logLoss': 0.09349018036708649}"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": 134,
   "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": 143,
   "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)  \n",
    "testScores['LogReg-logLoss']=log_loss(y_test, proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'SVM-jaccard': 0.004395604395604396,\n",
       " 'SVM-f1-score': 0.967998342402801,\n",
       " 'LogReg-jaccard': 0.008298755186721992,\n",
       " 'LogReg-f1-score': 0.01646090534979424,\n",
       " 'LogReg-logLoss': 0.09543651360852345}"
      ]
     },
     "execution_count": 144,
     "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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