EDA_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19a92a89",
   "metadata": {},
   "source": [
    "### PROBLEM STMT:\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.\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a58099d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d6c0976",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test= pd.read_csv('test.csv')\n",
    "df_train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69707230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 233599 entries, 0 to 233598\n",
      "Data columns (total 11 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     233599 non-null  int64  \n",
      " 1   Product_ID                  233599 non-null  object \n",
      " 2   Gender                      233599 non-null  object \n",
      " 3   Age                         233599 non-null  object \n",
      " 4   Occupation                  233599 non-null  int64  \n",
      " 5   City_Category               233599 non-null  object \n",
      " 6   Stay_In_Current_City_Years  233599 non-null  object \n",
      " 7   Marital_Status              233599 non-null  int64  \n",
      " 8   Product_Category_1          233599 non-null  int64  \n",
      " 9   Product_Category_2          161255 non-null  float64\n",
      " 10  Product_Category_3          71037 non-null   float64\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 19.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a154853",
   "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",
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       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
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       "  </thead>\n",
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       "    <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",
       "      <td>2</td>\n",
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       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "96d4db75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                            0\n",
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "City_Category                      0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2             72344\n",
       "Product_Category_3            162562\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15684390",
   "metadata": {},
   "source": [
    "# merge both  the data sets \n",
    "\n",
    "df_final = pd.merge(df_train,df_test,how = 'left')\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c5b1a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_6428\\2927829381.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  final_df = df_train.append(df_test)\n"
     ]
    },
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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",
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       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
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       "      <td>1000001</td>\n",
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       "      <td>F</td>\n",
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       "      <td>1000001</td>\n",
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       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
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       "      <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",
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       "      <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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      ],
      "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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = df_train.append(df_test)\n",
    "final_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c7652d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64dbee5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see which feature not much important for our analysis \n",
    "\n",
    "final_df.drop(['User_ID'],axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "118db5ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "      <td>M</td>\n",
       "      <td>55+</td>\n",
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       "      <td>C</td>\n",
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       "      <td>M</td>\n",
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       "  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",
       "5  P00193542      M  26-35          15             A   \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",
       "5                          3               0                   1   \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  \n",
       "5                 2.0                 NaN   15227.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3635fd6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#let see here Gender are in categorical type so to convert into a way so model can take this value ,\n",
    "#'see how we can do this '\n",
    "\n",
    "final_df['Gender'] = final_df['Gender'].map({'F': 0,'M': 1})       \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93787c73",
   "metadata": {},
   "outputs": [
    {
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       "      <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>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>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>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>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>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>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>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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13a97575",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(final_df['City_Category'],drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71d92e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now murge df to original dataframe\n",
    "final_df = pd.concat([final_df,df],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe19cf3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>783667 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "       Product_ID  Gender    Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0       P00069042       0   0-17          10                          2   \n",
       "1       P00248942       0   0-17          10                          2   \n",
       "2       P00087842       0   0-17          10                          2   \n",
       "3       P00085442       0   0-17          10                          2   \n",
       "4       P00285442       1    55+          16                         4+   \n",
       "...           ...     ...    ...         ...                        ...   \n",
       "233594  P00118942       0  26-35          15                         4+   \n",
       "233595  P00254642       0  26-35          15                         4+   \n",
       "233596  P00031842       0  26-35          15                         4+   \n",
       "233597  P00124742       0  46-50           1                         4+   \n",
       "233598  P00316642       0  46-50           0                         4+   \n",
       "\n",
       "        Marital_Status  Product_Category_1  Product_Category_2  \\\n",
       "0                    0                   3                 NaN   \n",
       "1                    0                   1                 6.0   \n",
       "2                    0                  12                 NaN   \n",
       "3                    0                  12                14.0   \n",
       "4                    0                   8                 NaN   \n",
       "...                ...                 ...                 ...   \n",
       "233594               1                   8                 NaN   \n",
       "233595               1                   5                 8.0   \n",
       "233596               1                   1                 5.0   \n",
       "233597               0                  10                16.0   \n",
       "233598               1                   4                 5.0   \n",
       "\n",
       "        Product_Category_3  Purchase  B  C  \n",
       "0                      NaN    8370.0  0  0  \n",
       "1                     14.0   15200.0  0  0  \n",
       "2                      NaN    1422.0  0  0  \n",
       "3                      NaN    1057.0  0  0  \n",
       "4                      NaN    7969.0  0  1  \n",
       "...                    ...       ... .. ..  \n",
       "233594                 NaN       NaN  1  0  \n",
       "233595                 NaN       NaN  1  0  \n",
       "233596                12.0       NaN  1  0  \n",
       "233597                 NaN       NaN  0  1  \n",
       "233598                 NaN       NaN  1  0  \n",
       "\n",
       "[783667 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.drop(['City_Category'],axis=1\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67593c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "69d639c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Age']=final_df['Age'].map({'0-17':1,\n",
    "                                     '18-25':2,\n",
    "                                     '26-35':3,\n",
    "                                     '36-45':4,\n",
    "                                     '46-50':5,\n",
    "                                     '51-55':6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "108935a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1.0\n",
       "1         1.0\n",
       "2         1.0\n",
       "3         1.0\n",
       "4         NaN\n",
       "         ... \n",
       "233594    3.0\n",
       "233595    3.0\n",
       "233596    3.0\n",
       "233597    5.0\n",
       "233598    5.0\n",
       "Name: Age, Length: 783667, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "77fc4ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#look at age feature so change it to app\n",
    "\n",
    "final_df['Age'] = final_df['Age'].map({'0-17':1,\n",
    "                                       '18-25':2,'26-35':3,'36-45':4,'46-50':5,'51-55':6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9356f82c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>P00254642</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233596</th>\n",
       "      <td>P00031842</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233597</th>\n",
       "      <td>P00124742</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233598</th>\n",
       "      <td>P00316642</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>783667 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0       P00069042       0  NaN          10             A   \n",
       "1       P00248942       0  NaN          10             A   \n",
       "2       P00087842       0  NaN          10             A   \n",
       "3       P00085442       0  NaN          10             A   \n",
       "4       P00285442       1  NaN          16             C   \n",
       "...           ...     ...  ...         ...           ...   \n",
       "233594  P00118942       0  NaN          15             B   \n",
       "233595  P00254642       0  NaN          15             B   \n",
       "233596  P00031842       0  NaN          15             B   \n",
       "233597  P00124742       0  NaN           1             C   \n",
       "233598  P00316642       0  NaN           0             B   \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",
       "233594                         4+               1                   8   \n",
       "233595                         4+               1                   5   \n",
       "233596                         4+               1                   1   \n",
       "233597                         4+               0                  10   \n",
       "233598                         4+               1                   4   \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  \n",
       "...                    ...                 ...       ... .. ..  \n",
       "233594                 NaN                 NaN       NaN  1  0  \n",
       "233595                 8.0                 NaN       NaN  1  0  \n",
       "233596                 5.0                12.0       NaN  1  0  \n",
       "233597                16.0                 NaN       NaN  0  1  \n",
       "233598                 5.0                 NaN       NaN  1  0  \n",
       "\n",
       "[783667 rows x 13 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8f187705",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['B']=df['B'].astype(int)\n",
    "final_df['C']=df['C'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82985e93",
   "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": "code",
   "execution_count": 21,
   "id": "c487c519",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_p2 = final_df['Product_Category_2'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b609ac53",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nan value in product_category_2 is filled with its mode \n",
    "final_df['Product_Category_2'] = final_df['Product_Category_2'].fillna(mode_p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9340dc83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0    46469\n",
       "15.0    39968\n",
       "14.0    26283\n",
       "17.0    23818\n",
       "5.0     23799\n",
       "8.0     17861\n",
       "9.0     16532\n",
       "12.0    13115\n",
       "13.0     7849\n",
       "6.0      6888\n",
       "18.0     6621\n",
       "4.0      2691\n",
       "11.0     2585\n",
       "10.0     2501\n",
       "3.0       878\n",
       "Name: Product_Category_3, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Product_Category_3'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd76898c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_p3 = final_df['Product_Category_3'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ece0135",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['Product_Category_3'] = final_df['Product_Category_3'].fillna(mode_p3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1c339a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize']= (12,6)\n",
    "sns.heatmap(final_df.isnull(), yticklabels= False,cbar = False , cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "258ae4e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4+', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "111cd239",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_6428\\1525632823.py:3: 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",
      "  final_df['Stay_In_Current_City_Years'].str.replace('+','')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1         2\n",
       "2         2\n",
       "3         2\n",
       "4         4\n",
       "         ..\n",
       "233594    4\n",
       "233595    4\n",
       "233596    4\n",
       "233597    4\n",
       "233598    4\n",
       "Name: Stay_In_Current_City_Years, Length: 783667, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change this 4+ to only 4 \n",
    "\n",
    "final_df['Stay_In_Current_City_Years'].str.replace('+','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bfc61270",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 783667 entries, 0 to 233598\n",
      "Series name: Stay_In_Current_City_Years\n",
      "Non-Null Count   Dtype \n",
      "--------------   ----- \n",
      "783667 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 12.0+ MB\n"
     ]
    }
   ],
   "source": [
    "final_df['Stay_In_Current_City_Years'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "76a21aac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: '4+'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [30]\u001b[0m, in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#here data type is object we need to convert this into integer\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m final_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mStay_In_Current_City_Years\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mfinal_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mStay_In_Current_City_Years\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py:5912\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5905\u001b[0m     results \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   5906\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miloc[:, i]\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m   5907\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[0;32m   5908\u001b[0m     ]\n\u001b[0;32m   5910\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   5911\u001b[0m     \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[1;32m-> 5912\u001b[0m     new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   5913\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   5915\u001b[0m \u001b[38;5;66;03m# GH 33113: handle empty frame or series\u001b[39;00m\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\managers.py:419\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    418\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mastype\u001b[39m(\u001b[38;5;28mself\u001b[39m: T, dtype, copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m--> 419\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\managers.py:304\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m    302\u001b[0m         applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    303\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 304\u001b[0m         applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(b, f)(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    305\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mNotImplementedError\u001b[39;00m):\n\u001b[0;32m    306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ignore_failures:\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\blocks.py:580\u001b[0m, in \u001b[0;36mBlock.astype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    562\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    563\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[0;32m    564\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    576\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    578\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[1;32m--> 580\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    582\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[0;32m    583\u001b[0m newb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_block(new_values)\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1292\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   1289\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[0;32m   1291\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1292\u001b[0m     new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1293\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[0;32m   1294\u001b[0m     \u001b[38;5;66;03m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[0;32m   1295\u001b[0m     \u001b[38;5;66;03m#  trying to convert to float\u001b[39;00m\n\u001b[0;32m   1296\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1237\u001b[0m, in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m   1234\u001b[0m     values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[0;32m   1236\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1237\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1239\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[0;32m   1240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1154\u001b[0m, in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m   1150\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_object_dtype(arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m   1151\u001b[0m \n\u001b[0;32m   1152\u001b[0m     \u001b[38;5;66;03m# work around NumPy brokenness, #1987\u001b[39;00m\n\u001b[0;32m   1153\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39missubdtype(dtype\u001b[38;5;241m.\u001b[39mtype, np\u001b[38;5;241m.\u001b[39minteger):\n\u001b[1;32m-> 1154\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype_intsafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1156\u001b[0m     \u001b[38;5;66;03m# if we have a datetime/timedelta array of objects\u001b[39;00m\n\u001b[0;32m   1157\u001b[0m     \u001b[38;5;66;03m# then coerce to a proper dtype and recall astype_nansafe\u001b[39;00m\n\u001b[0;32m   1159\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m is_datetime64_dtype(dtype):\n",
      "File \u001b[1;32mF:\\Anaconda\\lib\\site-packages\\pandas\\_libs\\lib.pyx:668\u001b[0m, in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: '4+'"
     ]
    }
   ],
   "source": [
    "#here data type is object we need to convert this into integer\n",
    "final_df['Stay_In_Current_City_Years'] = final_df['Stay_In_Current_City_Years'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf98198f",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3caf94d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "##data is not ready for visualisation ,, ;ets explore data through visualisation \n",
    "\n",
    "sns.barplot(x='Age',y='Purchase',hue = 'Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9ac0b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_1', ylabel='Purchase'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_1',y='Purchase',hue = 'Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdfa3040",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ff1d3d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_2', ylabel='Purchase'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_2',y='Purchase',hue = 'Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4917a8f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a4bf29e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Category_3', ylabel='Purchase'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Product_Category_3',y='Purchase',hue = 'Gender',data=final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b99f84c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d35ffc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature Scaling \n",
    "\n",
    "df_test = final_df[final_df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1a79b969",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = final_df[~final_df['Purchase'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b72fbee",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       Product_ID  Gender  Age  Occupation City_Category  \\\n",
       "0       P00069042       0  NaN          10             A   \n",
       "1       P00248942       0  NaN          10             A   \n",
       "2       P00087842       0  NaN          10             A   \n",
       "3       P00085442       0  NaN          10             A   \n",
       "4       P00285442       1  NaN          16             C   \n",
       "...           ...     ...  ...         ...           ...   \n",
       "550063  P00372445       1  NaN          13             B   \n",
       "550064  P00375436       0  NaN           1             C   \n",
       "550065  P00375436       0  NaN          15             B   \n",
       "550066  P00375436       0  NaN           1             C   \n",
       "550067  P00371644       0  NaN           0             B   \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",
       "550063                          1               1                  20   \n",
       "550064                          3               0                  20   \n",
       "550065                         4+               1                  20   \n",
       "550066                          2               0                  20   \n",
       "550067                         4+               1                  20   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  Purchase  B  C  \n",
       "0                      8.0                16.0    8370.0  0  0  \n",
       "1                      6.0                14.0   15200.0  0  0  \n",
       "2                      8.0                16.0    1422.0  0  0  \n",
       "3                     14.0                16.0    1057.0  0  0  \n",
       "4                      8.0                16.0    7969.0  0  1  \n",
       "...                    ...                 ...       ... .. ..  \n",
       "550063                 8.0                16.0     368.0  1  0  \n",
       "550064                 8.0                16.0     371.0  0  1  \n",
       "550065                 8.0                16.0     137.0  1  0  \n",
       "550066                 8.0                16.0     365.0  0  1  \n",
       "550067                 8.0                16.0     490.0  1  0  \n",
       "\n",
       "[550068 rows x 13 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e82d1b65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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