Untitled.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "4ea04285-7d0d-4d10-be11-7152340381c4",
   "metadata": {},
   "source": [
    "## Black Friday Data Set EDA and Feature Engineering \n",
    "### Cleaning  and preparing the data for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "f629c459-a2b2-4b2f-b7b9-f6f709816a19",
   "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": "markdown",
   "id": "9c610dad-5b44-4e1c-9a92-fc4b1a10d54f",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.\n",
    "\n",
    "Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "44247a10-f5e2-44d7-91dd-48a71824a59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## importing the data frames\n",
    "df_train = pd.read_csv(\"blackfriday_train.csv\")\n",
    "df_test = pd.read_csv(\"blackfriday_test.csv\")                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "917e4d62-6439-4cc4-991b-70f81df32944",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <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": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "52fbf315-50f7-4539-a1f8-83d343ea55d4",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
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       "      <th>3</th>\n",
       "      <td>1000010</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>4</td>\n",
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       "      <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": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "60fe7b55-15fe-4e2c-98ba-13b61faa030d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
      "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
      "       'Product_Category_2', 'Product_Category_3', 'Purchase'],\n",
      "      dtype='object')\n",
      "Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',\n",
      "       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',\n",
      "       'Product_Category_2', 'Product_Category_3'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_train.columns)\n",
    "print(df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "7646d95d-1d9b-4fe3-850d-8190de199a29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 550068 entries, 0 to 550067\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     550068 non-null  int64  \n",
      " 1   Product_ID                  550068 non-null  object \n",
      " 2   Gender                      550068 non-null  object \n",
      " 3   Age                         550068 non-null  object \n",
      " 4   Occupation                  550068 non-null  int64  \n",
      " 5   City_Category               550068 non-null  object \n",
      " 6   Stay_In_Current_City_Years  550068 non-null  object \n",
      " 7   Marital_Status              550068 non-null  int64  \n",
      " 8   Product_Category_1          550068 non-null  int64  \n",
      " 9   Product_Category_2          376430 non-null  float64\n",
      " 10  Product_Category_3          166821 non-null  float64\n",
      " 11  Purchase                    550068 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 50.4+ MB\n",
      "<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_train.info()\n",
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "5c991384-1712-41b0-bba3-e3e9e086af9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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            173638\n",
      "Product_Category_3            383247\n",
      "Purchase                           0\n",
      "dtype: int64 Values are missing from 2 features =  347276\n"
     ]
    }
   ],
   "source": [
    "\n",
    "missing = df_train[[\"Product_Category_2\", \"Product_Category_2\"]].isnull().sum()\n",
    "\n",
    "print(df_train.isnull().sum(), \"Values are missing from 2 features = \", missing.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "21efe693-eeb3-43d0-a348-406dbda6a3ea",
   "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": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "493609b1-5bd8-4197-a739-5da85422af83",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Merge both the test and train data set\n",
    "df = pd.concat([df_train, df_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "e0c6aafc-2a34-4441-b525-5798532b0cb0",
   "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",
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       "      <th>Product_Category_1</th>\n",
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       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "    </tr>\n",
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       "      <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>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "d92971e7-aff6-4f31-b611-978f705bce5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": [
    "##Basic\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "ec54cb5d-9160-4bf4-8f97-423170751699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 12)"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "dc502a53-dc2c-4c37-99a9-0579a7b8fcc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Occupation</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>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": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "7498a456-5127-43f6-a6d6-1a216ad84e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Deleting any useless feature\n",
    "df.drop([\"User_ID\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "5c410d2e-9c1a-4fc5-8870-f3407a6acc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <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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       "    </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",
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       "      <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",
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       "      <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",
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       "    </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",
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      ],
      "text/plain": [
       "  Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042      F  0-17          10             A   \n",
       "1  P00248942      F  0-17          10             A   \n",
       "2  P00087842      F  0-17          10             A   \n",
       "3  P00085442      F  0-17          10             A   \n",
       "4  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "78c5d97d-10e2-4318-b99b-385d97148bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(783667, 11)"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "dd28e929-5a28-4516-9afc-9a450b669d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "## coverting Gender categorical type into int by replacing f = 0 and m = 1. easy to make model \n",
    "df['Gender'] = df['Gender'].map({'F' : 0, 'M' : 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "91b47c63-5a9c-45da-bdc3-eddb114a94ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Product_Category_3</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "  Product_ID  Gender   Age  Occupation City_Category  \\\n",
       "0  P00069042       0  0-17          10             A   \n",
       "1  P00248942       0  0-17          10             A   \n",
       "2  P00087842       0  0-17          10             A   \n",
       "3  P00085442       0  0-17          10             A   \n",
       "4  P00285442       1   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN    8370.0  \n",
       "1                 6.0                14.0   15200.0  \n",
       "2                 NaN                 NaN    1422.0  \n",
       "3                14.0                 NaN    1057.0  \n",
       "4                 NaN                 NaN    7969.0  "
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "6fbb156e-5ebc-48ba-a326-386561a4067c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 11 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  object \n",
      " 3   Occupation                  783667 non-null  int64  \n",
      " 4   City_Category               783667 non-null  object \n",
      " 5   Stay_In_Current_City_Years  783667 non-null  object \n",
      " 6   Marital_Status              783667 non-null  int64  \n",
      " 7   Product_Category_1          783667 non-null  int64  \n",
      " 8   Product_Category_2          537685 non-null  float64\n",
      " 9   Product_Category_3          237858 non-null  float64\n",
      " 10  Purchase                    550068 non-null  float64\n",
      "dtypes: float64(3), int64(4), object(4)\n",
      "memory usage: 71.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "db6b6e17-30b6-440a-bb87-fdf79dfb8fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Handle categorical featutre i.e age \n",
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "d12db3c4-e58f-4f46-bf96-dc0e8d8994ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          0-17\n",
       "1          0-17\n",
       "2          0-17\n",
       "3          0-17\n",
       "4           55+\n",
       "          ...  \n",
       "233594    26-35\n",
       "233595    26-35\n",
       "233596    26-35\n",
       "233597    46-50\n",
       "233598    46-50\n",
       "Name: Age, Length: 783667, dtype: object"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Age\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "03f9e991-6a1b-49b3-bba9-3e6c19a56474",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 372,
   "id": "9a71bb2f-411d-4af7-b38d-8a375cf8f862",
   "metadata": {},
   "outputs": [
    {
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       "      <td>8</td>\n",
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       "  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": 372,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "66831cee-6105-4fdb-ad40-d07e5b8367a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 783667 entries, 0 to 233598\n",
      "Series name: City_Category\n",
      "Non-Null Count   Dtype \n",
      "--------------   ----- \n",
      "783667 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 12.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df[\"City_Category\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "7684d569-670b-4394-9c57-100a0aff7005",
   "metadata": {},
   "outputs": [],
   "source": [
    "## fixing categorical feature city\n",
    "df_city = pd.get_dummies(df['City_Category'], drop_first = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "301fcdaa-a9bc-4dc0-8eb1-cd14ad48db7b",
   "metadata": {},
   "outputs": [
    {
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       "       B      C\n",
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  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "3964d5ac-6b5f-4149-952f-827904d4a92c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, df_city], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "983625fd-2747-4504-a465-5ceb6d69619e",
   "metadata": {},
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    {
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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",
       "233594  P00118942       0    3          15             B   \n",
       "233595  P00254642       0    3          15             B   \n",
       "233596  P00031842       0    3          15             B   \n",
       "233597  P00124742       0    5           1             C   \n",
       "233598  P00316642       0    5           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  False  False  \n",
       "1                      6.0                14.0   15200.0  False  False  \n",
       "2                      NaN                 NaN    1422.0  False  False  \n",
       "3                     14.0                 NaN    1057.0  False  False  \n",
       "4                      NaN                 NaN    7969.0  False   True  \n",
       "...                    ...                 ...       ...    ...    ...  \n",
       "233594                 NaN                 NaN       NaN   True  False  \n",
       "233595                 8.0                 NaN       NaN   True  False  \n",
       "233596                 5.0                12.0       NaN   True  False  \n",
       "233597                16.0                 NaN       NaN  False   True  \n",
       "233598                 5.0                 NaN       NaN   True  False  \n",
       "\n",
       "[783667 rows x 13 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "e00b0eb6-f207-452d-82cb-b40f4544fde1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('City_Category', axis  = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "b6dfb83c-e349-4771-ac93-62dde65ae231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370.0</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4+   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 NaN                 NaN   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 NaN                 NaN   \n",
       "3               0                  12                14.0                 NaN   \n",
       "4               0                   8                 NaN                 NaN   \n",
       "\n",
       "   Purchase      B      C  \n",
       "0    8370.0  False  False  \n",
       "1   15200.0  False  False  \n",
       "2    1422.0  False  False  \n",
       "3    1057.0  False  False  \n",
       "4    7969.0  False   True  "
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "dd0a0e82-97b2-4dd3-ab47-7acd515d2b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"B\"] = df[\"B\"].map({False: 0, True: 1})\n",
    "df['C'] = df['C'].map({False: 0, True: 1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "6fdadf34-c511-4fa5-bf0e-6b329750c12b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>14.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
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       "      <td>16</td>\n",
       "      <td>4+</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233594</th>\n",
       "      <td>P00118942</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233595</th>\n",
       "      <td>P00254642</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</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>3</td>\n",
       "      <td>15</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>5</td>\n",
       "      <td>1</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>5</td>\n",
       "      <td>0</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 × 12 columns</p>\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",
       "233594  P00118942       0    3          15                         4+   \n",
       "233595  P00254642       0    3          15                         4+   \n",
       "233596  P00031842       0    3          15                         4+   \n",
       "233597  P00124742       0    5           1                         4+   \n",
       "233598  P00316642       0    5           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": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "bd504a00-6268-402a-9bc9-cf570235a164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_ID                         0\n",
       "Gender                             0\n",
       "Age                                0\n",
       "Occupation                         0\n",
       "Stay_In_Current_City_Years         0\n",
       "Marital_Status                     0\n",
       "Product_Category_1                 0\n",
       "Product_Category_2            245982\n",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Missing Values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "a4e5748f-c909-4b14-bc19-1af913787e0f",
   "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": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Focus on replacing the missing values \n",
    "df['Product_Category_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "e5b75f22-8c9a-442b-83c1-28be9ec837f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product_Category_2\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "e32cf12c-42bd-463c-a8c2-a64720a5c8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Replace the missing value with mode "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "2cdfa762-c5e2-44b5-bd7c-ca34bb3f03b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "3feb980c-1736-4758-b2a9-3f57b81d99ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Product_Category_2'] = df['Product_Category_2'].fillna(df['Product_Category_2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "b73ddbc9-78b7-4fa4-a98d-1d4f384ceeb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.,  6., 14.,  2., 15., 16., 11.,  5.,  3.,  4., 12.,  9., 10.,\n",
       "       17., 13.,  7., 18.])"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Product_Category_2\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "8dd8a2cc-6660-4e9d-a494-5adacdaac088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "72cc63bb-30b8-409a-bc5a-2e2609e3ded1",
   "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                 0\n",
       "Product_Category_3            545809\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "9217b92d-46f9-4660-a424-86fde2644601",
   "metadata": {},
   "outputs": [],
   "source": [
    "## For Product category 3 \n",
    "df['Product_Category_3'] = df['Product_Category_3'].fillna(df['Product_Category_3'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "ff2f28fb-9228-4e00-b088-03f5db24961c",
   "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                 0\n",
       "Product_Category_3                 0\n",
       "Purchase                      233599\n",
       "B                                  0\n",
       "C                                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "d5b9b4e0-8f2a-4ec4-821e-24922c9cd137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P00069042</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8370.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P00248942</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P00087842</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1422.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P00085442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P00285442</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7969.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_ID  Gender  Age  Occupation Stay_In_Current_City_Years  \\\n",
       "0  P00069042       0    1          10                          2   \n",
       "1  P00248942       0    1          10                          2   \n",
       "2  P00087842       0    1          10                          2   \n",
       "3  P00085442       0    1          10                          2   \n",
       "4  P00285442       1    7          16                         4+   \n",
       "\n",
       "   Marital_Status  Product_Category_1  Product_Category_2  Product_Category_3  \\\n",
       "0               0                   3                 8.0                16.0   \n",
       "1               0                   1                 6.0                14.0   \n",
       "2               0                  12                 8.0                16.0   \n",
       "3               0                  12                14.0                16.0   \n",
       "4               0                   8                 8.0                16.0   \n",
       "\n",
       "   Purchase  B  C  \n",
       "0    8370.0  0  0  \n",
       "1   15200.0  0  0  \n",
       "2    1422.0  0  0  \n",
       "3    1057.0  0  0  \n",
       "4    7969.0  0  1  "
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "66fe4cb6-8030-4823-84c1-89a6d80d50dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Stay_In_Current_City_Years'] = df['Stay_In_Current_City_Years'].str.replace('+', ' ' ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "5ec55fe8-9553-455d-b88e-bd5caa60b091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4 ', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "18f4f579-6c89-404f-9f2e-c794db1f62fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 783667 entries, 0 to 233598\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   Product_ID                  783667 non-null  object \n",
      " 1   Gender                      783667 non-null  int64  \n",
      " 2   Age                         783667 non-null  int64  \n",
      " 3   Occupation                  783667 non-null  int64  \n",
      " 4   Stay_In_Current_City_Years  783667 non-null  int64  \n",
      " 5   Marital_Status              783667 non-null  int64  \n",
      " 6   Product_Category_1          783667 non-null  int64  \n",
      " 7   Product_Category_2          783667 non-null  float64\n",
      " 8   Product_Category_3          783667 non-null  float64\n",
      " 9   Purchase                    550068 non-null  float64\n",
      " 10  B                           783667 non-null  int64  \n",
      " 11  C                           783667 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 77.7+ MB\n"
     ]
    }
   ],
   "source": [
    "## converting object into integer \n",
    "df['Stay_In_Current_City_Years'] = df['Stay_In_Current_City_Years'].astype(int)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "0ce9b194-fb76-4126-8c69-be4f7b78d98f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Purchase'>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualisation \n",
    " ## categories  = df['Age'].unique()\n",
    "sns.barplot(x = 'Age' , y = 'Purchase', data = df, hue = 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4fbf79-521e-4b8d-8bb3-8945e139ef49",
   "metadata": {},
   "source": [
    "## Purchasing of Men is greater than Women "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "add90ebf-07d4-4e02-824a-26e87ec958b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Purchase'>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualisation of Purchase with respect to occupation \n",
    "sns.barplot(x = 'Occupation', y = 'Purchase', data = df, hue = 'Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39839798-1fc7-4d32-baf3-45a23a1f32dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e538426e-1254-4c13-933b-3db3c64d3278",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e163eb85-a6aa-43a6-863d-c51da67eee2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e07094e7-8b92-49ad-b55d-58adf56e76d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}