EDA-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Black Friday Dataset\n",
    "\n",
    "### EDA and Feature Engineering\n",
    "\n",
    "### cleaning and preparing the data for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.\n",
    "\n",
    "Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN      8370  \n",
       "1                 6.0                14.0     15200  \n",
       "2                 NaN                 NaN      1422  \n",
       "3                14.0                 NaN      1057  \n",
       "4                 NaN                 NaN      7969  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
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       "      <th>0</th>\n",
       "      <td>1000004</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1000009</td>\n",
       "      <td>P00113442</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>17</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00288442</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
       "      <td>P00145342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000011</td>\n",
       "      <td>P00053842</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "0  1000004  P00128942      M  46-50           7             B   \n",
       "1  1000009  P00113442      M  26-35          17             C   \n",
       "2  1000010  P00288442      F  36-45           1             B   \n",
       "3  1000010  P00145342      F  36-45           1             B   \n",
       "4  1000011  P00053842      F  26-35           1             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               1                   1   \n",
       "1                          0               0                   3   \n",
       "2                         4+               1                   5   \n",
       "3                         4+               1                   4   \n",
       "4                          1               0                   4   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  \n",
       "0                11.0                 NaN  \n",
       "1                 5.0                 NaN  \n",
       "2                14.0                 NaN  \n",
       "3                 9.0                 NaN  \n",
       "4                 5.0                12.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append Both Train And Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
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       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <td>A</td>\n",
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       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = df_train.append(df_test)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     783667 non-null  int64  \n",
      " 1   Product_ID                  783667 non-null  object \n",
      " 2   Gender                      783667 non-null  object \n",
      " 3   Age                         783667 non-null  object \n",
      " 4   Occupation                  783667 non-null  int64  \n",
      " 5   City_Category               783667 non-null  object \n",
      " 6   Stay_In_Current_City_Years  783667 non-null  object \n",
      " 7   Marital_Status              783667 non-null  int64  \n",
      " 8   Product_Category_1          783667 non-null  int64  \n",
      " 9   Product_Category_2          537685 non-null  float64\n",
      " 10  Product_Category_3          237858 non-null  float64\n",
      " 11  Purchase                    550068 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 77.7+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Product_Category_1</th>\n",
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       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.836670e+05</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>783667.000000</td>\n",
       "      <td>537685.000000</td>\n",
       "      <td>237858.000000</td>\n",
       "      <td>550068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.079300</td>\n",
       "      <td>0.409777</td>\n",
       "      <td>5.366196</td>\n",
       "      <td>9.844506</td>\n",
       "      <td>12.668605</td>\n",
       "      <td>9263.968713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.727267e+03</td>\n",
       "      <td>6.522206</td>\n",
       "      <td>0.491793</td>\n",
       "      <td>3.878160</td>\n",
       "      <td>5.089093</td>\n",
       "      <td>4.125510</td>\n",
       "      <td>5023.065394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001519e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003075e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004478e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12054.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23961.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  7.836670e+05  783667.000000   783667.000000       783667.000000   \n",
       "mean   1.003029e+06       8.079300        0.409777            5.366196   \n",
       "std    1.727267e+03       6.522206        0.491793            3.878160   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001519e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003075e+06       7.000000        0.000000            5.000000   \n",
       "75%    1.004478e+06      14.000000        1.000000            8.000000   \n",
       "max    1.006040e+06      20.000000        1.000000           20.000000   \n",
       "\n",
       "       Product_Category_2  Product_Category_3       Purchase  \n",
       "count       537685.000000       237858.000000  550068.000000  \n",
       "mean             9.844506           12.668605    9263.968713  \n",
       "std              5.089093            4.125510    5023.065394  \n",
       "min              2.000000            3.000000      12.000000  \n",
       "25%              5.000000            9.000000    5823.000000  \n",
       "50%              9.000000           14.000000    8047.000000  \n",
       "75%             15.000000           16.000000   12054.000000  \n",
       "max             18.000000           18.000000   23961.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(['User_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
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       "      <th>Marital_Status</th>\n",
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       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
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      "text/plain": [
       "  Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042      F  0-17          10             A   \n",
       "1  P00248942      F  0-17          10             A   \n",
       "2  P00087842      F  0-17          10             A   \n",
       "3  P00085442      F  0-17          10             A   \n",
       "4  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Feature Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Occupation</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
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      ],
      "text/plain": [
       "  Product_ID  Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042       0  0-17          10             A   \n",
       "1  P00248942       0  0-17          10             A   \n",
       "2  P00087842       0  0-17          10             A   \n",
       "3  P00085442       0  0-17          10             A   \n",
       "4  P00285442       1   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Gender'] = final_df['Gender'].map({'F':0,'M':1})\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Feature Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Age'] = final_df['Age'].map({'0-17':1,'18-25':2,'26-35':3,'36-45':4,'46-50':5,'51-55':6,'55+':7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
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       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0  P00069042       0    1          10             A   \n",
       "1  P00248942       0    1          10             A   \n",
       "2  P00087842       0    1          10             A   \n",
       "3  P00085442       0    1          10             A   \n",
       "4  P00285442       1    7          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Categorical Feature City_Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_city=pd.get_dummies(final_df['City_Category'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   B  C\n",
       "0  0  0\n",
       "1  0  0\n",
       "2  0  0\n",
       "3  0  0\n",
       "4  0  1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>A</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0  P00069042       0    1          10             A   \n",
       "1  P00248942       0    1          10             A   \n",
       "2  P00087842       0    1          10             A   \n",
       "3  P00085442       0    1          10             A   \n",
       "4  P00285442       1    7          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  B  C  \n",
       "0                 NaN                 NaN    8370.0  0  0  \n",
       "1                 6.0                14.0   15200.0  0  0  \n",
       "2                 NaN                 NaN    1422.0  0  0  \n",
       "3                14.0                 NaN    1057.0  0  0  \n",
       "4                 NaN                 NaN    7969.0  0  1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.concat([final_df,df_city],axis=1)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop City Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop(['City_Category'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4+   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 NaN                 NaN   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 NaN                 NaN   \n",
       "3               0                  12                14.0                 NaN   \n",
       "4               0                   8                 NaN                 NaN   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            245982\n",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Missing Values For Product Category 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0     91317\n",
       "14.0    78834\n",
       "2.0     70498\n",
       "16.0    61687\n",
       "15.0    54114\n",
       "5.0     37165\n",
       "4.0     36705\n",
       "6.0     23575\n",
       "11.0    20230\n",
       "17.0    19104\n",
       "13.0    15054\n",
       "9.0      8177\n",
       "12.0     7801\n",
       "10.0     4420\n",
       "3.0      4123\n",
       "18.0     4027\n",
       "7.0       854\n",
       "Name: Product_Category_2, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Product_Category_2'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Best Way to Replace The Missing Value Can Be Done by Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Product_Category_2'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Product_Category_2'] = final_df['Product_Category_2'].fillna(final_df['Product_Category_2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
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       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4+   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                 NaN   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                 NaN   \n",
       "3               0                  12                14.0                 NaN   \n",
       "4               0                   8                 8.0                 NaN   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Product_Category_2'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Missing Values For Product Category 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Product_Category_3']  = final_df['Product_Category_3'].fillna(final_df['Product_Category_3'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4+   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Product_Category_3'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stay in Current City Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4+', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Stay_In_Current_City_Years'] =final_df['Stay_In_Current_City_Years'].str.replace('+',' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4    \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Product_ID                  783667 non-null  object \n",
      " 1   Gender                      783667 non-null  int64  \n",
      " 2   Age                         783667 non-null  int64  \n",
      " 3   Occupation                  783667 non-null  int64  \n",
      " 4   Stay_In_Current_City_Years  783667 non-null  object \n",
      " 5   Marital_Status              783667 non-null  int64  \n",
      " 6   Product_Category_1          783667 non-null  int64  \n",
      " 7   Product_Category_2          783667 non-null  float64\n",
      " 8   Product_Category_3          783667 non-null  float64\n",
      " 9   Purchase                    550068 non-null  float64\n",
      " 10  B                           783667 non-null  uint8  \n",
      " 11  C                           783667 non-null  uint8  \n",
      "dtypes: float64(3), int64(5), object(2), uint8(2)\n",
      "memory usage: 67.3+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Object Into Integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Stay_In_Current_City_Years'] = final_df['Stay_In_Current_City_Years'].astype(int)\n",
    "final_df['Stay_In_Current_City_Years'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert Unsigned Integer (0,255) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['B'] = final_df['B'].astype(int)\n",
    "final_df['C'] = final_df['C'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Product_ID                  783667 non-null  object \n",
      " 1   Gender                      783667 non-null  int64  \n",
      " 2   Age                         783667 non-null  int64  \n",
      " 3   Occupation                  783667 non-null  int64  \n",
      " 4   Stay_In_Current_City_Years  783667 non-null  int32  \n",
      " 5   Marital_Status              783667 non-null  int64  \n",
      " 6   Product_Category_1          783667 non-null  int64  \n",
      " 7   Product_Category_2          783667 non-null  float64\n",
      " 8   Product_Category_3          783667 non-null  float64\n",
      " 9   Purchase                    550068 non-null  float64\n",
      " 10  B                           783667 non-null  int32  \n",
      " 11  C                           783667 non-null  int32  \n",
      "dtypes: float64(3), int32(3), int64(5), object(1)\n",
      "memory usage: 68.8+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5802b1b08>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Visualization OF AGE VS PURCHASE\n",
    "sns.barplot('Age','Purchase',hue='Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation :\n",
    "    1. purchasing of men is higher than woman in all the age groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization with purchases and occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5fe372b48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Occupation','Purchase',hue='Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of Product_category_1 and Purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5945f4cc8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Product_Category_1','Purchase',hue='Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of Product_category_2 and Purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a596170988>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Product_Category_2','Purchase',hue='Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of Product_category_3 and Purchases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a599f98dc8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('Product_Category_3','Purchase',hue='Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation  Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                           2   \n",
       "1  P00248942       0    1          10                           2   \n",
       "2  P00087842       0    1          10                           2   \n",
       "3  P00085442       0    1          10                           2   \n",
       "4  P00285442       1    7          16                           4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = final_df[final_df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = final_df[~final_df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sklearn Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train.drop('Purchase',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation  Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                           2   \n",
       "1  P00248942       0    1          10                           2   \n",
       "2  P00087842       0    1          10                           2   \n",
       "3  P00085442       0    1          10                           2   \n",
       "4  P00285442       1    7          16                           4   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   B  C  \n",
       "0  0  0  \n",
       "1  0  0  \n",
       "2  0  0  \n",
       "3  0  0  \n",
       "4  0  1  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = df_train['Purchase']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     8370.0\n",
       "1    15200.0\n",
       "2     1422.0\n",
       "3     1057.0\n",
       "4     7969.0\n",
       "Name: Purchase, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,Y,test_size=0.33,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\siddu\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3997: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "x_train.drop('Product_ID',axis=1,inplace = True)\n",
    "x_test.drop('Product_ID',axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Feature Scaling\n",
    "from sklearn import preprocessing\n",
    "sc = preprocessing.StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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