DSN Bootcamp Qualification Hackathon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import category_encoders as ce\n",
    "import catboost as ctb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>...</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4A9AR6FQ</td>\n",
       "      <td>1955</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>77504.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22-06-2014</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>56784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>01-08-2013</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>1962</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>103714.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-11-2013</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>1979</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Single</td>\n",
       "      <td>46311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13-09-2013</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>1959</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>87486.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21-01-2014</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>165</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_4A9AR6FQ           1955      Graduation       Divorced   \n",
       "1  ID_X28T3VEK           1958      Graduation       Together   \n",
       "2  ID_AWXARH57           1962      Graduation         Single   \n",
       "3  ID_FQVZHE81           1979        2n Cycle         Single   \n",
       "4  ID_QVLWGPRN           1959      Graduation        Married   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            77504.4                   1                   1    22-06-2014   \n",
       "1            56784.0                   0                   1    01-08-2013   \n",
       "2           103714.8                   0                   0    21-11-2013   \n",
       "3            46311.6                   1                   0    13-09-2013   \n",
       "4            87486.0                   0                   0    21-01-2014   \n",
       "\n",
       "   Recency  Discounted_Purchases  ...  Amount_on_SweetProducts  \\\n",
       "0       56                     2  ...                        6   \n",
       "1       17                     6  ...                       15   \n",
       "2       17                     0  ...                       18   \n",
       "3       49                     3  ...                       24   \n",
       "4       59                     1  ...                      165   \n",
       "\n",
       "   Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "0                   20               3             0             0   \n",
       "1                   19               6             0             0   \n",
       "2                   37               2             0             0   \n",
       "3                   24               8             0             0   \n",
       "4                  203               1             0             0   \n",
       "\n",
       "   Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  Response  \n",
       "0             0             0             0             0         0  \n",
       "1             0             0             0             0         0  \n",
       "2             0             1             0             0         1  \n",
       "3             0             0             0             0         0  \n",
       "4             0             1             0             0         0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_of_Birth', 'Education_Level', 'Marital_Status',\n",
       "       'Disposable_Income', 'No_of_Kids_in_home', 'No_of_Teen_in_home',\n",
       "       'Date_Customer', 'Recency', 'Discounted_Purchases', 'WebPurchases',\n",
       "       'CatalogPurchases', 'StorePurchases', 'Amount_on_Wines',\n",
       "       'Amount_on_Fruits', 'Amount_on_MeatProducts', 'Amount_on_FishProducts',\n",
       "       'Amount_on_SweetProducts', 'Amount_on_GoldProds', 'WebVisitsMonth',\n",
       "       'Cmp3Accepted', 'Cmp4Accepted', 'Cmp5Accepted', 'Cmp1Accepted',\n",
       "       'Cmp2Accepted', 'Any_Complain', 'Response'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Year_of_Birth'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date_Customer']=df['Date_Customer'].astype('datetime64[ns]')\n",
    "\n",
    "df['Year_Customer']=pd.DatetimeIndex(df['Date_Customer']).year\n",
    "\n",
    "df['Year_Customer'].unique()\n",
    "\n",
    "df=df.drop(['Date_Customer'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Disposable_Income']=df['Disposable_Income'].fillna(df['Disposable_Income'].mean())\n",
    "df['Disposable_Income_Log']=np.log(df['Disposable_Income']+1)\n",
    "df=df.drop(['Disposable_Income'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
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       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "      <th>Year_Customer</th>\n",
       "      <th>Disposable_Income_Log</th>\n",
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       "      <th>1</th>\n",
       "      <td>ID_X28T3VEK</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2013</td>\n",
       "      <td>10.947027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_AWXARH57</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>11.549410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_FQVZHE81</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Single</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>2013</td>\n",
       "      <td>10.743169</td>\n",
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       "      <th>4</th>\n",
       "      <td>ID_QVLWGPRN</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.379245</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID Education_Level Marital_Status  No_of_Kids_in_home  \\\n",
       "0  ID_4A9AR6FQ      Graduation       Divorced                   1   \n",
       "1  ID_X28T3VEK      Graduation       Together                   0   \n",
       "2  ID_AWXARH57      Graduation         Single                   0   \n",
       "3  ID_FQVZHE81        2n Cycle         Single                   1   \n",
       "4  ID_QVLWGPRN      Graduation        Married                   0   \n",
       "\n",
       "   No_of_Teen_in_home  Recency  Discounted_Purchases  WebPurchases  \\\n",
       "0                   1       56                     2             1   \n",
       "1                   1       17                     6             5   \n",
       "2                   0       17                     0             7   \n",
       "3                   0       49                     3             4   \n",
       "4                   0       59                     1             3   \n",
       "\n",
       "   CatalogPurchases  StorePurchases  ...  WebVisitsMonth  Cmp3Accepted  \\\n",
       "0                 1               4  ...               3             0   \n",
       "1                 1               8  ...               6             0   \n",
       "2                 4               7  ...               2             0   \n",
       "3                 1               3  ...               8             0   \n",
       "4                 7               9  ...               1             0   \n",
       "\n",
       "   Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   Response  Year_Customer  Disposable_Income_Log  \n",
       "0         0           2014              11.258103  \n",
       "1         0           2013              10.947027  \n",
       "2         1           2013              11.549410  \n",
       "3         0           2013              10.743169  \n",
       "4         0           2014              11.379245  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.drop(['ID'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>...</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Response</th>\n",
       "      <th>Year_Customer</th>\n",
       "      <th>Disposable_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>11.258103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.947027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>469</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>11.549410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Education_Level Marital_Status  No_of_Kids_in_home  No_of_Teen_in_home  \\\n",
       "0      Graduation       Divorced                   1                   1   \n",
       "1      Graduation       Together                   0                   1   \n",
       "2      Graduation         Single                   0                   0   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       56                     2             1                 1   \n",
       "1       17                     6             5                 1   \n",
       "2       17                     0             7                 4   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  ...  WebVisitsMonth  Cmp3Accepted  \\\n",
       "0               4               71  ...               3             0   \n",
       "1               8              205  ...               6             0   \n",
       "2               7              469  ...               2             0   \n",
       "\n",
       "   Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             1             0             0   \n",
       "\n",
       "   Response  Year_Customer  Disposable_Income_Log  \n",
       "0         0           2014              11.258103  \n",
       "1         0           2013              10.947027  \n",
       "2         1           2013              11.549410  \n",
       "\n",
       "[3 rows x 25 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Education_Level             object\n",
       "Marital_Status              object\n",
       "No_of_Kids_in_home           int64\n",
       "No_of_Teen_in_home           int64\n",
       "Recency                      int64\n",
       "Discounted_Purchases         int64\n",
       "WebPurchases                 int64\n",
       "CatalogPurchases             int64\n",
       "StorePurchases               int64\n",
       "Amount_on_Wines              int64\n",
       "Amount_on_Fruits             int64\n",
       "Amount_on_MeatProducts       int64\n",
       "Amount_on_FishProducts       int64\n",
       "Amount_on_SweetProducts      int64\n",
       "Amount_on_GoldProds          int64\n",
       "WebVisitsMonth               int64\n",
       "Cmp3Accepted                 int64\n",
       "Cmp4Accepted                 int64\n",
       "Cmp5Accepted                 int64\n",
       "Cmp1Accepted                 int64\n",
       "Cmp2Accepted                 int64\n",
       "Any_Complain                 int64\n",
       "Response                     int64\n",
       "Year_Customer                int64\n",
       "Disposable_Income_Log      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2.drop(['Education_Level','Marital_Status','Year_Customer','Response'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.549410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   No_of_Kids_in_home  No_of_Teen_in_home  Recency  Discounted_Purchases  \\\n",
       "0                   1                   1       56                     2   \n",
       "1                   0                   1       17                     6   \n",
       "2                   0                   0       17                     0   \n",
       "\n",
       "   WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0             1                 1               4               71   \n",
       "1             5                 1               8              205   \n",
       "2             7                 4               7              469   \n",
       "\n",
       "   Amount_on_Fruits  Amount_on_MeatProducts  ...  Amount_on_SweetProducts  \\\n",
       "0                 3                      16  ...                        6   \n",
       "1                22                     111  ...                       15   \n",
       "2                31                     873  ...                       18   \n",
       "\n",
       "   Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "0                   20               3             0             0   \n",
       "1                   19               6             0             0   \n",
       "2                   37               2             0             0   \n",
       "\n",
       "   Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0             0             0             0             0   \n",
       "1             0             0             0             0   \n",
       "2             0             1             0             0   \n",
       "\n",
       "   Disposable_Income_Log  \n",
       "0              11.258103  \n",
       "1              10.947027  \n",
       "2              11.549410  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "df3=scaler.fit_transform(df3)\n",
    "Numerical_Data=pd.DataFrame(df3)\n",
    "Numerical_Data.columns=cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.245885</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.037201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>-1.334619</td>\n",
       "      <td>-1.183695</td>\n",
       "      <td>1.081454</td>\n",
       "      <td>0.438512</td>\n",
       "      <td>0.397776</td>\n",
       "      <td>0.487629</td>\n",
       "      <td>0.048259</td>\n",
       "      <td>3.019658</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.215460</td>\n",
       "      <td>-0.130667</td>\n",
       "      <td>-1.348282</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>3.811135</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>1.217447</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   No_of_Kids_in_home  No_of_Teen_in_home   Recency  Discounted_Purchases  \\\n",
       "0            0.998801            0.923845  0.020543             -0.151131   \n",
       "1           -0.852404            0.923845 -1.334619              1.913997   \n",
       "2           -0.852404           -0.914466 -1.334619             -1.183695   \n",
       "\n",
       "   WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0     -1.082374         -0.547512       -0.522607        -0.682846   \n",
       "1      0.360178         -0.547512        0.704570        -0.288766   \n",
       "2      1.081454          0.438512        0.397776         0.487629   \n",
       "\n",
       "   Amount_on_Fruits  Amount_on_MeatProducts  ...  Amount_on_SweetProducts  \\\n",
       "0         -0.659651               -0.653007  ...                -0.509256   \n",
       "1         -0.179283               -0.245885  ...                -0.288909   \n",
       "2          0.048259                3.019658  ...                -0.215460   \n",
       "\n",
       "   Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "0            -0.460283       -0.942970     -0.280008     -0.280008   \n",
       "1            -0.479672        0.272965     -0.280008     -0.280008   \n",
       "2            -0.130667       -1.348282     -0.280008     -0.280008   \n",
       "\n",
       "   Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "2     -0.295599      3.811135     -0.113666     -0.091434   \n",
       "\n",
       "   Disposable_Income_Log  \n",
       "0               0.646690  \n",
       "1               0.037201  \n",
       "2               1.217447  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Numerical_Data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Marital_Status']=df2['Marital_Status'].astype('category')\n",
    "df2['Education_Level']=df2['Education_Level'].astype('category')\n",
    "df2['Year_Customer']=df2['Year_Customer'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "Categorical_Data=df2[['Marital_Status','Education_Level','Year_Customer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3new\\lib\\site-packages\\category_encoders\\utils.py:21: FutureWarning: is_categorical is deprecated and will be removed in a future version.  Use is_categorical_dtype instead\n",
      "  elif pd.api.types.is_categorical(cols):\n"
     ]
    }
   ],
   "source": [
    "encoders=ce.TargetEncoder()\n",
    "Target_Response=df2['Response']\n",
    "encoders.fit_transform(Categorical_Data,Target_Response)\n",
    "Categorical_Data=encoders.transform(Categorical_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
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       "<p>1568 rows × 3 columns</p>\n",
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      "text/plain": [
       "      Marital_Status  Education_Level  Year_Customer\n",
       "0           0.201258         0.143392       0.096606\n",
       "1           0.100244         0.143392       0.129808\n",
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       "...              ...              ...            ...\n",
       "1563        0.126829         0.143392       0.096606\n",
       "1564        0.215805         0.143392       0.129808\n",
       "1565        0.126829         0.154472       0.129808\n",
       "1566        0.215805         0.208824       0.096606\n",
       "1567        0.100244         0.143392       0.096606\n",
       "\n",
       "[1568 rows x 3 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categorical_Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
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       "      <th>No_of_Kids_in_home</th>\n",
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       "      <td>-1.334619</td>\n",
       "      <td>-1.183695</td>\n",
       "      <td>1.081454</td>\n",
       "      <td>0.438512</td>\n",
       "      <td>0.397776</td>\n",
       "      <td>0.487629</td>\n",
       "      <td>0.048259</td>\n",
       "      <td>3.019658</td>\n",
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       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>3.811135</td>\n",
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       "      <td>-0.511586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
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       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.362218</td>\n",
       "      <td>0.215805</td>\n",
       "      <td>0.098592</td>\n",
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       "      <th>4</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>0.124786</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.361098</td>\n",
       "      <td>1.424536</td>\n",
       "      <td>1.011365</td>\n",
       "      <td>0.319998</td>\n",
       "      <td>2.222556</td>\n",
       "      <td>1.185468</td>\n",
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       "      <td>-0.280008</td>\n",
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       "      <td>-1.091385</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.829401</td>\n",
       "      <td>-0.806363</td>\n",
       "      <td>-0.634369</td>\n",
       "      <td>-0.678720</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.101853</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.096606</td>\n",
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       "      <th>1564</th>\n",
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       "      <td>0.263777</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.000460</td>\n",
       "      <td>-0.218837</td>\n",
       "      <td>0.704570</td>\n",
       "      <td>-0.400520</td>\n",
       "      <td>0.604475</td>\n",
       "      <td>-0.293026</td>\n",
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       "      <th>1565</th>\n",
       "      <td>-0.852404</td>\n",
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       "      <td>0.646002</td>\n",
       "      <td>0.881433</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.218837</td>\n",
       "      <td>1.011365</td>\n",
       "      <td>1.519882</td>\n",
       "      <td>-0.457391</td>\n",
       "      <td>-0.494444</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.571328</td>\n",
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       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.729665</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.154472</td>\n",
       "      <td>0.129808</td>\n",
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       "      <th>1566</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>-0.952394</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-1.082374</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.724018</td>\n",
       "      <td>-0.659651</td>\n",
       "      <td>-0.683005</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.102273</td>\n",
       "      <td>0.215805</td>\n",
       "      <td>0.208824</td>\n",
       "      <td>0.096606</td>\n",
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       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>0.784993</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.829401</td>\n",
       "      <td>-0.838713</td>\n",
       "      <td>-0.609086</td>\n",
       "      <td>-0.644436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-1.356950</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.096606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1568 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      No_of_Kids_in_home  No_of_Teen_in_home   Recency  Discounted_Purchases  \\\n",
       "0               0.998801            0.923845  0.020543             -0.151131   \n",
       "1              -0.852404            0.923845 -1.334619              1.913997   \n",
       "2              -0.852404           -0.914466 -1.334619             -1.183695   \n",
       "3               0.998801           -0.914466 -0.222691              0.365151   \n",
       "4              -0.852404           -0.914466  0.124786             -0.667413   \n",
       "...                  ...                 ...       ...                   ...   \n",
       "1563            0.998801            0.923845 -1.091385             -0.151131   \n",
       "1564           -0.852404            0.923845  0.263777             -0.667413   \n",
       "1565           -0.852404            0.923845  0.646002              0.881433   \n",
       "1566            0.998801            0.923845 -0.952394             -0.151131   \n",
       "1567            0.998801           -0.914466  0.784993             -0.151131   \n",
       "\n",
       "      WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0        -1.082374         -0.547512       -0.522607        -0.682846   \n",
       "1         0.360178         -0.547512        0.704570        -0.288766   \n",
       "2         1.081454          0.438512        0.397776         0.487629   \n",
       "3        -0.000460         -0.547512       -0.829401        -0.726959   \n",
       "4        -0.361098          1.424536        1.011365         0.319998   \n",
       "...            ...               ...             ...              ...   \n",
       "1563     -0.721736         -0.876186       -0.829401        -0.806363   \n",
       "1564     -0.000460         -0.218837        0.704570        -0.400520   \n",
       "1565     -0.721736         -0.218837        1.011365         1.519882   \n",
       "1566     -1.082374         -0.876186       -0.522607        -0.724018   \n",
       "1567     -0.721736         -0.876186       -0.829401        -0.838713   \n",
       "\n",
       "      Amount_on_Fruits  Amount_on_MeatProducts  ...  Cmp3Accepted  \\\n",
       "0            -0.659651               -0.653007  ...     -0.280008   \n",
       "1            -0.179283               -0.245885  ...     -0.280008   \n",
       "2             0.048259                3.019658  ...     -0.280008   \n",
       "3            -0.356261               -0.511586  ...     -0.280008   \n",
       "4             2.222556                1.185468  ...     -0.280008   \n",
       "...                ...                     ...  ...           ...   \n",
       "1563         -0.634369               -0.678720  ...     -0.280008   \n",
       "1564          0.604475               -0.293026  ...     -0.280008   \n",
       "1565         -0.457391               -0.494444  ...     -0.280008   \n",
       "1566         -0.659651               -0.683005  ...     -0.280008   \n",
       "1567         -0.609086               -0.644436  ...     -0.280008   \n",
       "\n",
       "      Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0        -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1        -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "2        -0.280008     -0.295599      3.811135     -0.113666     -0.091434   \n",
       "3        -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "4        -0.280008     -0.295599      3.811135     -0.113666     -0.091434   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "1563     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1564     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1565      3.571328     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1566     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1567     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "\n",
       "      Disposable_Income_Log  Marital_Status  Education_Level  Year_Customer  \n",
       "0                  0.646690        0.201258         0.143392       0.096606  \n",
       "1                  0.037201        0.100244         0.143392       0.129808  \n",
       "2                  1.217447        0.215805         0.143392       0.129808  \n",
       "3                 -0.362218        0.215805         0.098592       0.129808  \n",
       "4                  0.884045        0.126829         0.143392       0.096606  \n",
       "...                     ...             ...              ...            ...  \n",
       "1563              -0.101853        0.126829         0.143392       0.096606  \n",
       "1564               0.561060        0.215805         0.143392       0.129808  \n",
       "1565               0.729665        0.126829         0.154472       0.129808  \n",
       "1566               0.102273        0.215805         0.208824       0.096606  \n",
       "1567              -1.356950        0.100244         0.143392       0.096606  \n",
       "\n",
       "[1568 rows x 24 columns]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=Numerical_Data\n",
    "X['Marital_Status']=Categorical_Data['Marital_Status']\n",
    "X['Education_Level']=Categorical_Data['Education_Level']\n",
    "X['Year_Customer']=Categorical_Data['Year_Customer']\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_impute=X['Disposable_Income_Log'].mean()\n",
    "#df['Disposable_Income']=df['Disposable_Income'].fillna(df['Disposable_Income'].mean())\n",
    "X['Disposable_Income_Log']=X['Disposable_Income_Log'].fillna(mean_impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Disposable_Income_Log</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Year_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1.568000e+03</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "      <td>1568.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.466149e-17</td>\n",
       "      <td>-1.636304e-16</td>\n",
       "      <td>3.405722e-17</td>\n",
       "      <td>1.791368e-17</td>\n",
       "      <td>2.173715e-17</td>\n",
       "      <td>-1.925897e-17</td>\n",
       "      <td>1.883414e-17</td>\n",
       "      <td>-4.269544e-17</td>\n",
       "      <td>3.547332e-17</td>\n",
       "      <td>-9.771095e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.755043e-16</td>\n",
       "      <td>1.373618e-16</td>\n",
       "      <td>-4.022434e-16</td>\n",
       "      <td>4.078370e-17</td>\n",
       "      <td>4.835453e-16</td>\n",
       "      <td>6.919423e-17</td>\n",
       "      <td>7.904675e-16</td>\n",
       "      <td>0.151722</td>\n",
       "      <td>0.152423</td>\n",
       "      <td>0.152423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>1.000319e+00</td>\n",
       "      <td>0.051990</td>\n",
       "      <td>0.037160</td>\n",
       "      <td>0.062879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-8.524043e-01</td>\n",
       "      <td>-9.144656e-01</td>\n",
       "      <td>-1.682096e+00</td>\n",
       "      <td>-1.183695e+00</td>\n",
       "      <td>-1.443012e+00</td>\n",
       "      <td>-8.761862e-01</td>\n",
       "      <td>-1.749784e+00</td>\n",
       "      <td>-8.769447e-01</td>\n",
       "      <td>-6.596513e-01</td>\n",
       "      <td>-7.215749e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.955988e-01</td>\n",
       "      <td>-2.623890e-01</td>\n",
       "      <td>-1.136657e-01</td>\n",
       "      <td>-9.143374e-02</td>\n",
       "      <td>-6.444839e+00</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.096606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.524043e-01</td>\n",
       "      <td>-9.144656e-01</td>\n",
       "      <td>-8.481505e-01</td>\n",
       "      <td>-6.674130e-01</td>\n",
       "      <td>-7.217359e-01</td>\n",
       "      <td>-8.761862e-01</td>\n",
       "      <td>-8.294011e-01</td>\n",
       "      <td>-8.122450e-01</td>\n",
       "      <td>-6.343687e-01</td>\n",
       "      <td>-6.572925e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.955988e-01</td>\n",
       "      <td>-2.623890e-01</td>\n",
       "      <td>-1.136657e-01</td>\n",
       "      <td>-9.143374e-02</td>\n",
       "      <td>-5.629685e-01</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-8.524043e-01</td>\n",
       "      <td>-9.144656e-01</td>\n",
       "      <td>2.054282e-02</td>\n",
       "      <td>-1.511310e-01</td>\n",
       "      <td>-3.610980e-01</td>\n",
       "      <td>-2.188369e-01</td>\n",
       "      <td>-2.158126e-01</td>\n",
       "      <td>-4.005202e-01</td>\n",
       "      <td>-4.573911e-01</td>\n",
       "      <td>-4.473035e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.955988e-01</td>\n",
       "      <td>-2.623890e-01</td>\n",
       "      <td>-1.136657e-01</td>\n",
       "      <td>-9.143374e-02</td>\n",
       "      <td>1.900526e-01</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.988006e-01</td>\n",
       "      <td>9.238447e-01</td>\n",
       "      <td>8.544884e-01</td>\n",
       "      <td>3.651510e-01</td>\n",
       "      <td>7.208160e-01</td>\n",
       "      <td>4.385123e-01</td>\n",
       "      <td>7.045703e-01</td>\n",
       "      <td>5.773263e-01</td>\n",
       "      <td>1.746719e-01</td>\n",
       "      <td>2.726588e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.800079e-01</td>\n",
       "      <td>-2.955988e-01</td>\n",
       "      <td>-2.623890e-01</td>\n",
       "      <td>-1.136657e-01</td>\n",
       "      <td>-9.143374e-02</td>\n",
       "      <td>7.344572e-01</td>\n",
       "      <td>0.201258</td>\n",
       "      <td>0.154472</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.850005e+00</td>\n",
       "      <td>2.762155e+00</td>\n",
       "      <td>1.757929e+00</td>\n",
       "      <td>6.560535e+00</td>\n",
       "      <td>8.294213e+00</td>\n",
       "      <td>8.326704e+00</td>\n",
       "      <td>2.238542e+00</td>\n",
       "      <td>3.513807e+00</td>\n",
       "      <td>4.371570e+00</td>\n",
       "      <td>6.670896e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>3.571328e+00</td>\n",
       "      <td>3.571328e+00</td>\n",
       "      <td>3.382964e+00</td>\n",
       "      <td>3.811135e+00</td>\n",
       "      <td>8.797727e+00</td>\n",
       "      <td>1.093688e+01</td>\n",
       "      <td>5.220210e+00</td>\n",
       "      <td>0.406522</td>\n",
       "      <td>0.208824</td>\n",
       "      <td>0.266289</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       No_of_Kids_in_home  No_of_Teen_in_home       Recency  \\\n",
       "count        1.568000e+03        1.568000e+03  1.568000e+03   \n",
       "mean         5.466149e-17       -1.636304e-16  3.405722e-17   \n",
       "std          1.000319e+00        1.000319e+00  1.000319e+00   \n",
       "min         -8.524043e-01       -9.144656e-01 -1.682096e+00   \n",
       "25%         -8.524043e-01       -9.144656e-01 -8.481505e-01   \n",
       "50%         -8.524043e-01       -9.144656e-01  2.054282e-02   \n",
       "75%          9.988006e-01        9.238447e-01  8.544884e-01   \n",
       "max          2.850005e+00        2.762155e+00  1.757929e+00   \n",
       "\n",
       "       Discounted_Purchases  WebPurchases  CatalogPurchases  StorePurchases  \\\n",
       "count          1.568000e+03  1.568000e+03      1.568000e+03    1.568000e+03   \n",
       "mean           1.791368e-17  2.173715e-17     -1.925897e-17    1.883414e-17   \n",
       "std            1.000319e+00  1.000319e+00      1.000319e+00    1.000319e+00   \n",
       "min           -1.183695e+00 -1.443012e+00     -8.761862e-01   -1.749784e+00   \n",
       "25%           -6.674130e-01 -7.217359e-01     -8.761862e-01   -8.294011e-01   \n",
       "50%           -1.511310e-01 -3.610980e-01     -2.188369e-01   -2.158126e-01   \n",
       "75%            3.651510e-01  7.208160e-01      4.385123e-01    7.045703e-01   \n",
       "max            6.560535e+00  8.294213e+00      8.326704e+00    2.238542e+00   \n",
       "\n",
       "       Amount_on_Wines  Amount_on_Fruits  Amount_on_MeatProducts  ...  \\\n",
       "count     1.568000e+03      1.568000e+03            1.568000e+03  ...   \n",
       "mean     -4.269544e-17      3.547332e-17           -9.771095e-17  ...   \n",
       "std       1.000319e+00      1.000319e+00            1.000319e+00  ...   \n",
       "min      -8.769447e-01     -6.596513e-01           -7.215749e-01  ...   \n",
       "25%      -8.122450e-01     -6.343687e-01           -6.572925e-01  ...   \n",
       "50%      -4.005202e-01     -4.573911e-01           -4.473035e-01  ...   \n",
       "75%       5.773263e-01      1.746719e-01            2.726588e-01  ...   \n",
       "max       3.513807e+00      4.371570e+00            6.670896e+00  ...   \n",
       "\n",
       "       Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  \\\n",
       "count  1.568000e+03  1.568000e+03  1.568000e+03  1.568000e+03  1.568000e+03   \n",
       "mean  -8.755043e-16  1.373618e-16 -4.022434e-16  4.078370e-17  4.835453e-16   \n",
       "std    1.000319e+00  1.000319e+00  1.000319e+00  1.000319e+00  1.000319e+00   \n",
       "min   -2.800079e-01 -2.800079e-01 -2.955988e-01 -2.623890e-01 -1.136657e-01   \n",
       "25%   -2.800079e-01 -2.800079e-01 -2.955988e-01 -2.623890e-01 -1.136657e-01   \n",
       "50%   -2.800079e-01 -2.800079e-01 -2.955988e-01 -2.623890e-01 -1.136657e-01   \n",
       "75%   -2.800079e-01 -2.800079e-01 -2.955988e-01 -2.623890e-01 -1.136657e-01   \n",
       "max    3.571328e+00  3.571328e+00  3.382964e+00  3.811135e+00  8.797727e+00   \n",
       "\n",
       "       Any_Complain  Disposable_Income_Log  Marital_Status  Education_Level  \\\n",
       "count  1.568000e+03           1.568000e+03     1568.000000      1568.000000   \n",
       "mean   6.919423e-17           7.904675e-16        0.151722         0.152423   \n",
       "std    1.000319e+00           1.000319e+00        0.051990         0.037160   \n",
       "min   -9.143374e-02          -6.444839e+00        0.100244         0.026316   \n",
       "25%   -9.143374e-02          -5.629685e-01        0.100244         0.143392   \n",
       "50%   -9.143374e-02           1.900526e-01        0.126829         0.143392   \n",
       "75%   -9.143374e-02           7.344572e-01        0.201258         0.154472   \n",
       "max    1.093688e+01           5.220210e+00        0.406522         0.208824   \n",
       "\n",
       "       Year_Customer  \n",
       "count    1568.000000  \n",
       "mean        0.152423  \n",
       "std         0.062879  \n",
       "min         0.096606  \n",
       "25%         0.129808  \n",
       "50%         0.129808  \n",
       "75%         0.129808  \n",
       "max         0.266289  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=Target_Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Disposable_Income_Log</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Year_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>1.653686</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.361098</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.821068</td>\n",
       "      <td>-0.634369</td>\n",
       "      <td>-0.635865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.743943</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.096606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>-1.473610</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>0.720816</td>\n",
       "      <td>2.081886</td>\n",
       "      <td>0.397776</td>\n",
       "      <td>1.578700</td>\n",
       "      <td>1.185973</td>\n",
       "      <td>0.906911</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>1.061662</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.096606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>1.028227</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.829401</td>\n",
       "      <td>-0.812245</td>\n",
       "      <td>-0.583804</td>\n",
       "      <td>-0.644436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.641726</td>\n",
       "      <td>0.201258</td>\n",
       "      <td>0.154472</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>0.854488</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.829401</td>\n",
       "      <td>-0.774013</td>\n",
       "      <td>-0.583804</td>\n",
       "      <td>-0.635865</td>\n",
       "      <td>...</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.740676</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>-0.118448</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.829401</td>\n",
       "      <td>-0.850477</td>\n",
       "      <td>-0.558521</td>\n",
       "      <td>-0.644436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-1.147330</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>-1.056637</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>2.410560</td>\n",
       "      <td>1.318159</td>\n",
       "      <td>-0.056436</td>\n",
       "      <td>3.688942</td>\n",
       "      <td>-0.404449</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.680285</td>\n",
       "      <td>0.215805</td>\n",
       "      <td>0.098592</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>0.998801</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>1.271461</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>-0.361098</td>\n",
       "      <td>-0.547512</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.479924</td>\n",
       "      <td>-0.659651</td>\n",
       "      <td>-0.678720</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.482167</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.154472</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>1.723182</td>\n",
       "      <td>0.881433</td>\n",
       "      <td>0.720816</td>\n",
       "      <td>-0.547512</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.312293</td>\n",
       "      <td>-0.533239</td>\n",
       "      <td>-0.494444</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-0.170655</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.266289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.881433</td>\n",
       "      <td>-0.361098</td>\n",
       "      <td>0.438512</td>\n",
       "      <td>1.624953</td>\n",
       "      <td>2.328627</td>\n",
       "      <td>-0.356261</td>\n",
       "      <td>-0.310168</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.698066</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>0.229029</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.361098</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.588737</td>\n",
       "      <td>-0.634369</td>\n",
       "      <td>-0.648722</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.211288</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.208824</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      No_of_Kids_in_home  No_of_Teen_in_home   Recency  Discounted_Purchases  \\\n",
       "1535            0.998801           -0.914466  1.653686             -0.151131   \n",
       "983            -0.852404            0.923845 -1.473610             -0.667413   \n",
       "432             0.998801           -0.914466  1.028227             -0.667413   \n",
       "962             0.998801           -0.914466  0.854488             -0.151131   \n",
       "976             0.998801           -0.914466 -0.118448             -0.151131   \n",
       "...                  ...                 ...       ...                   ...   \n",
       "236            -0.852404           -0.914466 -1.056637             -0.667413   \n",
       "1383            0.998801            0.923845  1.271461             -0.151131   \n",
       "1310           -0.852404            0.923845  1.723182              0.881433   \n",
       "1510           -0.852404            0.923845  0.263777              0.881433   \n",
       "925            -0.852404            0.923845  0.229029             -0.667413   \n",
       "\n",
       "      WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "1535     -0.361098         -0.876186       -0.522607        -0.821068   \n",
       "983       0.720816          2.081886        0.397776         1.578700   \n",
       "432      -0.721736         -0.876186       -0.829401        -0.812245   \n",
       "962      -0.721736         -0.876186       -0.829401        -0.774013   \n",
       "976      -0.721736         -0.876186       -0.829401        -0.850477   \n",
       "...            ...               ...             ...              ...   \n",
       "236      -0.721736          2.410560        1.318159        -0.056436   \n",
       "1383     -0.361098         -0.547512       -0.522607        -0.479924   \n",
       "1310      0.720816         -0.547512       -0.522607        -0.312293   \n",
       "1510     -0.361098          0.438512        1.624953         2.328627   \n",
       "925      -0.361098         -0.876186       -0.522607        -0.588737   \n",
       "\n",
       "      Amount_on_Fruits  Amount_on_MeatProducts  ...  Cmp3Accepted  \\\n",
       "1535         -0.634369               -0.635865  ...     -0.280008   \n",
       "983           1.185973                0.906911  ...     -0.280008   \n",
       "432          -0.583804               -0.644436  ...     -0.280008   \n",
       "962          -0.583804               -0.635865  ...      3.571328   \n",
       "976          -0.558521               -0.644436  ...     -0.280008   \n",
       "...                ...                     ...  ...           ...   \n",
       "236           3.688942               -0.404449  ...     -0.280008   \n",
       "1383         -0.659651               -0.678720  ...     -0.280008   \n",
       "1310         -0.533239               -0.494444  ...     -0.280008   \n",
       "1510         -0.356261               -0.310168  ...     -0.280008   \n",
       "925          -0.634369               -0.648722  ...     -0.280008   \n",
       "\n",
       "      Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "1535     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "983       3.571328     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "432      -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "962      -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "976      -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "236      -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1383     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1310     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1510      3.571328     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "925       3.571328     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "\n",
       "      Disposable_Income_Log  Marital_Status  Education_Level  Year_Customer  \n",
       "1535              -0.743943        0.126829         0.143392       0.096606  \n",
       "983                1.061662        0.126829         0.143392       0.096606  \n",
       "432               -0.641726        0.201258         0.154472       0.129808  \n",
       "962               -0.740676        0.126829         0.143392       0.129808  \n",
       "976               -1.147330        0.100244         0.143392       0.129808  \n",
       "...                     ...             ...              ...            ...  \n",
       "236                0.680285        0.215805         0.098592       0.129808  \n",
       "1383               0.482167        0.100244         0.154472       0.129808  \n",
       "1310              -0.170655        0.126829         0.143392       0.266289  \n",
       "1510               0.698066        0.126829         0.143392       0.129808  \n",
       "925                0.211288        0.100244         0.208824       0.129808  \n",
       "\n",
       "[314 rows x 24 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89171974522293"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticModel=LogisticRegression()\n",
    "LogisticModel.fit(X_train,y_train)\n",
    "LogisticModel.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_confusion_matrix(LogisticModel,X_test, y_test)\n",
    "metrics.plot_roc_curve(LogisticModel, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.89171974522293\n",
      "0.7121751321039578\n",
      "0.8786882813675206\n"
     ]
    }
   ],
   "source": [
    "ypred=LogisticModel.predict(X_test)\n",
    "print (f1_score(y_test, ypred, average='micro'))\n",
    "print (f1_score(y_test, ypred, average='macro'))\n",
    "print (f1_score(y_test, ypred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.880667 using {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.880133 (0.023699) with: {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.880400 (0.023741) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.880667 (0.023419) with: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.879611 (0.023457) with: {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.879611 (0.023457) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.880142 (0.023101) with: {'C': 10, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.877746 (0.021583) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.877481 (0.021591) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.878811 (0.020992) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.879350 (0.020145) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.879350 (0.020145) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.879886 (0.020977) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.867638 (0.014778) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.867638 (0.014778) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.873232 (0.020541) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "# example of grid searching key hyperparametres for logistic regression\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model = LogisticRegression()\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "\n",
    "# define grid search\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=cv, scoring='accuracy',error_score=0)\n",
    "grid_result = grid_search.fit(X_train, y_train)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8789808917197452"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisticModel1=LogisticRegression(C=100, penalty='l2',solver='liblinear')\n",
    "LogisticModel1.fit(X_train,y_train)\n",
    "LogisticModel1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced-learn\n",
      "  Using cached https://files.pythonhosted.org/packages/b1/bd/4bb46fb4d317fd0f19aa7463d8906598e5fee073c0842b57cb112f023a45/imbalanced_learn-0.8.1-py3-none-any.whl\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\user\\anaconda3new\\lib\\site-packages (from imbalanced-learn) (0.13.2)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\user\\anaconda3new\\lib\\site-packages (from imbalanced-learn) (1.5.2)\n",
      "Requirement already satisfied: scikit-learn>=0.24 in c:\\users\\user\\anaconda3new\\lib\\site-packages (from imbalanced-learn) (1.0.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\user\\anaconda3new\\lib\\site-packages (from imbalanced-learn) (1.19.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda3new\\lib\\site-packages (from scikit-learn>=0.24->imbalanced-learn) (2.1.0)\n",
      "Installing collected packages: imbalanced-learn\n",
      "Successfully installed imbalanced-learn-0.8.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_confusion_matrix(LogisticModel,X_test, y_test)\n",
    "metrics.plot_roc_curve(LogisticModel, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8789808917197452\n",
      "0.7019384492406076\n",
      "0.8702019276298706\n"
     ]
    }
   ],
   "source": [
    "ypred=LogisticModel1.predict(X_test)\n",
    "print (f1_score(y_test, ypred, average='micro'))\n",
    "print (f1_score(y_test, ypred, average='macro'))\n",
    "print (f1_score(y_test, ypred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.011348\n",
      "0:\tlearn: 0.6839824\ttotal: 549ms\tremaining: 9m 8s\n",
      "1:\tlearn: 0.6757509\ttotal: 556ms\tremaining: 4m 37s\n",
      "2:\tlearn: 0.6673686\ttotal: 564ms\tremaining: 3m 7s\n",
      "3:\tlearn: 0.6604963\ttotal: 571ms\tremaining: 2m 22s\n",
      "4:\tlearn: 0.6518588\ttotal: 579ms\tremaining: 1m 55s\n",
      "5:\tlearn: 0.6418911\ttotal: 586ms\tremaining: 1m 37s\n",
      "6:\tlearn: 0.6339143\ttotal: 593ms\tremaining: 1m 24s\n",
      "7:\tlearn: 0.6259999\ttotal: 601ms\tremaining: 1m 14s\n",
      "8:\tlearn: 0.6189453\ttotal: 609ms\tremaining: 1m 7s\n",
      "9:\tlearn: 0.6117216\ttotal: 617ms\tremaining: 1m 1s\n",
      "10:\tlearn: 0.6059859\ttotal: 624ms\tremaining: 56.1s\n",
      "11:\tlearn: 0.5994744\ttotal: 633ms\tremaining: 52.1s\n",
      "12:\tlearn: 0.5918318\ttotal: 640ms\tremaining: 48.6s\n",
      "13:\tlearn: 0.5848184\ttotal: 646ms\tremaining: 45.5s\n",
      "14:\tlearn: 0.5781920\ttotal: 654ms\tremaining: 43s\n",
      "15:\tlearn: 0.5696781\ttotal: 660ms\tremaining: 40.6s\n",
      "16:\tlearn: 0.5625248\ttotal: 666ms\tremaining: 38.5s\n",
      "17:\tlearn: 0.5563273\ttotal: 672ms\tremaining: 36.6s\n",
      "18:\tlearn: 0.5501370\ttotal: 678ms\tremaining: 35s\n",
      "19:\tlearn: 0.5430394\ttotal: 683ms\tremaining: 33.5s\n",
      "20:\tlearn: 0.5373927\ttotal: 689ms\tremaining: 32.1s\n",
      "21:\tlearn: 0.5308088\ttotal: 696ms\tremaining: 30.9s\n",
      "22:\tlearn: 0.5259030\ttotal: 702ms\tremaining: 29.8s\n",
      "23:\tlearn: 0.5215292\ttotal: 707ms\tremaining: 28.8s\n",
      "24:\tlearn: 0.5168021\ttotal: 714ms\tremaining: 27.8s\n",
      "25:\tlearn: 0.5110607\ttotal: 723ms\tremaining: 27.1s\n",
      "26:\tlearn: 0.5053863\ttotal: 730ms\tremaining: 26.3s\n",
      "27:\tlearn: 0.5000609\ttotal: 737ms\tremaining: 25.6s\n",
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      "984:\tlearn: 0.0851777\ttotal: 7.79s\tremaining: 119ms\n",
      "985:\tlearn: 0.0850711\ttotal: 7.8s\tremaining: 111ms\n",
      "986:\tlearn: 0.0849581\ttotal: 7.8s\tremaining: 103ms\n",
      "987:\tlearn: 0.0848833\ttotal: 7.81s\tremaining: 94.9ms\n",
      "988:\tlearn: 0.0848153\ttotal: 7.82s\tremaining: 86.9ms\n",
      "989:\tlearn: 0.0847510\ttotal: 7.82s\tremaining: 79ms\n",
      "990:\tlearn: 0.0846708\ttotal: 7.83s\tremaining: 71.1ms\n",
      "991:\tlearn: 0.0846416\ttotal: 7.83s\tremaining: 63.2ms\n",
      "992:\tlearn: 0.0845617\ttotal: 7.84s\tremaining: 55.3ms\n",
      "993:\tlearn: 0.0844886\ttotal: 7.85s\tremaining: 47.4ms\n",
      "994:\tlearn: 0.0844074\ttotal: 7.85s\tremaining: 39.5ms\n",
      "995:\tlearn: 0.0843437\ttotal: 7.86s\tremaining: 31.6ms\n",
      "996:\tlearn: 0.0841914\ttotal: 7.86s\tremaining: 23.7ms\n",
      "997:\tlearn: 0.0841433\ttotal: 7.87s\tremaining: 15.8ms\n",
      "998:\tlearn: 0.0840675\ttotal: 7.88s\tremaining: 7.88ms\n",
      "999:\tlearn: 0.0839966\ttotal: 7.88s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8949044585987261"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CBC=ctb.CatBoostClassifier()\n",
    "CBC.fit(X_train,y_train)\n",
    "CBC.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_confusion_matrix(CBC,X_test, y_test)\n",
    "metrics.plot_roc_curve(CBC, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8949044585987261\n",
      "0.7379826535514704\n",
      "0.8865094649130705\n"
     ]
    }
   ],
   "source": [
    "ypred=CBC.predict(X_test)\n",
    "print (f1_score(y_test, ypred, average='micro'))\n",
    "print (f1_score(y_test, ypred, average='macro'))\n",
    "print (f1_score(y_test, ypred, average='weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.read_csv(\"Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "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>ID</th>\n",
       "      <th>Year_of_Birth</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Disposable_Income</th>\n",
       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Date_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>...</th>\n",
       "      <th>Amount_on_FishProducts</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_ZPMABNVX</td>\n",
       "      <td>1954</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>48556.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11-01-2013</td>\n",
       "      <td>85</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_WFE91NAA</td>\n",
       "      <td>1961</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Widow</td>\n",
       "      <td>57499.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22-11-2012</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_JV11RBRK</td>\n",
       "      <td>1973</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Married</td>\n",
       "      <td>17025.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28-02-2013</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_6B7SVKY9</td>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>91983.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16-08-2013</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>219</td>\n",
       "      <td>189</td>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_GOVUZ545</td>\n",
       "      <td>1959</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>78235.2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25-07-2013</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Year_of_Birth Education_Level Marital_Status  \\\n",
       "0  ID_ZPMABNVX           1954      Graduation         Single   \n",
       "1  ID_WFE91NAA           1961      Graduation          Widow   \n",
       "2  ID_JV11RBRK           1973           Basic        Married   \n",
       "3  ID_6B7SVKY9           1970      Graduation       Together   \n",
       "4  ID_GOVUZ545           1959      Graduation       Together   \n",
       "\n",
       "   Disposable_Income  No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  \\\n",
       "0            48556.8                   0                   1    11-01-2013   \n",
       "1            57499.2                   0                   1    22-11-2012   \n",
       "2            17025.6                   0                   0    28-02-2013   \n",
       "3            91983.6                   0                   0    16-08-2013   \n",
       "4            78235.2                   0                   2    25-07-2013   \n",
       "\n",
       "   Recency  Discounted_Purchases  ...  Amount_on_FishProducts  \\\n",
       "0       85                     6  ...                       7   \n",
       "1       79                     5  ...                       0   \n",
       "2       47                     1  ...                      16   \n",
       "3       98                     1  ...                     219   \n",
       "4       41                     2  ...                      12   \n",
       "\n",
       "   Amount_on_SweetProducts  Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  \\\n",
       "0                       23                   41               8             0   \n",
       "1                        0                   75               6             0   \n",
       "2                       12                   27               6             0   \n",
       "3                      189                  126               2             0   \n",
       "4                        0                  200               5             1   \n",
       "\n",
       "   Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \n",
       "0             0             0             0             0             0  \n",
       "1             1             0             0             0             0  \n",
       "2             0             0             0             0             0  \n",
       "3             0             1             1             0             0  \n",
       "4             0             0             0             0             0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=df_test.drop(['Year_of_Birth'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>ID_WFE91NAA</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Widow</td>\n",
       "      <td>57499.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22-11-2012</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>ID_JV11RBRK</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Married</td>\n",
       "      <td>17025.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28-02-2013</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_6B7SVKY9</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>91983.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16-08-2013</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>219</td>\n",
       "      <td>189</td>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>ID_GOVUZ545</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>78235.2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>25-07-2013</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID Education_Level Marital_Status  Disposable_Income  \\\n",
       "0  ID_ZPMABNVX      Graduation         Single            48556.8   \n",
       "1  ID_WFE91NAA      Graduation          Widow            57499.2   \n",
       "2  ID_JV11RBRK           Basic        Married            17025.6   \n",
       "3  ID_6B7SVKY9      Graduation       Together            91983.6   \n",
       "4  ID_GOVUZ545      Graduation       Together            78235.2   \n",
       "\n",
       "   No_of_Kids_in_home  No_of_Teen_in_home Date_Customer  Recency  \\\n",
       "0                   0                   1    11-01-2013       85   \n",
       "1                   0                   1    22-11-2012       79   \n",
       "2                   0                   0    28-02-2013       47   \n",
       "3                   0                   0    16-08-2013       98   \n",
       "4                   0                   2    25-07-2013       41   \n",
       "\n",
       "   Discounted_Purchases  WebPurchases  ...  Amount_on_FishProducts  \\\n",
       "0                     6             8  ...                       7   \n",
       "1                     5             7  ...                       0   \n",
       "2                     1             2  ...                      16   \n",
       "3                     1             4  ...                     219   \n",
       "4                     2             7  ...                      12   \n",
       "\n",
       "   Amount_on_SweetProducts  Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  \\\n",
       "0                       23                   41               8             0   \n",
       "1                        0                   75               6             0   \n",
       "2                       12                   27               6             0   \n",
       "3                      189                  126               2             0   \n",
       "4                        0                  200               5             1   \n",
       "\n",
       "   Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \n",
       "0             0             0             0             0             0  \n",
       "1             1             0             0             0             0  \n",
       "2             0             0             0             0             0  \n",
       "3             0             1             1             0             0  \n",
       "4             0             0             0             0             0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Date_Customer']=df_test['Date_Customer'].astype('datetime64[ns]')\n",
    "\n",
    "df_test['Year_Customer']=pd.DatetimeIndex(df_test['Date_Customer']).year\n",
    "\n",
    "df_test['Year_Customer'].unique()\n",
    "\n",
    "df_test=df_test.drop(['Date_Customer'],1)\n",
    "\n",
    "df_test['Disposable_Income']=df_test['Disposable_Income'].fillna(df_test['Disposable_Income'].mean())\n",
    "df_test['Disposable_Income_Log']=np.log(df_test['Disposable_Income']+1)\n",
    "df_test=df_test.drop(['Disposable_Income'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test2=df_test.drop(['ID'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2013</td>\n",
       "      <td>10.790510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Graduation</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>10.959544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Basic</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>9.742532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Education_Level Marital_Status  No_of_Kids_in_home  No_of_Teen_in_home  \\\n",
       "0      Graduation         Single                   0                   1   \n",
       "1      Graduation          Widow                   0                   1   \n",
       "2           Basic        Married                   0                   0   \n",
       "\n",
       "   Recency  Discounted_Purchases  WebPurchases  CatalogPurchases  \\\n",
       "0       85                     6             8                 2   \n",
       "1       79                     5             7                 4   \n",
       "2       47                     1             2                 0   \n",
       "\n",
       "   StorePurchases  Amount_on_Wines  ...  Amount_on_GoldProds  WebVisitsMonth  \\\n",
       "0               8              429  ...                   41               8   \n",
       "1               6              510  ...                   75               6   \n",
       "2               4                7  ...                   27               6   \n",
       "\n",
       "   Cmp3Accepted  Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             1             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "\n",
       "   Any_Complain  Year_Customer  Disposable_Income_Log  \n",
       "0             0           2013              10.790510  \n",
       "1             0           2012              10.959544  \n",
       "2             0           2013               9.742532  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test3=df_test2.drop(['Education_Level','Marital_Status','Year_Customer'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=df_test3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>No_of_Kids_in_home</th>\n",
       "      <th>No_of_Teen_in_home</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Discounted_Purchases</th>\n",
       "      <th>WebPurchases</th>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <th>StorePurchases</th>\n",
       "      <th>Amount_on_Wines</th>\n",
       "      <th>Amount_on_Fruits</th>\n",
       "      <th>Amount_on_MeatProducts</th>\n",
       "      <th>...</th>\n",
       "      <th>Amount_on_SweetProducts</th>\n",
       "      <th>Amount_on_GoldProds</th>\n",
       "      <th>WebVisitsMonth</th>\n",
       "      <th>Cmp3Accepted</th>\n",
       "      <th>Cmp4Accepted</th>\n",
       "      <th>Cmp5Accepted</th>\n",
       "      <th>Cmp1Accepted</th>\n",
       "      <th>Cmp2Accepted</th>\n",
       "      <th>Any_Complain</th>\n",
       "      <th>Disposable_Income_Log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>429</td>\n",
       "      <td>20</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.790510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>510</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.959544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.742532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   No_of_Kids_in_home  No_of_Teen_in_home  Recency  Discounted_Purchases  \\\n",
       "0                   0                   1       85                     6   \n",
       "1                   0                   1       79                     5   \n",
       "2                   0                   0       47                     1   \n",
       "\n",
       "   WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0             8                 2               8              429   \n",
       "1             7                 4               6              510   \n",
       "2             2                 0               4                7   \n",
       "\n",
       "   Amount_on_Fruits  Amount_on_MeatProducts  ...  Amount_on_SweetProducts  \\\n",
       "0                20                     118  ...                       23   \n",
       "1                 3                      26  ...                        0   \n",
       "2                10                      11  ...                       12   \n",
       "\n",
       "   Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "0                   41               8             0             0   \n",
       "1                   75               6             0             1   \n",
       "2                   27               6             0             0   \n",
       "\n",
       "   Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0             0             0             0             0   \n",
       "1             0             0             0             0   \n",
       "2             0             0             0             0   \n",
       "\n",
       "   Disposable_Income_Log  \n",
       "0              10.790510  \n",
       "1              10.959544  \n",
       "2               9.742532  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test3.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test3=scaler.transform(df_test3)\n",
    "Numerical_Data_Test=pd.DataFrame(df_test3)\n",
    "Numerical_Data_Test.columns=cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.914466</td>\n",
       "      <td>-0.292187</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.721736</td>\n",
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       "      <td>-0.482674</td>\n",
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       "<p>3 rows × 21 columns</p>\n",
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      "text/plain": [
       "   No_of_Kids_in_home  No_of_Teen_in_home   Recency  Discounted_Purchases  \\\n",
       "0           -0.852404            0.923845  1.028227              1.913997   \n",
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       "2           -0.852404           -0.914466 -0.292187             -0.667413   \n",
       "\n",
       "   WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0      1.442092         -0.218837        0.704570         0.369993   \n",
       "1      1.081454          0.438512        0.090982         0.608206   \n",
       "2     -0.721736         -0.876186       -0.522607        -0.871063   \n",
       "\n",
       "   Amount_on_Fruits  Amount_on_MeatProducts  ...  Amount_on_SweetProducts  \\\n",
       "0         -0.229848               -0.215887  ...                -0.093045   \n",
       "1         -0.659651               -0.610152  ...                -0.656154   \n",
       "2         -0.482674               -0.674434  ...                -0.362358   \n",
       "\n",
       "   Amount_on_GoldProds  WebVisitsMonth  Cmp3Accepted  Cmp4Accepted  \\\n",
       "0            -0.053110        1.083589     -0.280008     -0.280008   \n",
       "1             0.606123        0.272965     -0.280008      3.571328   \n",
       "2            -0.324559        0.272965     -0.280008     -0.280008   \n",
       "\n",
       "   Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
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       "2     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "\n",
       "   Disposable_Income_Log  \n",
       "0              -0.269463  \n",
       "1               0.061724  \n",
       "2              -2.322765  \n",
       "\n",
       "[3 rows x 21 columns]"
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     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "Numerical_Data_Test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "Categorical_Data_Test=df_test2[['Marital_Status','Education_Level','Year_Customer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "Categorical_Data_Test=encoders.transform(Categorical_Data_Test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>672 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "     Marital_Status  Education_Level  Year_Customer\n",
       "0          0.215805         0.143392       0.129808\n",
       "1          0.280000         0.143392       0.266289\n",
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       "4          0.100244         0.143392       0.129808\n",
       "..              ...              ...            ...\n",
       "667        0.126829         0.143392       0.266289\n",
       "668        0.201258         0.143392       0.129808\n",
       "669        0.126829         0.208824       0.129808\n",
       "670        0.100244         0.143392       0.129808\n",
       "671        0.126829         0.154472       0.129808\n",
       "\n",
       "[672 rows x 3 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Categorical_Data_Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.608206</td>\n",
       "      <td>-0.659651</td>\n",
       "      <td>-0.610152</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.061724</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.266289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>-0.292187</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.876186</td>\n",
       "      <td>-0.522607</td>\n",
       "      <td>-0.871063</td>\n",
       "      <td>-0.482674</td>\n",
       "      <td>-0.674434</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>-2.322765</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>1.479948</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>-0.000460</td>\n",
       "      <td>1.424536</td>\n",
       "      <td>1.624953</td>\n",
       "      <td>1.287552</td>\n",
       "      <td>0.933148</td>\n",
       "      <td>3.332499</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>3.382964</td>\n",
       "      <td>3.811135</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.982266</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>2.762155</td>\n",
       "      <td>-0.500673</td>\n",
       "      <td>-0.151131</td>\n",
       "      <td>1.081454</td>\n",
       "      <td>1.095862</td>\n",
       "      <td>1.624953</td>\n",
       "      <td>1.308138</td>\n",
       "      <td>-0.179283</td>\n",
       "      <td>0.054099</td>\n",
       "      <td>...</td>\n",
       "      <td>3.571328</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.665078</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>0.507011</td>\n",
       "      <td>1.397715</td>\n",
       "      <td>1.802730</td>\n",
       "      <td>1.095862</td>\n",
       "      <td>1.931747</td>\n",
       "      <td>1.349310</td>\n",
       "      <td>1.362950</td>\n",
       "      <td>0.208376</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.731525</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.266289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>-0.083700</td>\n",
       "      <td>0.881433</td>\n",
       "      <td>1.081454</td>\n",
       "      <td>2.410560</td>\n",
       "      <td>-0.215813</td>\n",
       "      <td>2.278632</td>\n",
       "      <td>-0.659651</td>\n",
       "      <td>0.349798</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.805608</td>\n",
       "      <td>0.201258</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>-0.914466</td>\n",
       "      <td>-0.570169</td>\n",
       "      <td>-0.667413</td>\n",
       "      <td>0.360178</td>\n",
       "      <td>-0.547512</td>\n",
       "      <td>-0.215813</td>\n",
       "      <td>-0.168190</td>\n",
       "      <td>-0.659651</td>\n",
       "      <td>-0.670149</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.270801</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.208824</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>-0.848150</td>\n",
       "      <td>2.430279</td>\n",
       "      <td>-0.000460</td>\n",
       "      <td>1.095862</td>\n",
       "      <td>0.704570</td>\n",
       "      <td>-0.024086</td>\n",
       "      <td>0.832017</td>\n",
       "      <td>0.036957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>10.936882</td>\n",
       "      <td>0.434478</td>\n",
       "      <td>0.100244</td>\n",
       "      <td>0.143392</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>-0.852404</td>\n",
       "      <td>0.923845</td>\n",
       "      <td>1.549443</td>\n",
       "      <td>0.365151</td>\n",
       "      <td>-0.721736</td>\n",
       "      <td>-0.218837</td>\n",
       "      <td>0.090982</td>\n",
       "      <td>-0.335821</td>\n",
       "      <td>-0.609086</td>\n",
       "      <td>-0.640151</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.280008</td>\n",
       "      <td>-0.295599</td>\n",
       "      <td>-0.262389</td>\n",
       "      <td>-0.113666</td>\n",
       "      <td>-0.091434</td>\n",
       "      <td>0.038567</td>\n",
       "      <td>0.126829</td>\n",
       "      <td>0.154472</td>\n",
       "      <td>0.129808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>672 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     No_of_Kids_in_home  No_of_Teen_in_home   Recency  Discounted_Purchases  \\\n",
       "0             -0.852404            0.923845  1.028227              1.913997   \n",
       "1             -0.852404            0.923845  0.819741              1.397715   \n",
       "2             -0.852404           -0.914466 -0.292187             -0.667413   \n",
       "3             -0.852404           -0.914466  1.479948             -0.667413   \n",
       "4             -0.852404            2.762155 -0.500673             -0.151131   \n",
       "..                  ...                 ...       ...                   ...   \n",
       "667           -0.852404            0.923845  0.507011              1.397715   \n",
       "668           -0.852404            0.923845 -0.083700              0.881433   \n",
       "669           -0.852404           -0.914466 -0.570169             -0.667413   \n",
       "670           -0.852404            0.923845 -0.848150              2.430279   \n",
       "671           -0.852404            0.923845  1.549443              0.365151   \n",
       "\n",
       "     WebPurchases  CatalogPurchases  StorePurchases  Amount_on_Wines  \\\n",
       "0        1.442092         -0.218837        0.704570         0.369993   \n",
       "1        1.081454          0.438512        0.090982         0.608206   \n",
       "2       -0.721736         -0.876186       -0.522607        -0.871063   \n",
       "3       -0.000460          1.424536        1.624953         1.287552   \n",
       "4        1.081454          1.095862        1.624953         1.308138   \n",
       "..            ...               ...             ...              ...   \n",
       "667      1.802730          1.095862        1.931747         1.349310   \n",
       "668      1.081454          2.410560       -0.215813         2.278632   \n",
       "669      0.360178         -0.547512       -0.215813        -0.168190   \n",
       "670     -0.000460          1.095862        0.704570        -0.024086   \n",
       "671     -0.721736         -0.218837        0.090982        -0.335821   \n",
       "\n",
       "     Amount_on_Fruits  Amount_on_MeatProducts  ...  Cmp3Accepted  \\\n",
       "0           -0.229848               -0.215887  ...     -0.280008   \n",
       "1           -0.659651               -0.610152  ...     -0.280008   \n",
       "2           -0.482674               -0.674434  ...     -0.280008   \n",
       "3            0.933148                3.332499  ...     -0.280008   \n",
       "4           -0.179283                0.054099  ...      3.571328   \n",
       "..                ...                     ...  ...           ...   \n",
       "667          1.362950                0.208376  ...     -0.280008   \n",
       "668         -0.659651                0.349798  ...     -0.280008   \n",
       "669         -0.659651               -0.670149  ...     -0.280008   \n",
       "670          0.832017                0.036957  ...     -0.280008   \n",
       "671         -0.609086               -0.640151  ...     -0.280008   \n",
       "\n",
       "     Cmp4Accepted  Cmp5Accepted  Cmp1Accepted  Cmp2Accepted  Any_Complain  \\\n",
       "0       -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "1        3.571328     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "2       -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "3       -0.280008      3.382964      3.811135     -0.113666     -0.091434   \n",
       "4       -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "667     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "668     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "669     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "670     -0.280008     -0.295599     -0.262389     -0.113666     10.936882   \n",
       "671     -0.280008     -0.295599     -0.262389     -0.113666     -0.091434   \n",
       "\n",
       "     Disposable_Income_Log  Marital_Status  Education_Level  Year_Customer  \n",
       "0                -0.269463        0.215805         0.143392       0.129808  \n",
       "1                 0.061724        0.280000         0.143392       0.266289  \n",
       "2                -2.322765        0.126829         0.026316       0.129808  \n",
       "3                 0.982266        0.100244         0.143392       0.129808  \n",
       "4                 0.665078        0.100244         0.143392       0.129808  \n",
       "..                     ...             ...              ...            ...  \n",
       "667               0.731525        0.126829         0.143392       0.266289  \n",
       "668               0.805608        0.201258         0.143392       0.129808  \n",
       "669               0.270801        0.126829         0.208824       0.129808  \n",
       "670               0.434478        0.100244         0.143392       0.129808  \n",
       "671               0.038567        0.126829         0.154472       0.129808  \n",
       "\n",
       "[672 rows x 24 columns]"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test=Numerical_Data_Test\n",
    "X_test['Marital_Status']=Categorical_Data_Test['Marital_Status']\n",
    "X_test['Education_Level']=Categorical_Data_Test['Education_Level']\n",
    "X_test['Year_Customer']=Categorical_Data_Test['Year_Customer']\n",
    "\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "Prediction=LogisticModel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "identity=df_test['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "Prediction=pd.DataFrame(Prediction,identity)\n",
    "Prediction.columns=['Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "Prediction.to_csv(\"Prediction1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "HyperPrediction=LogisticModel1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "identity=df_test['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "HyperPrediction=pd.DataFrame(HyperPrediction,identity)\n",
    "HyperPrediction.columns=['Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "HyperPrediction.to_csv(\"HyperPrediction1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBCPrediction=CBC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "identity=df_test['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBCPrediction=pd.DataFrame(HyperPrediction,identity)\n",
    "CBCPrediction.columns=['Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "CBCPrediction.to_csv(\"CBCPrediction1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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