black-friday-sales-sayalishevale.ipynb

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     "text": [
      "/kaggle/input/black-friday-sales-eda/train.csv\n"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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       "      <th>Occupation</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</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",
       "4  1000002  P00285442      M    55+          16             C   \n",
       "5  1000003  P00193542      M  26-35          15             A   \n",
       "6  1000004  P00184942      M  46-50           7             B   \n",
       "7  1000004  P00346142      M  46-50           7             B   \n",
       "8  1000004   P0097242      M  46-50           7             B   \n",
       "9  1000005  P00274942      M  26-35          20             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",
       "6                          2               1                   1   \n",
       "7                          2               1                   1   \n",
       "8                          2               1                   1   \n",
       "9                          1               1                   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  \n",
       "5                 2.0                 NaN     15227  \n",
       "6                 8.0                17.0     19215  \n",
       "7                15.0                 NaN     15854  \n",
       "8                16.0                 NaN     15686  \n",
       "9                 NaN                 NaN      7871  "
      ]
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   ],
   "source": [
    "df=pd.read_csv('/kaggle/input/black-friday-sales-eda/train.csv')\n",
    "df.head(10)"
   ]
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    {
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      "text/plain": [
       "(550068, 12)"
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    "df.shape"
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     "data": {
      "text/plain": [
       "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')"
      ]
     },
     "execution_count": 4,
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   "source": [
    "df.columns"
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       "      <th></th>\n",
       "      <th>User_ID</th>\n",
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       "      <th>count</th>\n",
       "      <td>5.500680e+05</td>\n",
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       "      <td>166821.000000</td>\n",
       "      <td>550068.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.003029e+06</td>\n",
       "      <td>8.076707</td>\n",
       "      <td>0.409653</td>\n",
       "      <td>5.404270</td>\n",
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       "      <td>12.668243</td>\n",
       "      <td>9263.968713</td>\n",
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       "      <th>std</th>\n",
       "      <td>1.727592e+03</td>\n",
       "      <td>6.522660</td>\n",
       "      <td>0.491770</td>\n",
       "      <td>3.936211</td>\n",
       "      <td>5.086590</td>\n",
       "      <td>4.125338</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>1.001516e+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",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003077e+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",
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       "    <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",
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      "text/plain": [
       "            User_ID     Occupation  Marital_Status  Product_Category_1  \\\n",
       "count  5.500680e+05  550068.000000   550068.000000       550068.000000   \n",
       "mean   1.003029e+06       8.076707        0.409653            5.404270   \n",
       "std    1.727592e+03       6.522660        0.491770            3.936211   \n",
       "min    1.000001e+06       0.000000        0.000000            1.000000   \n",
       "25%    1.001516e+06       2.000000        0.000000            1.000000   \n",
       "50%    1.003077e+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       376430.000000       166821.000000  550068.000000  \n",
       "mean             9.842329           12.668243    9263.968713  \n",
       "std              5.086590            4.125338    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  "
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    "df.describe()"
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       "<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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550063</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550064</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550065</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550066</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550067</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>550068 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_ID  Product_ID  Gender    Age  Occupation  City_Category  \\\n",
       "0         False       False   False  False       False          False   \n",
       "1         False       False   False  False       False          False   \n",
       "2         False       False   False  False       False          False   \n",
       "3         False       False   False  False       False          False   \n",
       "4         False       False   False  False       False          False   \n",
       "...         ...         ...     ...    ...         ...            ...   \n",
       "550063    False       False   False  False       False          False   \n",
       "550064    False       False   False  False       False          False   \n",
       "550065    False       False   False  False       False          False   \n",
       "550066    False       False   False  False       False          False   \n",
       "550067    False       False   False  False       False          False   \n",
       "\n",
       "        Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                            False           False               False   \n",
       "1                            False           False               False   \n",
       "2                            False           False               False   \n",
       "3                            False           False               False   \n",
       "4                            False           False               False   \n",
       "...                            ...             ...                 ...   \n",
       "550063                       False           False               False   \n",
       "550064                       False           False               False   \n",
       "550065                       False           False               False   \n",
       "550066                       False           False               False   \n",
       "550067                       False           False               False   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  Purchase  \n",
       "0                     True                True     False  \n",
       "1                    False               False     False  \n",
       "2                     True                True     False  \n",
       "3                    False                True     False  \n",
       "4                     True                True     False  \n",
       "...                    ...                 ...       ...  \n",
       "550063                True                True     False  \n",
       "550064                True                True     False  \n",
       "550065                True                True     False  \n",
       "550066                True                True     False  \n",
       "550067                True                True     False  \n",
       "\n",
       "[550068 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07738f94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:03.282919Z",
     "iopub.status.busy": "2024-02-06T17:17:03.282133Z",
     "iopub.status.idle": "2024-02-06T17:17:03.574808Z",
     "shell.execute_reply": "2024-02-06T17:17:03.573602Z"
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     "status": "completed"
    },
    "tags": []
   },
   "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            173638\n",
       "Product_Category_3            383247\n",
       "Purchase                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cebfa958",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:03.613951Z",
     "iopub.status.busy": "2024-02-06T17:17:03.612685Z",
     "iopub.status.idle": "2024-02-06T17:17:03.628367Z",
     "shell.execute_reply": "2024-02-06T17:17:03.627287Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 16, 15,  7, 20,  9,  1, 12, 17,  0,  3,  4, 11,  8, 19,  2, 18,\n",
       "        5, 14, 13,  6])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Occupation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5da5ce3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:03.668371Z",
     "iopub.status.busy": "2024-02-06T17:17:03.667906Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fb152c9",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'C', 'B'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['City_Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e93d442",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:03.856537Z",
     "iopub.status.busy": "2024-02-06T17:17:03.856080Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2', '4+', '3', '1', '0'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Stay_In_Current_City_Years'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0921064f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:03.949399Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Marital_Status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf298662",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:04.070725Z",
     "iopub.status.busy": "2024-02-06T17:17:04.069859Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1, 12,  8,  5,  4,  2,  6, 14, 11, 13, 15,  7, 16, 18, 10, 17,\n",
       "        9, 20, 19])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "51232b6e",
   "metadata": {
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     "iopub.execute_input": "2024-02-06T17:17:04.123800Z",
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     "status": "completed"
    },
    "tags": []
   },
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7512fdd9",
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     "iopub.execute_input": "2024-02-06T17:17:04.181295Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 14., 17.,  5.,  4., 16., 15.,  8.,  9., 13.,  6., 12.,  3.,\n",
       "       18., 11., 10.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Category_3'].unique()"
   ]
  },
  {
   "cell_type": "code",
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   "id": "01c7afb1",
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     "iopub.execute_input": "2024-02-06T17:17:04.239963Z",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8370, 15200,  1422, ...,   135,   123,   613])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Purchase'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5d0fb988",
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   "outputs": [],
   "source": [
    "df.rename(columns={'City_Category':'City','Stay_In_Current_City_Year':'Current_City','Product_Category_1':'Prod_1','Product_Category_2':'Prod_2','Product_Category_3':'Prod_3'},inplace=True)"
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   "source": [
    "df.drop(['User_ID','Product_ID'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f69dc82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:04.414773Z",
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   "outputs": [],
   "source": [
    "df['Gender']=df['Gender'].map({'M':1,'F':0})"
   ]
  },
  {
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   "execution_count": 20,
   "id": "5522c878",
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       "        Gender    Age  Occupation City Stay_In_Current_City_Years  \\\n",
       "0            0   0-17          10    A                          2   \n",
       "1            0   0-17          10    A                          2   \n",
       "2            0   0-17          10    A                          2   \n",
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       "4            1    55+          16    C                         4+   \n",
       "...        ...    ...         ...  ...                        ...   \n",
       "550063       1  51-55          13    B                          1   \n",
       "550064       0  26-35           1    C                          3   \n",
       "550065       0  26-35          15    B                         4+   \n",
       "550066       0    55+           1    C                          2   \n",
       "550067       0  46-50           0    B                         4+   \n",
       "\n",
       "        Marital_Status  Prod_1  Prod_2  Prod_3  Purchase  \n",
       "0                    0       3     NaN     NaN      8370  \n",
       "1                    0       1     6.0    14.0     15200  \n",
       "2                    0      12     NaN     NaN      1422  \n",
       "3                    0      12    14.0     NaN      1057  \n",
       "4                    0       8     NaN     NaN      7969  \n",
       "...                ...     ...     ...     ...       ...  \n",
       "550063               1      20     NaN     NaN       368  \n",
       "550064               0      20     NaN     NaN       371  \n",
       "550065               1      20     NaN     NaN       137  \n",
       "550066               0      20     NaN     NaN       365  \n",
       "550067               1      20     NaN     NaN       490  \n",
       "\n",
       "[550068 rows x 10 columns]"
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     "execution_count": 20,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender                          int64\n",
       "Age                            object\n",
       "Occupation                      int64\n",
       "City                           object\n",
       "Stay_In_Current_City_Years     object\n",
       "Marital_Status                  int64\n",
       "Prod_1                          int64\n",
       "Prod_2                        float64\n",
       "Prod_3                        float64\n",
       "Purchase                        int64\n",
       "dtype: object"
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     "execution_count": 21,
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   "source": [
    "df.dtypes"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-17', '0-17', '0-17', ..., '26-35', '55+', '46-50'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.values"
   ]
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aebeda62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:04.659620Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Age'].replace({'0-17':'child','18-25':'teenage','26-35':' young adults','36-45':'adults','46-50':'mid age','51-55':'old adults','55+':'old'},inplace=True)"
   ]
  },
  {
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   "execution_count": 24,
   "id": "613fb5d1",
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       "        Gender            Age  Occupation City Stay_In_Current_City_Years  \\\n",
       "0            0          child          10    A                          2   \n",
       "1            0          child          10    A                          2   \n",
       "2            0          child          10    A                          2   \n",
       "3            0          child          10    A                          2   \n",
       "4            1            old          16    C                         4+   \n",
       "...        ...            ...         ...  ...                        ...   \n",
       "550063       1     old adults          13    B                          1   \n",
       "550064       0   young adults           1    C                          3   \n",
       "550065       0   young adults          15    B                         4+   \n",
       "550066       0            old           1    C                          2   \n",
       "550067       0        mid age           0    B                         4+   \n",
       "\n",
       "        Marital_Status  Prod_1  Prod_2  Prod_3  Purchase  \n",
       "0                    0       3     NaN     NaN      8370  \n",
       "1                    0       1     6.0    14.0     15200  \n",
       "2                    0      12     NaN     NaN      1422  \n",
       "3                    0      12    14.0     NaN      1057  \n",
       "4                    0       8     NaN     NaN      7969  \n",
       "...                ...     ...     ...     ...       ...  \n",
       "550063               1      20     NaN     NaN       368  \n",
       "550064               0      20     NaN     NaN       371  \n",
       "550065               1      20     NaN     NaN       137  \n",
       "550066               0      20     NaN     NaN       365  \n",
       "550067               1      20     NaN     NaN       490  \n",
       "\n",
       "[550068 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e3e8e23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:05.098938Z",
     "iopub.status.busy": "2024-02-06T17:17:05.098525Z",
     "iopub.status.idle": "2024-02-06T17:17:06.843271Z",
     "shell.execute_reply": "2024-02-06T17:17:06.842397Z"
    },
    "papermill": {
     "duration": 1.767844,
     "end_time": "2024-02-06T17:17:06.845802",
     "exception": false,
     "start_time": "2024-02-06T17:17:05.077958",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdb28d17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:06.888401Z",
     "iopub.status.busy": "2024-02-06T17:17:06.887711Z",
     "iopub.status.idle": "2024-02-06T17:17:06.891908Z",
     "shell.execute_reply": "2024-02-06T17:17:06.890951Z"
    },
    "papermill": {
     "duration": 0.027875,
     "end_time": "2024-02-06T17:17:06.894296",
     "exception": false,
     "start_time": "2024-02-06T17:17:06.866421",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Purchase Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d2f0e2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:06.936596Z",
     "iopub.status.busy": "2024-02-06T17:17:06.935844Z",
     "iopub.status.idle": "2024-02-06T17:17:09.871249Z",
     "shell.execute_reply": "2024-02-06T17:17:09.869991Z"
    },
    "papermill": {
     "duration": 2.959163,
     "end_time": "2024-02-06T17:17:09.873873",
     "exception": false,
     "start_time": "2024-02-06T17:17:06.914710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/352269465.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(x=df['Purchase'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(nrows=1,ncols=1,figsize=(6,6))\n",
    "sns.distplot(x=df['Purchase'])\n",
    "axes.set_title('Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed5c56f",
   "metadata": {
    "papermill": {
     "duration": 0.021816,
     "end_time": "2024-02-06T17:17:09.916718",
     "exception": false,
     "start_time": "2024-02-06T17:17:09.894902",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d68755b3",
   "metadata": {
    "papermill": {
     "duration": 0.021193,
     "end_time": "2024-02-06T17:17:09.960331",
     "exception": false,
     "start_time": "2024-02-06T17:17:09.939138",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Fill NaN Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fc37ca97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:10.003668Z",
     "iopub.status.busy": "2024-02-06T17:17:10.003282Z",
     "iopub.status.idle": "2024-02-06T17:17:10.021805Z",
     "shell.execute_reply": "2024-02-06T17:17:10.020467Z"
    },
    "papermill": {
     "duration": 0.043849,
     "end_time": "2024-02-06T17:17:10.024703",
     "exception": false,
     "start_time": "2024-02-06T17:17:09.980854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Prod_2']=df['Prod_2'].replace(np.NaN,df['Prod_2'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b33f6c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:10.067805Z",
     "iopub.status.busy": "2024-02-06T17:17:10.067441Z",
     "iopub.status.idle": "2024-02-06T17:17:10.083805Z",
     "shell.execute_reply": "2024-02-06T17:17:10.082730Z"
    },
    "papermill": {
     "duration": 0.04066,
     "end_time": "2024-02-06T17:17:10.086431",
     "exception": false,
     "start_time": "2024-02-06T17:17:10.045771",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Prod_3']=df['Prod_3'].replace(np.NaN,df['Prod_3'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5d6d4ac0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:10.130257Z",
     "iopub.status.busy": "2024-02-06T17:17:10.129808Z",
     "iopub.status.idle": "2024-02-06T17:17:10.153260Z",
     "shell.execute_reply": "2024-02-06T17:17:10.151965Z"
    },
    "papermill": {
     "duration": 0.049229,
     "end_time": "2024-02-06T17:17:10.155781",
     "exception": false,
     "start_time": "2024-02-06T17:17:10.106552",
     "status": "completed"
    },
    "tags": []
   },
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Prod_1</th>\n",
       "      <th>Prod_2</th>\n",
       "      <th>Prod_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>child</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>child</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>child</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>child</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>old</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550063</th>\n",
       "      <td>1</td>\n",
       "      <td>old adults</td>\n",
       "      <td>13</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550064</th>\n",
       "      <td>0</td>\n",
       "      <td>young adults</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550065</th>\n",
       "      <td>0</td>\n",
       "      <td>young adults</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550066</th>\n",
       "      <td>0</td>\n",
       "      <td>old</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550067</th>\n",
       "      <td>0</td>\n",
       "      <td>mid age</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9.842329</td>\n",
       "      <td>12.668243</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>550068 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender            Age  Occupation City Stay_In_Current_City_Years  \\\n",
       "0            0          child          10    A                          2   \n",
       "1            0          child          10    A                          2   \n",
       "2            0          child          10    A                          2   \n",
       "3            0          child          10    A                          2   \n",
       "4            1            old          16    C                         4+   \n",
       "...        ...            ...         ...  ...                        ...   \n",
       "550063       1     old adults          13    B                          1   \n",
       "550064       0   young adults           1    C                          3   \n",
       "550065       0   young adults          15    B                         4+   \n",
       "550066       0            old           1    C                          2   \n",
       "550067       0        mid age           0    B                         4+   \n",
       "\n",
       "        Marital_Status  Prod_1     Prod_2     Prod_3  Purchase  \n",
       "0                    0       3   9.842329  12.668243      8370  \n",
       "1                    0       1   6.000000  14.000000     15200  \n",
       "2                    0      12   9.842329  12.668243      1422  \n",
       "3                    0      12  14.000000  12.668243      1057  \n",
       "4                    0       8   9.842329  12.668243      7969  \n",
       "...                ...     ...        ...        ...       ...  \n",
       "550063               1      20   9.842329  12.668243       368  \n",
       "550064               0      20   9.842329  12.668243       371  \n",
       "550065               1      20   9.842329  12.668243       137  \n",
       "550066               0      20   9.842329  12.668243       365  \n",
       "550067               1      20   9.842329  12.668243       490  \n",
       "\n",
       "[550068 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218a2d48",
   "metadata": {
    "papermill": {
     "duration": 0.020198,
     "end_time": "2024-02-06T17:17:10.196707",
     "exception": false,
     "start_time": "2024-02-06T17:17:10.176509",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Check for Outliners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "30732d0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:10.240040Z",
     "iopub.status.busy": "2024-02-06T17:17:10.239028Z",
     "iopub.status.idle": "2024-02-06T17:17:10.637273Z",
     "shell.execute_reply": "2024-02-06T17:17:10.635839Z"
    },
    "papermill": {
     "duration": 0.423897,
     "end_time": "2024-02-06T17:17:10.641023",
     "exception": false,
     "start_time": "2024-02-06T17:17:10.217126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prod_1'>"
      ]
     },
     "execution_count": 31,
     "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": [
    "sns.boxplot(data=df,x=df['Prod_1'],color='yellow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "70ed1c4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:10.686860Z",
     "iopub.status.busy": "2024-02-06T17:17:10.686463Z",
     "iopub.status.idle": "2024-02-06T17:17:11.229587Z",
     "shell.execute_reply": "2024-02-06T17:17:11.228421Z"
    },
    "papermill": {
     "duration": 0.56832,
     "end_time": "2024-02-06T17:17:11.232335",
     "exception": false,
     "start_time": "2024-02-06T17:17:10.664015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prod_2'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "sns.boxplot(data=df,x=df['Prod_2'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f81d8fe7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:11.279524Z",
     "iopub.status.busy": "2024-02-06T17:17:11.279119Z",
     "iopub.status.idle": "2024-02-06T17:17:12.472418Z",
     "shell.execute_reply": "2024-02-06T17:17:12.471039Z"
    },
    "papermill": {
     "duration": 1.220264,
     "end_time": "2024-02-06T17:17:12.475248",
     "exception": false,
     "start_time": "2024-02-06T17:17:11.254984",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Prod_3'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "sns.boxplot(data=df,x=df['Prod_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d0ce9d05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:12.524365Z",
     "iopub.status.busy": "2024-02-06T17:17:12.523889Z",
     "iopub.status.idle": "2024-02-06T17:17:22.243411Z",
     "shell.execute_reply": "2024-02-06T17:17:22.242262Z"
    },
    "papermill": {
     "duration": 9.74748,
     "end_time": "2024-02-06T17:17:22.247048",
     "exception": false,
     "start_time": "2024-02-06T17:17:12.499568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Purchase'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "sns.lineplot(x=df['Age'],y=df['Purchase'],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "df9295b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:22.295391Z",
     "iopub.status.busy": "2024-02-06T17:17:22.294294Z",
     "iopub.status.idle": "2024-02-06T17:17:32.397051Z",
     "shell.execute_reply": "2024-02-06T17:17:32.395564Z"
    },
    "papermill": {
     "duration": 10.12954,
     "end_time": "2024-02-06T17:17:32.399615",
     "exception": false,
     "start_time": "2024-02-06T17:17:22.270075",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='City', ylabel='Purchase'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "sns.lineplot(x=df['City'],y=df['Purchase'],data=df,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "08d1fbe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:32.449782Z",
     "iopub.status.busy": "2024-02-06T17:17:32.448458Z",
     "iopub.status.idle": "2024-02-06T17:17:40.490456Z",
     "shell.execute_reply": "2024-02-06T17:17:40.489267Z"
    },
    "papermill": {
     "duration": 8.069852,
     "end_time": "2024-02-06T17:17:40.493152",
     "exception": false,
     "start_time": "2024-02-06T17:17:32.423300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Purchase'>"
      ]
     },
     "execution_count": 36,
     "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": [
    "sns.barplot(x=df['Occupation'],y=df['Purchase'],data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275621f5",
   "metadata": {
    "papermill": {
     "duration": 0.024017,
     "end_time": "2024-02-06T17:17:40.541692",
     "exception": false,
     "start_time": "2024-02-06T17:17:40.517675",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From above we can say that Occupation of 12,15,17 has highest purchasing capacity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b61998",
   "metadata": {
    "papermill": {
     "duration": 0.024655,
     "end_time": "2024-02-06T17:17:40.591810",
     "exception": false,
     "start_time": "2024-02-06T17:17:40.567155",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<!-- Product_category_1 vs Purchased -->"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed89270",
   "metadata": {
    "papermill": {
     "duration": 0.02569,
     "end_time": "2024-02-06T17:17:40.643608",
     "exception": false,
     "start_time": "2024-02-06T17:17:40.617918",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Product_category_1 vs Purchased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a9cf3b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:40.695486Z",
     "iopub.status.busy": "2024-02-06T17:17:40.695056Z",
     "iopub.status.idle": "2024-02-06T17:17:49.443411Z",
     "shell.execute_reply": "2024-02-06T17:17:49.442178Z"
    },
    "papermill": {
     "duration": 8.776965,
     "end_time": "2024-02-06T17:17:49.446221",
     "exception": false,
     "start_time": "2024-02-06T17:17:40.669256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 37,
     "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": [
    "sns.barplot(x=df['Prod_1'],y=df['Purchase'],data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09ab6d5",
   "metadata": {
    "papermill": {
     "duration": 0.025601,
     "end_time": "2024-02-06T17:17:49.496538",
     "exception": false,
     "start_time": "2024-02-06T17:17:49.470937",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Product_category_2 vs Purchased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "feb64c4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:49.549846Z",
     "iopub.status.busy": "2024-02-06T17:17:49.549421Z",
     "iopub.status.idle": "2024-02-06T17:17:58.304626Z",
     "shell.execute_reply": "2024-02-06T17:17:58.303238Z"
    },
    "papermill": {
     "duration": 8.785306,
     "end_time": "2024-02-06T17:17:58.307351",
     "exception": false,
     "start_time": "2024-02-06T17:17:49.522045",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sns.barplot(x=df['Prod_2'],y=df['Purchase'],data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99b2d80",
   "metadata": {
    "papermill": {
     "duration": 0.025123,
     "end_time": "2024-02-06T17:17:58.358994",
     "exception": false,
     "start_time": "2024-02-06T17:17:58.333871",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Product_category_3 vs Purchased"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4d2d957e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:17:58.412582Z",
     "iopub.status.busy": "2024-02-06T17:17:58.411847Z",
     "iopub.status.idle": "2024-02-06T17:18:09.521116Z",
     "shell.execute_reply": "2024-02-06T17:18:09.520016Z"
    },
    "papermill": {
     "duration": 11.139264,
     "end_time": "2024-02-06T17:18:09.523557",
     "exception": false,
     "start_time": "2024-02-06T17:17:58.384293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 39,
     "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": [
    "sns.barplot(x=df['Prod_3'],y=df['Purchase'],data=df,color='orange')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18a20032",
   "metadata": {
    "papermill": {
     "duration": 0.026006,
     "end_time": "2024-02-06T17:18:09.575777",
     "exception": false,
     "start_time": "2024-02-06T17:18:09.549771",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "City category pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7e7deaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-06T17:18:09.631625Z",
     "iopub.status.busy": "2024-02-06T17:18:09.630924Z",
     "iopub.status.idle": "2024-02-06T17:18:09.892122Z",
     "shell.execute_reply": "2024-02-06T17:18:09.890213Z"
    },
    "papermill": {
     "duration": 0.299318,
     "end_time": "2024-02-06T17:18:09.901726",
     "exception": false,
     "start_time": "2024-02-06T17:18:09.602408",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "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": [
    "df['City'].value_counts().plot.pie(autopct='%1.1f%%',labels=['A','B','C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f10bf629",
   "metadata": {
    "papermill": {
     "duration": 0.02714,
     "end_time": "2024-02-06T17:18:09.984299",
     "exception": false,
     "start_time": "2024-02-06T17:18:09.957159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 2457185,
     "sourceId": 4163061,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30558,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 75.297048,
   "end_time": "2024-02-06T17:18:10.735938",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-02-06T17:16:55.438890",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}