Black Friday - Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb3dd480-061f-46c6-a281-d4b873a37131",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae07eba1-285e-4f56-aa55-33e29eaa8a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BlackFriday.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c364e5e9-1716-4668-bd75-4676e1d46f8a",
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       "      <th>User_ID</th>\n",
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       "      <th>Occupation</th>\n",
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       "      <th>Product_Category_3</th>\n",
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       "<p>537577 rows × 12 columns</p>\n",
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       "        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",
       "537572  1004737  P00193542      M  36-45          16             C   \n",
       "537573  1004737  P00111142      M  36-45          16             C   \n",
       "537574  1004737  P00345942      M  36-45          16             C   \n",
       "537575  1004737  P00285842      M  36-45          16             C   \n",
       "537576  1004737  P00118242      M  36-45          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",
       "537572                          1               0                   1   \n",
       "537573                          1               0                   1   \n",
       "537574                          1               0                   8   \n",
       "537575                          1               0                   5   \n",
       "537576                          1               0                   5   \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",
       "...                    ...                 ...       ...  \n",
       "537572                 2.0                 NaN     11664  \n",
       "537573                15.0                16.0     19196  \n",
       "537574                15.0                 NaN      8043  \n",
       "537575                 NaN                 NaN      7172  \n",
       "537576                 8.0                 NaN      6875  \n",
       "\n",
       "[537577 rows x 12 columns]"
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   "source": [
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   ]
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   "id": "253a6c80-3d5e-4358-9238-afd1e9864589",
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    {
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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",
       "\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  "
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     "execution_count": 8,
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   "source": [
    "df.head()"
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   "id": "dd1007cd-51e6-431e-bb63-1fbcadf8c264",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 537577 entries, 0 to 537576\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     537577 non-null  int64  \n",
      " 1   Product_ID                  537577 non-null  object \n",
      " 2   Gender                      537577 non-null  object \n",
      " 3   Age                         537577 non-null  object \n",
      " 4   Occupation                  537577 non-null  int64  \n",
      " 5   City_Category               537577 non-null  object \n",
      " 6   Stay_In_Current_City_Years  537577 non-null  object \n",
      " 7   Marital_Status              537577 non-null  int64  \n",
      " 8   Product_Category_1          537577 non-null  int64  \n",
      " 9   Product_Category_2          370591 non-null  float64\n",
      " 10  Product_Category_3          164278 non-null  float64\n",
      " 11  Purchase                    537577 non-null  int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 49.2+ MB\n"
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   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7241fd1d-969f-4b71-9f1a-a8bd62da6446",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>537574</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",
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       "    <tr>\n",
       "      <th>537575</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>537576</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>537577 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",
       "537572    False       False   False  False       False          False   \n",
       "537573    False       False   False  False       False          False   \n",
       "537574    False       False   False  False       False          False   \n",
       "537575    False       False   False  False       False          False   \n",
       "537576    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",
       "537572                       False           False               False   \n",
       "537573                       False           False               False   \n",
       "537574                       False           False               False   \n",
       "537575                       False           False               False   \n",
       "537576                       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",
       "537572               False                True     False  \n",
       "537573               False               False     False  \n",
       "537574               False                True     False  \n",
       "537575                True                True     False  \n",
       "537576               False                True     False  \n",
       "\n",
       "[537577 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "457142f3-a7fd-48af-92f6-0e8ca29b6d09",
   "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            166986\n",
       "Product_Category_3            373299\n",
       "Purchase                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3687a103-9856-4c86-b551-eb43d9ebdb7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        User_ID Product_ID Gender    Age  Occupation City_Category  \\\n",
       "1       1000001  P00248942      F   0-17          10             A   \n",
       "6       1000004  P00184942      M  46-50           7             B   \n",
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       "...         ...        ...    ...    ...         ...           ...   \n",
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       "537551  1004735  P00313442      M  46-50           3             C   \n",
       "537562  1004736  P00146742      M  18-25          20             A   \n",
       "537571  1004737  P00221442      M  36-45          16             C   \n",
       "537573  1004737  P00111142      M  36-45          16             C   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "1                               2               0                   1   \n",
       "6                               2               1                   1   \n",
       "13                              1               1                   1   \n",
       "14                              1               0                   5   \n",
       "16                              1               0                   2   \n",
       "...                           ...             ...                 ...   \n",
       "537549                          1               1                   2   \n",
       "537551                          3               0                   5   \n",
       "537562                          1               1                   1   \n",
       "537571                          1               0                   1   \n",
       "537573                          1               0                   1   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  Purchase  \n",
       "1                      6.0                14.0     15200  \n",
       "6                      8.0                17.0     19215  \n",
       "13                     2.0                 5.0     15665  \n",
       "14                     8.0                14.0      5378  \n",
       "16                     3.0                 4.0     13055  \n",
       "...                    ...                 ...       ...  \n",
       "537549                 8.0                14.0     13082  \n",
       "537551                 6.0                 8.0      6863  \n",
       "537562                13.0                14.0     11508  \n",
       "537571                 2.0                 5.0     11852  \n",
       "537573                15.0                16.0     19196  \n",
       "\n",
       "[164278 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "041a5a67-2320-47c3-b366-f66edfe48bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "del df['Product_Category_2']\n",
    "del df['Product_Category_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "42cdbb5a-3878-46ee-845d-347b13452916",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537574</th>\n",
       "      <td>1004737</td>\n",
       "      <td>P00345942</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537575</th>\n",
       "      <td>1004737</td>\n",
       "      <td>P00285842</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537576</th>\n",
       "      <td>1004737</td>\n",
       "      <td>P00118242</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537577 rows × 10 columns</p>\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",
       "537572  1004737  P00193542      M  36-45          16             C   \n",
       "537573  1004737  P00111142      M  36-45          16             C   \n",
       "537574  1004737  P00345942      M  36-45          16             C   \n",
       "537575  1004737  P00285842      M  36-45          16             C   \n",
       "537576  1004737  P00118242      M  36-45          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",
       "537572                          1               0                   1   \n",
       "537573                          1               0                   1   \n",
       "537574                          1               0                   8   \n",
       "537575                          1               0                   5   \n",
       "537576                          1               0                   5   \n",
       "\n",
       "        Purchase  \n",
       "0           8370  \n",
       "1          15200  \n",
       "2           1422  \n",
       "3           1057  \n",
       "4           7969  \n",
       "...          ...  \n",
       "537572     11664  \n",
       "537573     19196  \n",
       "537574      8043  \n",
       "537575      7172  \n",
       "537576      6875  \n",
       "\n",
       "[537577 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b85f15c0-ea53-445f-aab5-cf25edf5287b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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