Modelling - Dummy Variables.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "\n",
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Gender\"]=train[\"Gender\"].astype(\"category\")\n",
    "train[\"Region_Code\"]=train.Region_Code.astype(\"int\")\n",
    "train.Vehicle_Age=train.Vehicle_Age.astype(\"category\")\n",
    "train.Vehicle_Damage=train.Vehicle_Damage.astype('category')\n",
    "train.Policy_Sales_Channel=train.Policy_Sales_Channel.astype('int')\n",
    "\n",
    "train.drop(columns=\"id\",inplace=True)\n",
    "\n",
    "train[\"Region_Code\"]=train[\"Region_Code\"].astype('category')\n",
    "train[\"Policy_Sales_Channel\"]=train[\"Policy_Sales_Channel\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Gender\"]=train[\"Gender\"].astype(\"category\")\n",
    "test[\"Region_Code\"]=test.Region_Code.astype(\"int\")\n",
    "test.Vehicle_Age=test.Vehicle_Age.astype(\"category\")\n",
    "test.Vehicle_Damage=test.Vehicle_Damage.astype('category')\n",
    "test.Policy_Sales_Channel=test.Policy_Sales_Channel.astype('int')\n",
    "\n",
    "test.drop(columns=\"id\",inplace=True)\n",
    "\n",
    "test[\"Region_Code\"]=test[\"Region_Code\"].astype('category')\n",
    "test[\"Policy_Sales_Channel\"]=test[\"Policy_Sales_Channel\"].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                  category\n",
       "Age                        int64\n",
       "Driving_License            int64\n",
       "Region_Code             category\n",
       "Previously_Insured         int64\n",
       "Vehicle_Age             category\n",
       "Vehicle_Damage          category\n",
       "Annual_Premium           float64\n",
       "Policy_Sales_Channel    category\n",
       "Vintage                    int64\n",
       "Response                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         28\n",
       "1          3\n",
       "2         28\n",
       "3         11\n",
       "4         41\n",
       "          ..\n",
       "381104    26\n",
       "381105    37\n",
       "381106    30\n",
       "381107    14\n",
       "381108    29\n",
       "Name: Region_Code, Length: 381109, dtype: category\n",
       "Categories (53, int64): [0, 1, 2, 3, ..., 49, 50, 51, 52]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Region_Code\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         11\n",
       "1         28\n",
       "2         28\n",
       "3         27\n",
       "4         28\n",
       "          ..\n",
       "127032    37\n",
       "127033    28\n",
       "127034    46\n",
       "127035    28\n",
       "127036    29\n",
       "Name: Region_Code, Length: 127037, dtype: category\n",
       "Categories (53, int64): [0, 1, 2, 3, ..., 49, 50, 51, 52]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"Region_Code\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 53 categories in both Train and Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          26\n",
       "1          26\n",
       "2          26\n",
       "3         152\n",
       "4         152\n",
       "         ... \n",
       "381104     26\n",
       "381105    152\n",
       "381106    160\n",
       "381107    124\n",
       "381108     26\n",
       "Name: Policy_Sales_Channel, Length: 381109, dtype: category\n",
       "Categories (155, int64): [1, 2, 3, 4, ..., 158, 159, 160, 163]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Policy_Sales_Channel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         152\n",
       "1           7\n",
       "2         124\n",
       "3         152\n",
       "4         152\n",
       "         ... \n",
       "127032    152\n",
       "127033    122\n",
       "127034    152\n",
       "127035     26\n",
       "127036    124\n",
       "Name: Policy_Sales_Channel, Length: 127037, dtype: category\n",
       "Categories (145, int64): [1, 2, 3, 4, ..., 158, 159, 160, 163]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"Policy_Sales_Channel\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is inconsistency in the number of categories in both Train and Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       " 41]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=list(train.Policy_Sales_Channel.unique())\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       " 58]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=list(test.Policy_Sales_Channel.unique())\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{141, 142}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(b).difference(set(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.extend([141,142])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"Policy_Sales_Channel\"]=pd.Categorical(train[\"Policy_Sales_Channel\"], categories=a, ordered=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"Policy_Sales_Channel\"]=pd.Categorical(test[\"Policy_Sales_Channel\"], categories=a, ordered=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         152\n",
       "1           7\n",
       "2         124\n",
       "3         152\n",
       "4         152\n",
       "         ... \n",
       "127032    152\n",
       "127033    122\n",
       "127034    152\n",
       "127035     26\n",
       "127036    124\n",
       "Name: Policy_Sales_Channel, Length: 127037, dtype: category\n",
       "Categories (157, int64): [26, 152, 160, 124, ..., 143, 41, 141, 142]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"Policy_Sales_Channel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26</td>\n",
       "      <td>217</td>\n",
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       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
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       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381104</th>\n",
       "      <td>Male</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>30170.0</td>\n",
       "      <td>26</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>40016.0</td>\n",
       "      <td>152</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>381106</th>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>35118.0</td>\n",
       "      <td>160</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>381107</th>\n",
       "      <td>Female</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
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       "        Gender  Age  Driving_License Region_Code  Previously_Insured  \\\n",
       "0         Male   44                1          28                   0   \n",
       "1         Male   76                1           3                   0   \n",
       "2         Male   47                1          28                   0   \n",
       "3         Male   21                1          11                   1   \n",
       "4       Female   29                1          41                   1   \n",
       "...        ...  ...              ...         ...                 ...   \n",
       "381104    Male   74                1          26                   1   \n",
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       "381106    Male   21                1          30                   1   \n",
       "381107  Female   68                1          14                   0   \n",
       "381108    Male   46                1          29                   0   \n",
       "\n",
       "       Vehicle_Age Vehicle_Damage  Annual_Premium Policy_Sales_Channel  \\\n",
       "0        > 2 Years            Yes         40454.0                   26   \n",
       "1         1-2 Year             No         33536.0                   26   \n",
       "2        > 2 Years            Yes         38294.0                   26   \n",
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       "381106    < 1 Year             No         35118.0                  160   \n",
       "381107   > 2 Years            Yes         44617.0                  124   \n",
       "381108    1-2 Year             No         41777.0                   26   \n",
       "\n",
       "        Vintage  Response  \n",
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       "2            27         1  \n",
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       "381106      161         0  \n",
       "381107       74         0  \n",
       "381108      237         0  \n",
       "\n",
       "[381109 rows x 11 columns]"
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       "        Age  Driving_License  Previously_Insured  Annual_Premium  Vintage  \\\n",
       "0        44                1                   0         40454.0      217   \n",
       "1        76                1                   0         33536.0      183   \n",
       "2        47                1                   0         38294.0       27   \n",
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       "...     ...              ...                 ...             ...      ...   \n",
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       "381107   68                1                   0         44617.0       74   \n",
       "381108   46                1                   0         41777.0      237   \n",
       "\n",
       "        Response  Gender_Male  Region_Code_1  Region_Code_2  Region_Code_3  \\\n",
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       "        ...  Policy_Sales_Channel_102  Policy_Sales_Channel_149  \\\n",
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       "      <td>33762.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40050.0</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
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       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37356.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>127032</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>56</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>127033</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
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       "      <td>28700.0</td>\n",
       "      <td>165</td>\n",
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       "    <tr>\n",
       "      <th>127034</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29802.0</td>\n",
       "      <td>74</td>\n",
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       "    <tr>\n",
       "      <th>127035</th>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>62875.0</td>\n",
       "      <td>265</td>\n",
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       "    <tr>\n",
       "      <th>127036</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27927.0</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>127037 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Driving_License  Previously_Insured  Annual_Premium  Vintage  \\\n",
       "0        25                1                   1         35786.0       53   \n",
       "1        40                1                   0         33762.0      111   \n",
       "2        47                1                   0         40050.0      199   \n",
       "3        24                1                   1         37356.0      187   \n",
       "4        27                1                   1         59097.0      297   \n",
       "...     ...              ...                 ...             ...      ...   \n",
       "127032   26                1                   1         30867.0       56   \n",
       "127033   38                1                   0         28700.0      165   \n",
       "127034   21                1                   1         29802.0       74   \n",
       "127035   71                1                   1         62875.0      265   \n",
       "127036   41                1                   1         27927.0      231   \n",
       "\n",
       "        Gender_Male  Region_Code_1  Region_Code_2  Region_Code_3  \\\n",
       "0                 1              0              0              0   \n",
       "1                 1              0              0              0   \n",
       "2                 1              0              0              0   \n",
       "3                 1              0              0              0   \n",
       "4                 0              0              0              0   \n",
       "...             ...            ...            ...            ...   \n",
       "127032            1              0              0              0   \n",
       "127033            1              0              0              0   \n",
       "127034            0              0              0              0   \n",
       "127035            1              0              0              0   \n",
       "127036            1              0              0              0   \n",
       "\n",
       "        Region_Code_4  ...  Policy_Sales_Channel_102  \\\n",
       "0                   0  ...                         0   \n",
       "1                   0  ...                         0   \n",
       "2                   0  ...                         0   \n",
       "3                   0  ...                         0   \n",
       "4                   0  ...                         0   \n",
       "...               ...  ...                       ...   \n",
       "127032              0  ...                         0   \n",
       "127033              0  ...                         0   \n",
       "127034              0  ...                         0   \n",
       "127035              0  ...                         0   \n",
       "127036              0  ...                         0   \n",
       "\n",
       "        Policy_Sales_Channel_149  Policy_Sales_Channel_43  \\\n",
       "0                              0                        0   \n",
       "1                              0                        0   \n",
       "2                              0                        0   \n",
       "3                              0                        0   \n",
       "4                              0                        0   \n",
       "...                          ...                      ...   \n",
       "127032                         0                        0   \n",
       "127033                         0                        0   \n",
       "127034                         0                        0   \n",
       "127035                         0                        0   \n",
       "127036                         0                        0   \n",
       "\n",
       "        Policy_Sales_Channel_6  Policy_Sales_Channel_50  \\\n",
       "0                            0                        0   \n",
       "1                            0                        0   \n",
       "2                            0                        0   \n",
       "3                            0                        0   \n",
       "4                            0                        0   \n",
       "...                        ...                      ...   \n",
       "127032                       0                        0   \n",
       "127033                       0                        0   \n",
       "127034                       0                        0   \n",
       "127035                       0                        0   \n",
       "127036                       0                        0   \n",
       "\n",
       "        Policy_Sales_Channel_144  Policy_Sales_Channel_143  \\\n",
       "0                              0                         0   \n",
       "1                              0                         0   \n",
       "2                              0                         0   \n",
       "3                              0                         0   \n",
       "4                              0                         0   \n",
       "...                          ...                       ...   \n",
       "127032                         0                         0   \n",
       "127033                         0                         0   \n",
       "127034                         0                         0   \n",
       "127035                         0                         0   \n",
       "127036                         0                         0   \n",
       "\n",
       "        Policy_Sales_Channel_41  Policy_Sales_Channel_141  \\\n",
       "0                             0                         0   \n",
       "1                             0                         0   \n",
       "2                             0                         0   \n",
       "3                             0                         0   \n",
       "4                             0                         0   \n",
       "...                         ...                       ...   \n",
       "127032                        0                         0   \n",
       "127033                        0                         0   \n",
       "127034                        0                         0   \n",
       "127035                        0                         0   \n",
       "127036                        0                         0   \n",
       "\n",
       "        Policy_Sales_Channel_142  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              0  \n",
       "4                              0  \n",
       "...                          ...  \n",
       "127032                         0  \n",
       "127033                         0  \n",
       "127034                         0  \n",
       "127035                         0  \n",
       "127036                         0  \n",
       "\n",
       "[127037 rows x 217 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test=pd.get_dummies(test,drop_first=True)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train.drop(columns=\"Response\")\n",
    "y_train=train[\"Response\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler=MinMaxScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled=pd.DataFrame(scaler.transform(X_train),columns=X_train.columns)\n",
    "X_test_scaled=pd.DataFrame(scaler.transform(test),columns=test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Vintage</th>\n",
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       "      <th>Policy_Sales_Channel_143</th>\n",
       "      <th>Policy_Sales_Channel_41</th>\n",
       "      <th>Policy_Sales_Channel_141</th>\n",
       "      <th>Policy_Sales_Channel_142</th>\n",
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       "<p>381109 rows × 217 columns</p>\n",
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       "             Age  Driving_License  Previously_Insured  Annual_Premium  \\\n",
       "0       0.369231              1.0                 0.0        0.070366   \n",
       "1       0.861538              1.0                 0.0        0.057496   \n",
       "2       0.415385              1.0                 0.0        0.066347   \n",
       "3       0.015385              1.0                 1.0        0.048348   \n",
       "4       0.138462              1.0                 1.0        0.046259   \n",
       "...          ...              ...                 ...             ...   \n",
       "381104  0.830769              1.0                 1.0        0.051234   \n",
       "381105  0.153846              1.0                 1.0        0.069551   \n",
       "381106  0.015385              1.0                 1.0        0.060439   \n",
       "381107  0.738462              1.0                 0.0        0.078110   \n",
       "381108  0.400000              1.0                 0.0        0.072827   \n",
       "\n",
       "         Vintage  Gender_Male  Region_Code_1  Region_Code_2  Region_Code_3  \\\n",
       "0       0.716263          1.0            0.0            0.0            0.0   \n",
       "1       0.598616          1.0            0.0            0.0            1.0   \n",
       "2       0.058824          1.0            0.0            0.0            0.0   \n",
       "3       0.667820          1.0            0.0            0.0            0.0   \n",
       "4       0.100346          0.0            0.0            0.0            0.0   \n",
       "...          ...          ...            ...            ...            ...   \n",
       "381104  0.269896          1.0            0.0            0.0            0.0   \n",
       "381105  0.418685          1.0            0.0            0.0            0.0   \n",
       "381106  0.522491          1.0            0.0            0.0            0.0   \n",
       "381107  0.221453          0.0            0.0            0.0            0.0   \n",
       "381108  0.785467          1.0            0.0            0.0            0.0   \n",
       "\n",
       "        Region_Code_4  ...  Policy_Sales_Channel_102  \\\n",
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       "\n",
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       "3                            0.0                      0.0   \n",
       "4                            0.0                      0.0   \n",
       "...                          ...                      ...   \n",
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       "\n",
       "        Policy_Sales_Channel_6  Policy_Sales_Channel_50  \\\n",
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       "...                        ...                      ...   \n",
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       "381106                     0.0                      0.0   \n",
       "381107                     0.0                      0.0   \n",
       "381108                     0.0                      0.0   \n",
       "\n",
       "        Policy_Sales_Channel_144  Policy_Sales_Channel_143  \\\n",
       "0                            0.0                       0.0   \n",
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       "3                            0.0                       0.0   \n",
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       "...                          ...                       ...   \n",
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       "\n",
       "        Policy_Sales_Channel_41  Policy_Sales_Channel_141  \\\n",
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       "3                           0.0                       0.0   \n",
       "4                           0.0                       0.0   \n",
       "...                         ...                       ...   \n",
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       "381107                      0.0                       0.0   \n",
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       "\n",
       "        Policy_Sales_Channel_142  \n",
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       "\n",
       "[381109 rows x 217 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "      <td>0.784615</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.112076</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127036</th>\n",
       "      <td>0.323077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.047061</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127037 rows × 217 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age  Driving_License  Previously_Insured  Annual_Premium  \\\n",
       "0       0.076923              1.0                 1.0        0.061682   \n",
       "1       0.307692              1.0                 0.0        0.057916   \n",
       "2       0.415385              1.0                 0.0        0.069614   \n",
       "3       0.061538              1.0                 1.0        0.064602   \n",
       "4       0.107692              1.0                 1.0        0.105048   \n",
       "...          ...              ...                 ...             ...   \n",
       "127032  0.092308              1.0                 1.0        0.052531   \n",
       "127033  0.276923              1.0                 0.0        0.048499   \n",
       "127034  0.015385              1.0                 1.0        0.050549   \n",
       "127035  0.784615              1.0                 1.0        0.112076   \n",
       "127036  0.323077              1.0                 1.0        0.047061   \n",
       "\n",
       "         Vintage  Gender_Male  Region_Code_1  Region_Code_2  Region_Code_3  \\\n",
       "0       0.148789          1.0            0.0            0.0            0.0   \n",
       "1       0.349481          1.0            0.0            0.0            0.0   \n",
       "2       0.653979          1.0            0.0            0.0            0.0   \n",
       "3       0.612457          1.0            0.0            0.0            0.0   \n",
       "4       0.993080          0.0            0.0            0.0            0.0   \n",
       "...          ...          ...            ...            ...            ...   \n",
       "127032  0.159170          1.0            0.0            0.0            0.0   \n",
       "127033  0.536332          1.0            0.0            0.0            0.0   \n",
       "127034  0.221453          0.0            0.0            0.0            0.0   \n",
       "127035  0.882353          1.0            0.0            0.0            0.0   \n",
       "127036  0.764706          1.0            0.0            0.0            0.0   \n",
       "\n",
       "        Region_Code_4  ...  Policy_Sales_Channel_102  \\\n",
       "0                 0.0  ...                       0.0   \n",
       "1                 0.0  ...                       0.0   \n",
       "2                 0.0  ...                       0.0   \n",
       "3                 0.0  ...                       0.0   \n",
       "4                 0.0  ...                       0.0   \n",
       "...               ...  ...                       ...   \n",
       "127032            0.0  ...                       0.0   \n",
       "127033            0.0  ...                       0.0   \n",
       "127034            0.0  ...                       0.0   \n",
       "127035            0.0  ...                       0.0   \n",
       "127036            0.0  ...                       0.0   \n",
       "\n",
       "        Policy_Sales_Channel_149  Policy_Sales_Channel_43  \\\n",
       "0                            0.0                      0.0   \n",
       "1                            0.0                      0.0   \n",
       "2                            0.0                      0.0   \n",
       "3                            0.0                      0.0   \n",
       "4                            0.0                      0.0   \n",
       "...                          ...                      ...   \n",
       "127032                       0.0                      0.0   \n",
       "127033                       0.0                      0.0   \n",
       "127034                       0.0                      0.0   \n",
       "127035                       0.0                      0.0   \n",
       "127036                       0.0                      0.0   \n",
       "\n",
       "        Policy_Sales_Channel_6  Policy_Sales_Channel_50  \\\n",
       "0                          0.0                      0.0   \n",
       "1                          0.0                      0.0   \n",
       "2                          0.0                      0.0   \n",
       "3                          0.0                      0.0   \n",
       "4                          0.0                      0.0   \n",
       "...                        ...                      ...   \n",
       "127032                     0.0                      0.0   \n",
       "127033                     0.0                      0.0   \n",
       "127034                     0.0                      0.0   \n",
       "127035                     0.0                      0.0   \n",
       "127036                     0.0                      0.0   \n",
       "\n",
       "        Policy_Sales_Channel_144  Policy_Sales_Channel_143  \\\n",
       "0                            0.0                       0.0   \n",
       "1                            0.0                       0.0   \n",
       "2                            0.0                       0.0   \n",
       "3                            0.0                       0.0   \n",
       "4                            0.0                       0.0   \n",
       "...                          ...                       ...   \n",
       "127032                       0.0                       0.0   \n",
       "127033                       0.0                       0.0   \n",
       "127034                       0.0                       0.0   \n",
       "127035                       0.0                       0.0   \n",
       "127036                       0.0                       0.0   \n",
       "\n",
       "        Policy_Sales_Channel_41  Policy_Sales_Channel_141  \\\n",
       "0                           0.0                       0.0   \n",
       "1                           0.0                       0.0   \n",
       "2                           0.0                       0.0   \n",
       "3                           0.0                       0.0   \n",
       "4                           0.0                       0.0   \n",
       "...                         ...                       ...   \n",
       "127032                      0.0                       0.0   \n",
       "127033                      0.0                       0.0   \n",
       "127034                      0.0                       0.0   \n",
       "127035                      0.0                       0.0   \n",
       "127036                      0.0                       0.0   \n",
       "\n",
       "        Policy_Sales_Channel_142  \n",
       "0                            0.0  \n",
       "1                            0.0  \n",
       "2                            0.0  \n",
       "3                            0.0  \n",
       "4                            0.0  \n",
       "...                          ...  \n",
       "127032                       0.0  \n",
       "127033                       0.0  \n",
       "127034                       0.0  \n",
       "127035                       0.0  \n",
       "127036                       0.0  \n",
       "\n",
       "[127037 rows x 217 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight Parameter used (X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.57666243, 0.83032471, 0.83086463, 0.82880802, 0.82900244])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores=cross_val_score(LogisticRegression(class_weight=\"balanced\",random_state=40),X_train,y_train,scoring='roc_auc',cv=5)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7791324460636777"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression(class_weight='balanced')\n",
    "logreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=logreg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred=logreg.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    77855\n",
       "1    49182\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub1_df=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_pred})\n",
    "sub1_df.to_csv(\"submission1.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight Parameter used (X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,roc_auc_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84942367, 0.85103793, 0.84897083, 0.84843694, 0.84721015])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "scores=cross_val_score(LogisticRegression(class_weight=\"balanced\",random_state=40),X_train_scaled,y_train,scoring='roc_auc',cv=5,n_jobs=-1)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8490159031680591"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=40, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression(class_weight=\"balanced\",random_state=40,max_iter=1000)\n",
    "logreg.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=logreg.predict(X_train_scaled)\n",
    "y_test_pred=logreg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=logreg.predict_proba(X_train_scaled)[:,1]\n",
    "y_test_prob=logreg.predict_proba(X_test_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8499245602239953"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.66      0.79    334399\n",
      "           1       0.28      0.94      0.43     46710\n",
      "\n",
      "    accuracy                           0.69    381109\n",
      "   macro avg       0.63      0.80      0.61    381109\n",
      "weighted avg       0.90      0.69      0.75    381109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    74524\n",
       "1    52513\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub2_df=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_pred})\n",
    "sub2_df.to_csv(\"submission2_Ridge.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99525935, 0.00474065],\n",
       "       [0.21640806, 0.78359194],\n",
       "       [0.24859255, 0.75140745],\n",
       "       ...,\n",
       "       [0.99687513, 0.00312487],\n",
       "       [0.99494136, 0.00505864],\n",
       "       [0.98831801, 0.01168199]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.predict_proba(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(logreg.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing Irrelevant Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>cols</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-1.759721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Driving_License</td>\n",
       "      <td>1.203058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Previously_Insured</td>\n",
       "      <td>-3.951189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Annual_Premium</td>\n",
       "      <td>0.404464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vintage</td>\n",
       "      <td>-0.016104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Policy_Sales_Channel_144</td>\n",
       "      <td>-0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Policy_Sales_Channel_143</td>\n",
       "      <td>-0.036415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Policy_Sales_Channel_41</td>\n",
       "      <td>-0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Policy_Sales_Channel_141</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Policy_Sales_Channel_142</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>217 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         cols    Values\n",
       "0                         Age -1.759721\n",
       "1             Driving_License  1.203058\n",
       "2          Previously_Insured -3.951189\n",
       "3              Annual_Premium  0.404464\n",
       "4                     Vintage -0.016104\n",
       "..                        ...       ...\n",
       "212  Policy_Sales_Channel_144 -0.000406\n",
       "213  Policy_Sales_Channel_143 -0.036415\n",
       "214   Policy_Sales_Channel_41 -0.000536\n",
       "215  Policy_Sales_Channel_141  0.000000\n",
       "216  Policy_Sales_Channel_142  0.000000\n",
       "\n",
       "[217 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefficient_columns=pd.DataFrame({\"cols\":X_train.columns,\"Values\":logreg.coef_[0]})\n",
    "coefficient_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Annual_Premium</td>\n",
       "      <td>0.404464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cols    Values\n",
       "3  Annual_Premium  0.404464"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefficient_columns=coefficient_columns.sort_values(by=\"Values\")\n",
    "coefficient_columns[coefficient_columns.cols==\"Annual_Premium\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Policy_Sales_Channel_148',\n",
       " 'Policy_Sales_Channel_79',\n",
       " 'Policy_Sales_Channel_114',\n",
       " 'Policy_Sales_Channel_122',\n",
       " 'Policy_Sales_Channel_156',\n",
       " 'Policy_Sales_Channel_93',\n",
       " 'Policy_Sales_Channel_147',\n",
       " 'Policy_Sales_Channel_14',\n",
       " 'Policy_Sales_Channel_40',\n",
       " 'Policy_Sales_Channel_67',\n",
       " 'Policy_Sales_Channel_56',\n",
       " 'Policy_Sales_Channel_103',\n",
       " 'Policy_Sales_Channel_32',\n",
       " 'Policy_Sales_Channel_51',\n",
       " 'Policy_Sales_Channel_110',\n",
       " 'Policy_Sales_Channel_7',\n",
       " 'Policy_Sales_Channel_137',\n",
       " 'Policy_Sales_Channel_124',\n",
       " 'Policy_Sales_Channel_70',\n",
       " 'Policy_Sales_Channel_95',\n",
       " 'Region_Code_44',\n",
       " 'Policy_Sales_Channel_38',\n",
       " 'Policy_Sales_Channel_58',\n",
       " 'Policy_Sales_Channel_71',\n",
       " 'Policy_Sales_Channel_134',\n",
       " 'Policy_Sales_Channel_104',\n",
       " 'Policy_Sales_Channel_42',\n",
       " 'Policy_Sales_Channel_63',\n",
       " 'Policy_Sales_Channel_99',\n",
       " 'Policy_Sales_Channel_108',\n",
       " 'Policy_Sales_Channel_15',\n",
       " 'Region_Code_25',\n",
       " 'Policy_Sales_Channel_154',\n",
       " 'Policy_Sales_Channel_107',\n",
       " 'Policy_Sales_Channel_33',\n",
       " 'Policy_Sales_Channel_34',\n",
       " 'Region_Code_1',\n",
       " 'Policy_Sales_Channel_50',\n",
       " 'Policy_Sales_Channel_76',\n",
       " 'Policy_Sales_Channel_136',\n",
       " 'Policy_Sales_Channel_112',\n",
       " 'Policy_Sales_Channel_66',\n",
       " 'Policy_Sales_Channel_57',\n",
       " 'Region_Code_48',\n",
       " 'Policy_Sales_Channel_20',\n",
       " 'Policy_Sales_Channel_126',\n",
       " 'Policy_Sales_Channel_82',\n",
       " 'Region_Code_22',\n",
       " 'Policy_Sales_Channel_89',\n",
       " 'Policy_Sales_Channel_91',\n",
       " 'Policy_Sales_Channel_49',\n",
       " 'Policy_Sales_Channel_149',\n",
       " 'Policy_Sales_Channel_6',\n",
       " 'Policy_Sales_Channel_83',\n",
       " 'Policy_Sales_Channel_75',\n",
       " 'Policy_Sales_Channel_115',\n",
       " 'Policy_Sales_Channel_102',\n",
       " 'Policy_Sales_Channel_74',\n",
       " 'Policy_Sales_Channel_86',\n",
       " 'Region_Code_26',\n",
       " 'Policy_Sales_Channel_130',\n",
       " 'Policy_Sales_Channel_143',\n",
       " 'Policy_Sales_Channel_53',\n",
       " 'Policy_Sales_Channel_44',\n",
       " 'Policy_Sales_Channel_97',\n",
       " 'Region_Code_42',\n",
       " 'Policy_Sales_Channel_150',\n",
       " 'Vintage',\n",
       " 'Policy_Sales_Channel_157',\n",
       " 'Policy_Sales_Channel_2',\n",
       " 'Policy_Sales_Channel_158',\n",
       " 'Policy_Sales_Channel_92',\n",
       " 'Policy_Sales_Channel_27',\n",
       " 'Policy_Sales_Channel_117',\n",
       " 'Policy_Sales_Channel_105',\n",
       " 'Policy_Sales_Channel_78',\n",
       " 'Policy_Sales_Channel_41',\n",
       " 'Policy_Sales_Channel_144',\n",
       " 'Policy_Sales_Channel_84',\n",
       " 'Policy_Sales_Channel_142',\n",
       " 'Policy_Sales_Channel_141',\n",
       " 'Region_Code_50',\n",
       " 'Policy_Sales_Channel_4',\n",
       " 'Policy_Sales_Channel_28',\n",
       " 'Policy_Sales_Channel_101',\n",
       " 'Policy_Sales_Channel_94',\n",
       " 'Policy_Sales_Channel_62',\n",
       " 'Region_Code_20',\n",
       " 'Region_Code_34',\n",
       " 'Policy_Sales_Channel_87',\n",
       " 'Gender_Male',\n",
       " 'Policy_Sales_Channel_69',\n",
       " 'Policy_Sales_Channel_25',\n",
       " 'Policy_Sales_Channel_31',\n",
       " 'Policy_Sales_Channel_145',\n",
       " 'Policy_Sales_Channel_43',\n",
       " 'Region_Code_31',\n",
       " 'Policy_Sales_Channel_123',\n",
       " 'Policy_Sales_Channel_17',\n",
       " 'Policy_Sales_Channel_100',\n",
       " 'Region_Code_9',\n",
       " 'Region_Code_43',\n",
       " 'Policy_Sales_Channel_163',\n",
       " 'Policy_Sales_Channel_106',\n",
       " 'Policy_Sales_Channel_68',\n",
       " 'Region_Code_47',\n",
       " 'Region_Code_49',\n",
       " 'Region_Code_17',\n",
       " 'Policy_Sales_Channel_80',\n",
       " 'Vehicle_Age_> 2 Years',\n",
       " 'Region_Code_2',\n",
       " 'Region_Code_15',\n",
       " 'Policy_Sales_Channel_90',\n",
       " 'Policy_Sales_Channel_155',\n",
       " 'Region_Code_8',\n",
       " 'Region_Code_27']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove=list(coefficient_columns[abs(coefficient_columns.Values)<0.3][\"cols\"])\n",
    "remove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled=X_train_scaled.drop(columns=remove)\n",
    "X_test_scaled=X_test_scaled.drop(columns=remove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84734608, 0.84876032, 0.84729932, 0.84595134, 0.84555043])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores=cross_val_score(LogisticRegression(class_weight=\"balanced\",random_state=40),X_train_scaled,y_train,scoring='roc_auc',cv=5,n_jobs=-1)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8469814996417732"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=1000, multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=40, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression(class_weight=\"balanced\",random_state=40,max_iter=1000)\n",
    "logreg.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=logreg.predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=logreg.predict_proba(X_train_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred=logreg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_prob=logreg.predict_proba(X_test_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8475471324085696"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.66      0.79    334399\n",
      "           1       0.28      0.94      0.43     46710\n",
      "\n",
      "    accuracy                           0.69    381109\n",
      "   macro avg       0.63      0.80      0.61    381109\n",
      "weighted avg       0.90      0.69      0.75    381109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    74436\n",
       "1    52601\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "subB=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_pred})\n",
    "subB.to_csv(\"Logistic Model Probs.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Cutoffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=logreg.predict_proba(X_train_scaled)\n",
    "y_train_prob=y_train_prob[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score,roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1,auc_roc=[],[]\n",
    "\n",
    "for i in np.linspace(0.3,0.8,100):    \n",
    "    \n",
    "    x=list(map(lambda x: 1 if (x>i) else 0, y_train_prob))\n",
    "    \n",
    "    f1.append(f1_score(y_train,x))\n",
    "    auc_roc.append(roc_auc_score(y_train,x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame=pd.DataFrame({\"i\":np.linspace(0.3,0.8,100),\"f1_score\":f1,\"auc_roc_score\":auc_roc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fa92fd4288>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"i\",y=\"f1_score\",data=frame)\n",
    "sns.lineplot(x=\"i\",y=\"auc_roc_score\",data=frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>i</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>auc_roc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.527273</td>\n",
       "      <td>0.433705</td>\n",
       "      <td>0.798921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           i  f1_score  auc_roc_score\n",
       "45  0.527273  0.433705       0.798921"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[frame[\"auc_roc_score\"]==frame[\"auc_roc_score\"].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00448648, 0.79628202, 0.76666642, ..., 0.00314449, 0.00476199,\n",
       "       0.01220346])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_prob=logreg.predict_proba(X_test_scaled)\n",
    "y_test_prob=y_test_prob[:,1]\n",
    "y_test_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted=list(map(lambda x: 1 if (x>0.527273) else 0, y_test_prob))\n",
    "sub2_df=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":predicted})\n",
    "sub2_df.to_csv(\"submission2_RemovedFeatures_CutoffValues.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambdas=[0.001,0.01,0.1,1,10,100,1000,10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "means=[]\n",
    "stds=[]\n",
    "\n",
    "for i in lambdas:\n",
    "    scores=cross_val_score(LogisticRegression(class_weight=\"balanced\",random_state=40,C=i,n_jobs=-1),X_train_scaled,y_train,scoring='roc_auc',cv=5)\n",
    "    \n",
    "    means.append(scores.mean())\n",
    "    stds.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({\"i\":lambdas,\"mean\":means,\"std\":stds})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "sns.lineplot(x=lambdas,y=means,label=\"Mean\",ax=ax[0])\n",
    "sns.lineplot(x=lambdas,y=stds,label=\"Std\",ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8490471362801161"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10.0, class_weight='balanced', dual=False,\n",
       "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
       "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
       "                   random_state=40, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression(class_weight=\"balanced\",random_state=40,C=10.0,n_jobs=-1)\n",
    "logreg.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=logreg.predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred=logreg.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    68957\n",
       "1    58080\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub5_df=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_pred})\n",
    "sub5_df.to_csv(\"submission5.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Without Any Changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score,classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores=cross_val_score(DecisionTreeClassifier(random_state=40),X_train_scaled,y_train,scoring='roc_auc',cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.30738544, 0.30040025, 0.29624552, 0.29411765, 0.29103144])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2978360588082025"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree=DecisionTreeClassifier(random_state=40)\n",
    "tree.fit(X_train_scaled,y_train)\n",
    "\n",
    "y_train_pred=tree.predict(X_train_scaled)\n",
    "y_test_pred=tree.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_prob=tree.predict_proba(X_train_scaled)[:,1]\n",
    "y_test_prob=tree.predict_proba(X_test_scaled)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. ])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_test_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999100499339"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    111468\n",
       "1     15569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subA=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_prob})\n",
    "subA.to_csv(\"Proper Submission 3.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Pruning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=40, splitter='best')"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree=DecisionTreeClassifier(random_state=40)\n",
    "tree.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.get_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "means=[]\n",
    "stds=[]\n",
    "\n",
    "for i in range(2,10):\n",
    "    scores=cross_val_score(DecisionTreeClassifier(max_depth=i,random_state=40),X_train_scaled,y_train,scoring='roc_auc',cv=5)\n",
    "    \n",
    "    means.append(scores.mean())\n",
    "    stds.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16756"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(alphas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22bb5a875c8>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "sns.lineplot(x=range(2,88),y=means,label=\"Mean\",ax=ax[0])\n",
    "sns.lineplot(x=range(2,88),y=stds,label=\"Std\",ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>i</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.807251</td>\n",
       "      <td>0.000577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.824340</td>\n",
       "      <td>0.000990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.833669</td>\n",
       "      <td>0.000759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.837987</td>\n",
       "      <td>0.000828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.842928</td>\n",
       "      <td>0.000669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.847815</td>\n",
       "      <td>0.001179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.849829</td>\n",
       "      <td>0.001403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>0.850441</td>\n",
       "      <td>0.001284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.850405</td>\n",
       "      <td>0.001606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>0.849253</td>\n",
       "      <td>0.001806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    i      mean       std\n",
       "0   2  0.807251  0.000577\n",
       "1   3  0.824340  0.000990\n",
       "2   4  0.833669  0.000759\n",
       "3   5  0.837987  0.000828\n",
       "4   6  0.842928  0.000669\n",
       "5   7  0.847815  0.001179\n",
       "6   8  0.849829  0.001403\n",
       "7   9  0.850441  0.001284\n",
       "8  10  0.850405  0.001606\n",
       "9  11  0.849253  0.001806"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({\"i\":range(2,88),\"mean\":means,\"std\":stds})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=9, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=40, splitter='best')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree=DecisionTreeClassifier(max_depth=9,random_state=40)\n",
    "\n",
    "tree.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=tree.predict(X_train_scaled)\n",
    "y_test_pred=tree.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5014283331052538"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.93    334399\n",
      "           1       0.75      0.00      0.01     46710\n",
      "\n",
      "    accuracy                           0.88    381109\n",
      "   macro avg       0.81      0.50      0.47    381109\n",
      "weighted avg       0.86      0.88      0.82    381109\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    127036\n",
       "1         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59987618, 0.60221316, 0.60418947, 0.59387726, 0.59883199])"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores=cross_val_score(DecisionTreeClassifier(random_state=40,class_weight='balanced'),X_train_scaled,y_train,scoring='roc_auc',cv=5,n_jobs=2)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
       "                       max_depth=9, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=40, splitter='best')"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree=DecisionTreeClassifier(max_depth=9,random_state=40,class_weight='balanced')\n",
    "\n",
    "tree.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=tree.predict(X_train_scaled)\n",
    "y_test_pred=tree.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8007646150632153"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train,y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    120821\n",
       "1      6216\n",
       "dtype: int64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test_pred).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub4_df=pd.DataFrame({\"id\":pd.read_csv(\"test.csv\")[\"id\"],\"Response\":y_test_pred})\n",
    "sub4_df.to_csv(\"submission4.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbours=range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  \"timeout or by a memory leak.\", UserWarning\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-83aa51287bda>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m100\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----> 5\u001b[1;33m     \u001b[0mscores\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mX_train_scaled\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'roc_auc'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\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      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mmeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\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\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    388\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 390\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    391\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    234\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    235\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 236\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    237\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\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\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1005\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1006\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1007\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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[0m\u001b[0;32m   1008\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1009\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    833\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    834\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 835\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\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    836\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    752\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    753\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 754\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\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    755\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    756\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\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\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    207\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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    208\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 209\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\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    210\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\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\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\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[0m\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\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    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 256\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\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    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 256\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    257\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    513\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\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    516\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\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\\sklearn\\neighbors\\_base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m   1153\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\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   1154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1155\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\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[0m\u001b[0;32m   1156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_base.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    454\u001b[0m             self._tree = KDTree(X, self.leaf_size,\n\u001b[0;32m    455\u001b[0m                                 \u001b[0mmetric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meffective_metric_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 456\u001b[1;33m                                 **self.effective_metric_params_)\n\u001b[0m\u001b[0;32m    457\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'brute'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    458\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "means=[]\n",
    "stds=[]\n",
    "\n",
    "for i in range(2,100):\n",
    "    scores=cross_val_score(KNeighborsClassifier(n_neighbors=i,n_jobs=-1),X_train_scaled,y_train,scoring='roc_auc',cv=5)\n",
    "    \n",
    "    means.append(scores.mean())\n",
    "    stds.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6650271612771675, 0.7071946212340395]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "sns.lineplot(x=range(2,100),y=means,label=\"Mean\",ax=ax[0])\n",
    "sns.lineplot(x=range(2,100),y=stds,label=\"Std\",ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({\"i\":range(2,100),\"mean\":means,\"std\":stds})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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