black friday Eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Black Friday dataset EDA and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    " #imprting dataset\n",
    " df_train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "    <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550068, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000010</td>\n",
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       "      <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",
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      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233599, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhij\\AppData\\Local\\Temp\\ipykernel_600\\3989545912.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df = df_train.append(df_test)\n"
     ]
    }
   ],
   "source": [
    "#merge train and test\n",
    "df = df_train.append(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the features which are not valuable\n",
    "df.drop([\"User_ID\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <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>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>8370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>15200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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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",
       "      <th>Stay_In_Current_City_Years</th>\n",
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       "      <td>0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#handling categorical features\n",
    "df[\"Gender\"] = df[\"Gender\"].map({'F':0,'M':1})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique categories in age\n",
    "df[\"Age\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#label encoding Age\n",
    "df[\"Age\"] = 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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>16</td>\n",
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       "      <td>4+</td>\n",
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       "      <td>8</td>\n",
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      "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_city=pd.get_dummies(df[\"City_Category\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,df_city],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1422.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</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  A  B  C  \n",
       "0                 NaN                 NaN    8370.0  1  0  0  \n",
       "1                 6.0                14.0   15200.0  1  0  0  \n",
       "2                 NaN                 NaN    1422.0  1  0  0  \n",
       "3                14.0                 NaN    1057.0  1  0  0  \n",
       "4                 NaN                 NaN    7969.0  0  0  1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"City_Category\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "A                                  0\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  6., 14.,  2.,  8., 15., 16., 11.,  5.,  3.,  4., 12.,  9.,\n",
       "       10., 17., 13.,  7., 18.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Product_Category_2\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Product_Category_2\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace missing values with mode\n",
    "df[\"Product_Category_2\"] = df[\"Product_Category_2\"].fillna(df[\"Product_Category_2\"].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Product_Category_2\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Product_Category_3\"] = df[\"Product_Category_2\"].fillna(df[\"Product_Category_2\"].mode([0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Product_Category_2\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhij\\AppData\\Local\\Temp\\ipykernel_600\\3348642671.py:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  df[\"Stay_In_Current_City_Years\"] =df[\"Stay_In_Current_City_Years\"].str.replace('+','')\n"
     ]
    }
   ],
   "source": [
    "df[\"Stay_In_Current_City_Years\"] =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>A</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>8.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>1</td>\n",
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       "      <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>6.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>1</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>8.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>1</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>14.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>1</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>8.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>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                 8.0   \n",
       "1               0                   1                 6.0                 6.0   \n",
       "2               0                  12                 8.0                 8.0   \n",
       "3               0                  12                14.0                14.0   \n",
       "4               0                   8                 8.0                 8.0   \n",
       "\n",
       "   Purchase  A  B  C  \n",
       "0    8370.0  1  0  0  \n",
       "1   15200.0  1  0  0  \n",
       "2    1422.0  1  0  0  \n",
       "3    1057.0  1  0  0  \n",
       "4    7969.0  0  0  1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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 13 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  A                           783667 non-null  uint8  \n",
      " 11  B                           783667 non-null  uint8  \n",
      " 12  C                           783667 non-null  uint8  \n",
      "dtypes: float64(3), int64(5), object(2), uint8(3)\n",
      "memory usage: 68.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert object into integer\n",
    "df[\"Stay_In_Current_City_Years\"] = df[\"Stay_In_Current_City_Years\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 13 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  A                           783667 non-null  uint8  \n",
      " 11  B                           783667 non-null  uint8  \n",
      " 12  C                           783667 non-null  uint8  \n",
      "dtypes: float64(3), int32(1), int64(5), object(1), uint8(3)\n",
      "memory usage: 65.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"A\"] = df[\"A\"].astype(int)\n",
    "df[\"B\"] = df[\"B\"].astype(int)\n",
    "df[\"C\"] = df[\"C\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Purchase'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization\n",
    "sns.barplot(\"Age\",\"Purchase\",hue=\"Gender\",data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "purchasing of men  is high than women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Occupation', ylabel='Purchase'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of occupation vs purchase\n",
    "sns.barplot(\"Occupation\",\"Purchase\",hue=\"Gender\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Product_ID',\n",
       " 'Gender',\n",
       " 'Age',\n",
       " 'Occupation',\n",
       " 'Stay_In_Current_City_Years',\n",
       " 'Marital_Status',\n",
       " 'Product_Category_1',\n",
       " 'Product_Category_2',\n",
       " 'Product_Category_3',\n",
       " 'Purchase',\n",
       " 'A',\n",
       " 'B',\n",
       " 'C']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_1', ylabel='Purchase'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of occupation vs purchase\n",
    "sns.barplot(\"Product_Category_1\",\"Purchase\",hue=\"Gender\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_2', ylabel='Purchase'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of occupation vs purchase\n",
    "sns.barplot(\"Product_Category_2\",\"Purchase\",hue=\"Gender\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_3', ylabel='Purchase'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of occupation vs purchase\n",
    "sns.barplot(\"Product_Category_3\",\"Purchase\",hue=\"Gender\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "df_test = df[df[\"Purchase\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df[~df[\"Purchase\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_train.drop(\"Purchase\",axis=1)\n",
    "y=df_train[\"Purchase\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state=42,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\abhij\\anaconda3\\lib\\site-packages\\sklearn\\base.py:443: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>A</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>8.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>1</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>6.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>1</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>8.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>1</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>14.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>1</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>8.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>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                 8.0   \n",
       "1               0                   1                 6.0                 6.0   \n",
       "2               0                  12                 8.0                 8.0   \n",
       "3               0                  12                14.0                14.0   \n",
       "4               0                   8                 8.0                 8.0   \n",
       "\n",
       "   Purchase  A  B  C  \n",
       "0    8370.0  1  0  0  \n",
       "1   15200.0  1  0  0  \n",
       "2    1422.0  1  0  0  \n",
       "3    1057.0  1  0  0  \n",
       "4    7969.0  0  0  1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model building\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prediction\n",
    "predict = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.353678181336925e+30\n"
     ]
    }
   ],
   "source": [
    "#valueation\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "mse=mean_squared_error(y_test,predict)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 9260.57510712206\n",
      "Coefficients: [ 2.14327938e+02  1.61680152e+02  4.07985015e+01  1.16427112e+01\n",
      " -3.12722158e+01 -1.68075187e+03 -5.84948464e+13  5.84948464e+13\n",
      "  2.55154767e+15  2.84102487e+15  2.66622702e+15]\n"
     ]
    }
   ],
   "source": [
    "print('Intercept:', model.intercept_)\n",
    "print('Coefficients:', model.coef_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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