PPM.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "bBflHuSOoexY"
   },
   "outputs": [],
   "source": [
    "# Import library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Dm8i9N83ok4t"
   },
   "outputs": [],
   "source": [
    "#import data\n",
    "user = pd.read_csv('user.csv', dtype = {'User_id': str})\n",
    "campaign = pd.read_csv('campaign.csv', dtype = {'Campaign_id': str})\n",
    "purchase = pd.read_csv('purchase.csv', dtype = {'User_id': str})\n",
    "interact = pd.read_csv('interact.csv', dtype = {'User_id': str, 'Campaign_id': str})\n",
    "product = pd.read_csv('product.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "daCnI1UQuWCI"
   },
   "outputs": [],
   "source": [
    "purchase['Model'] = purchase['Model'].fillna('Other') #Clean data: fill missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3u6wB53p1HhW"
   },
   "outputs": [],
   "source": [
    "purchase['Purchase_day'] = purchase['Purchase_day'].replace(to_replace={0:30})\n",
    "purchase['Purchase_day'] = np.where((purchase['Purchase_month']==2)&(purchase['Purchase_day']>28), [28] * purchase.shape[0], purchase['Purchase_day'])\n",
    "#Clean data: correct date time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lIUmXgHBoyCN",
    "outputId": "52c9ddda-4ea2-49ce-ae9a-9326e44a6cb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 4), (40, 7), (800000, 10))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user['tmp'] = 0\n",
    "campaign['tmp'] = 0\n",
    "df = pd.merge(user, campaign, on='tmp', how='outer')\n",
    "user.shape, campaign.shape, df.shape\n",
    "#Cross join 2 dataframes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "VBqO4hWnpajZ"
   },
   "outputs": [],
   "source": [
    "df = df.drop('tmp', axis=1)"
   ]
  },
  {
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   "execution_count": 7,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Year_of_birth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Start_date</th>\n",
       "      <th>End_date</th>\n",
       "      <th>Campaign_type</th>\n",
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       "      <th>Campaign_id</th>\n",
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       "      <td>3713087409444908179</td>\n",
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       "      <td>1982</td>\n",
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       "      <td>2014-08-01</td>\n",
       "      <td>2014-09-30</td>\n",
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       "      <td>1982</td>\n",
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       "      <td>2015-06-01</td>\n",
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       "      <td>1987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>post</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>14795309059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799996</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>1987</td>\n",
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       "      <td>2017-04-01</td>\n",
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       "      <td>16593507087486383889</td>\n",
       "      <td>1987</td>\n",
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       "      <td>2018-03-31</td>\n",
       "      <td>post</td>\n",
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       "      <td>16593507087486383889</td>\n",
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       "      <td>2019-02-01</td>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>post</td>\n",
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       "      <td>2020-02-01</td>\n",
       "      <td>2020-03-02</td>\n",
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       "<p>800000 rows × 9 columns</p>\n",
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       "                     User_id  Year_of_birth  Gender  Start_date    End_date  \\\n",
       "0        3713087409444908179           1982     1.0  2011-08-01  2011-09-30   \n",
       "1        3713087409444908179           1982     1.0  2012-07-02  2012-08-31   \n",
       "2        3713087409444908179           1982     1.0  2013-07-02  2013-08-31   \n",
       "3        3713087409444908179           1982     1.0  2014-08-01  2014-09-30   \n",
       "4        3713087409444908179           1982     1.0  2015-06-01  2015-07-31   \n",
       "...                      ...            ...     ...         ...         ...   \n",
       "799995  16593507087486383889           1987     1.0  2016-03-01  2016-03-31   \n",
       "799996  16593507087486383889           1987     1.0  2017-04-01  2017-05-01   \n",
       "799997  16593507087486383889           1987     1.0  2018-03-01  2018-03-31   \n",
       "799998  16593507087486383889           1987     1.0  2019-02-01  2019-03-03   \n",
       "799999  16593507087486383889           1987     1.0  2020-02-01  2020-03-02   \n",
       "\n",
       "       Campaign_type  Model Series  Campaign_id  \n",
       "0                pre      1      A   3713087409  \n",
       "1                pre      2      A   7554402398  \n",
       "2                pre      3      A   1687604933  \n",
       "3                pre      4      A   7587835399  \n",
       "4                pre      5      A   6799016569  \n",
       "...              ...    ...    ...          ...  \n",
       "799995          post      6      B  14795309059  \n",
       "799996          post      7      B  16264149150  \n",
       "799997          post      8      B  11483833140  \n",
       "799998          post      9      B  10530765771  \n",
       "799999          post     10      B   2056813848  \n",
       "\n",
       "[800000 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "wUlPj13tpg7R"
   },
   "outputs": [],
   "source": [
    "df = df[df['Campaign_type']=='post']\n",
    "df = df.rename(columns={'Start_date':'base_date'})\n",
    "df = df.drop('End_date', axis=1)\n",
    "# Filter post launching campaign, rename column title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "T1ObikMtvbfk"
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Model':'Campaign_model','Series':'Campaign_series'}) #rename column title, to differentiate with model & series in purchase history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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       "      <td>3713087409444908179</td>\n",
       "      <td>1982</td>\n",
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       "      <td>2015-08-01</td>\n",
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       "      <td>16593507087486383889</td>\n",
       "      <td>1987</td>\n",
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       "<p>400000 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "                     User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "10       3713087409444908179           1982     1.0  2011-10-01          post   \n",
       "11       3713087409444908179           1982     1.0  2012-09-01          post   \n",
       "12       3713087409444908179           1982     1.0  2013-09-01          post   \n",
       "13       3713087409444908179           1982     1.0  2014-10-01          post   \n",
       "14       3713087409444908179           1982     1.0  2015-08-01          post   \n",
       "...                      ...            ...     ...         ...           ...   \n",
       "799995  16593507087486383889           1987     1.0  2016-03-01          post   \n",
       "799996  16593507087486383889           1987     1.0  2017-04-01          post   \n",
       "799997  16593507087486383889           1987     1.0  2018-03-01          post   \n",
       "799998  16593507087486383889           1987     1.0  2019-02-01          post   \n",
       "799999  16593507087486383889           1987     1.0  2020-02-01          post   \n",
       "\n",
       "        Campaign_model Campaign_series  Campaign_id  \n",
       "10                   1               A   4615867097  \n",
       "11                   2               A  10677889990  \n",
       "12                   3               A  16570399437  \n",
       "13                   4               A  13984037253  \n",
       "14                   5               A   2721833483  \n",
       "...                ...             ...          ...  \n",
       "799995               6               B  14795309059  \n",
       "799996               7               B  16264149150  \n",
       "799997               8               B  11483833140  \n",
       "799998               9               B  10530765771  \n",
       "799999              10               B   2056813848  \n",
       "\n",
       "[400000 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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       "      <th>0</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2006-10-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2007-10-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2012-04-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2014-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2016-11-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158967</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158968</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2013-08-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158969</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2015-06-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158970</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2018-10-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158971</th>\n",
       "      <td>16593507087486383889</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>2020-08-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158972 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     User_id Product_series Product_model   base_date\n",
       "0        3713087409444908179          Other         Other  2006-10-06\n",
       "1        3713087409444908179          Other         Other  2007-10-11\n",
       "2        3713087409444908179          Other         Other  2012-04-08\n",
       "3        3713087409444908179          Other         Other  2014-04-04\n",
       "4        3713087409444908179          Other         Other  2016-11-07\n",
       "...                      ...            ...           ...         ...\n",
       "158967  16593507087486383889              B             2  2012-05-03\n",
       "158968  16593507087486383889          Other         Other  2013-08-11\n",
       "158969  16593507087486383889          Other         Other  2015-06-03\n",
       "158970  16593507087486383889          Other         Other  2018-10-17\n",
       "158971  16593507087486383889          Other         Other  2020-08-02\n",
       "\n",
       "[158972 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase = purchase[~pd.isna(purchase['Purchase_year'])]\n",
    "purchase['Purchase_year'] = [('%04d' % x) for x in purchase['Purchase_year'].astype(int).values]\n",
    "purchase['Purchase_month'] = [('%02d' % x) for x in purchase['Purchase_month'].astype(int).values]\n",
    "purchase['Purchase_day'] = [('%02d' % x) for x in purchase['Purchase_day'].astype(int).values]\n",
    "purchase['base_date'] = purchase['Purchase_year'] + '-' + purchase['Purchase_month'] + '-' + purchase['Purchase_day']\n",
    "purchase = purchase.drop(['Purchase_year','Purchase_month','Purchase_day'], axis=1)\n",
    "purchase = purchase.rename(columns={'Series':'Product_series','Model':'Product_model'})\n",
    "purchase\n",
    "#Cleaning: formulate base_date column from separate year, month, day columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "yhULn7rxp71W"
   },
   "outputs": [],
   "source": [
    "purchase = pd.merge(purchase, user, on='User_id', how='left')\n",
    "for col in ['Campaign_type','Campaign_model','Campaign_series','Campaign_id']:\n",
    "  purchase[col] = np.nan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "mu_vfZdzq3_3"
   },
   "outputs": [],
   "source": [
    "for col in ['Product_series','Product_model']:\n",
    "  df[col] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "9BuObnpGtyWS"
   },
   "outputs": [],
   "source": [
    "purchase = purchase[df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
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    "id": "GbjxrMYAr9pJ",
    "outputId": "b09ef0b9-d150-47c9-ffc2-93b698e72bc0",
    "scrolled": true
   },
   "outputs": [
    {
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       "                     User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "10       3713087409444908179           1982     1.0  2011-10-01          post   \n",
       "11       3713087409444908179           1982     1.0  2012-09-01          post   \n",
       "12       3713087409444908179           1982     1.0  2013-09-01          post   \n",
       "13       3713087409444908179           1982     1.0  2014-10-01          post   \n",
       "14       3713087409444908179           1982     1.0  2015-08-01          post   \n",
       "...                      ...            ...     ...         ...           ...   \n",
       "799995  16593507087486383889           1987     1.0  2016-03-01          post   \n",
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       "799998  16593507087486383889           1987     1.0  2019-02-01          post   \n",
       "799999  16593507087486383889           1987     1.0  2020-02-01          post   \n",
       "\n",
       "        Campaign_model Campaign_series  Campaign_id  Product_series  \\\n",
       "10                   1               A   4615867097             NaN   \n",
       "11                   2               A  10677889990             NaN   \n",
       "12                   3               A  16570399437             NaN   \n",
       "13                   4               A  13984037253             NaN   \n",
       "14                   5               A   2721833483             NaN   \n",
       "...                ...             ...          ...             ...   \n",
       "799995               6               B  14795309059             NaN   \n",
       "799996               7               B  16264149150             NaN   \n",
       "799997               8               B  11483833140             NaN   \n",
       "799998               9               B  10530765771             NaN   \n",
       "799999              10               B   2056813848             NaN   \n",
       "\n",
       "        Product_model  \n",
       "10                NaN  \n",
       "11                NaN  \n",
       "12                NaN  \n",
       "13                NaN  \n",
       "14                NaN  \n",
       "...               ...  \n",
       "799995            NaN  \n",
       "799996            NaN  \n",
       "799997            NaN  \n",
       "799998            NaN  \n",
       "799999            NaN  \n",
       "\n",
       "[400000 rows x 10 columns]"
      ]
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     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df\n"
   ]
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    "id": "3522DtPtr-3o",
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      "text/plain": [
       "               User_id  Year_of_birth  Gender   base_date  Campaign_type  \\\n",
       "0  3713087409444908179           1982     1.0  2006-10-06            NaN   \n",
       "1  3713087409444908179           1982     1.0  2007-10-11            NaN   \n",
       "2  3713087409444908179           1982     1.0  2012-04-08            NaN   \n",
       "3  3713087409444908179           1982     1.0  2014-04-04            NaN   \n",
       "4  3713087409444908179           1982     1.0  2016-11-07            NaN   \n",
       "\n",
       "   Campaign_model  Campaign_series  Campaign_id Product_series Product_model  \n",
       "0             NaN              NaN          NaN          Other         Other  \n",
       "1             NaN              NaN          NaN          Other         Other  \n",
       "2             NaN              NaN          NaN          Other         Other  \n",
       "3             NaN              NaN          NaN          Other         Other  \n",
       "4             NaN              NaN          NaN          Other         Other  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "lW2Eh759sBOV"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([df, purchase]).sort_values(['User_id','base_date'])\n",
    "# from [14] to [19] merge purchase & campaign in order to calculate purchase history at base date of marketing campaign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
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       "      <td>2009-01-08</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
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       "      <td>2011-04-01</td>\n",
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       "      <td>17928414209</td>\n",
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       "      <th>500890</th>\n",
       "      <td>10000099596358040339</td>\n",
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       "      <td>A</td>\n",
       "      <td>4615867097</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>37737</th>\n",
       "      <td>9999892773214337297</td>\n",
       "      <td>1992</td>\n",
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       "    <tr>\n",
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       "<p>558972 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "                     User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "99820   10000099596358040339           1995     0.0  2001-05-22           NaN   \n",
       "99821   10000099596358040339           1995     0.0  2006-01-18           NaN   \n",
       "99822   10000099596358040339           1995     0.0  2009-01-08           NaN   \n",
       "500910  10000099596358040339           1995     0.0  2011-04-01          post   \n",
       "500890  10000099596358040339           1995     0.0  2011-10-01          post   \n",
       "...                      ...            ...     ...         ...           ...   \n",
       "37737    9999892773214337297           1992     0.0  2018-08-01          post   \n",
       "37758    9999892773214337297           1992     0.0  2019-02-01          post   \n",
       "37738    9999892773214337297           1992     0.0  2019-08-01          post   \n",
       "37759    9999892773214337297           1992     0.0  2020-02-01          post   \n",
       "37739    9999892773214337297           1992     0.0  2020-08-01          post   \n",
       "\n",
       "        Campaign_model Campaign_series  Campaign_id Product_series  \\\n",
       "99820              NaN             NaN          NaN          Other   \n",
       "99821              NaN             NaN          NaN          Other   \n",
       "99822              NaN             NaN          NaN          Other   \n",
       "500910             1.0               B  17928414209            NaN   \n",
       "500890             1.0               A   4615867097            NaN   \n",
       "...                ...             ...          ...            ...   \n",
       "37737              8.0               A   3291887422            NaN   \n",
       "37758              9.0               B  10530765771            NaN   \n",
       "37738              9.0               A   7037657696            NaN   \n",
       "37759             10.0               B   2056813848            NaN   \n",
       "37739             10.0               A   8942250407            NaN   \n",
       "\n",
       "       Product_model  \n",
       "99820          Other  \n",
       "99821          Other  \n",
       "99822          Other  \n",
       "500910           NaN  \n",
       "500890           NaN  \n",
       "...              ...  \n",
       "37737            NaN  \n",
       "37758            NaN  \n",
       "37738            NaN  \n",
       "37759            NaN  \n",
       "37739            NaN  \n",
       "\n",
       "[558972 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
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    "id": "0z8360avuz2I",
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    {
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      "text/plain": [
       "                User_id First_purchase\n",
       "0  10000099596358040339     2001-05-22\n",
       "1  10002134195333118155     2001-01-05\n",
       "2  10002461955448154319     2001-03-14\n",
       "3  10005203855513699745     2004-06-30\n",
       "4  10005348599546239145     2002-02-11"
      ]
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   ],
   "source": [
    "first_purchase = df[pd.notna(df['Product_model'])].groupby('User_id')['base_date'].first().reset_index(name='First_purchase')\n",
    "first_purchase.head()\n",
    "# find out first purchase of each user"
   ]
  },
  {
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   "execution_count": 20,
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   "outputs": [
    {
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      "text/plain": [
       "                User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0  2001-05-22           NaN   \n",
       "1  10000099596358040339           1995     0.0  2006-01-18           NaN   \n",
       "2  10000099596358040339           1995     0.0  2009-01-08           NaN   \n",
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       "4  10000099596358040339           1995     0.0  2011-10-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             NaN             NaN          NaN          Other         Other   \n",
       "1             NaN             NaN          NaN          Other         Other   \n",
       "2             NaN             NaN          NaN          Other         Other   \n",
       "3             1.0               B  17928414209            NaN           NaN   \n",
       "4             1.0               A   4615867097            NaN           NaN   \n",
       "\n",
       "  First_purchase  \n",
       "0     2001-05-22  \n",
       "1     2001-05-22  \n",
       "2     2001-05-22  \n",
       "3     2001-05-22  \n",
       "4     2001-05-22  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "df = pd.merge(df, first_purchase, on='User_id', how='left')\n",
    "df.head()\n",
    "# add first purchase column into original user table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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      "text/plain": [
       "                User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0  2001-05-22           NaN   \n",
       "1  10000099596358040339           1995     0.0  2006-01-18           NaN   \n",
       "2  10000099596358040339           1995     0.0  2009-01-08           NaN   \n",
       "3  10000099596358040339           1995     0.0  2011-04-01          post   \n",
       "4  10000099596358040339           1995     0.0  2011-10-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             NaN             NaN          NaN          Other         Other   \n",
       "1             NaN             NaN          NaN          Other         Other   \n",
       "2             NaN             NaN          NaN          Other         Other   \n",
       "3             1.0               B  17928414209            NaN           NaN   \n",
       "4             1.0               A   4615867097            NaN           NaN   \n",
       "\n",
       "  First_purchase Last_purchase  \n",
       "0     2001-05-22    2001-05-22  \n",
       "1     2001-05-22    2006-01-18  \n",
       "2     2001-05-22    2009-01-08  \n",
       "3     2001-05-22           NaN  \n",
       "4     2001-05-22           NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Last_purchase'] = np.where(pd.notna(df['Product_series']), df['base_date'], [np.nan] * df.shape[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
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    {
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      "text/plain": [
       "                User_id  Year_of_birth  Gender   base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0  2001-05-22           NaN   \n",
       "1  10000099596358040339           1995     0.0  2006-01-18           NaN   \n",
       "2  10000099596358040339           1995     0.0  2009-01-08           NaN   \n",
       "3  10000099596358040339           1995     0.0  2011-04-01          post   \n",
       "4  10000099596358040339           1995     0.0  2011-10-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             NaN             NaN          NaN          Other         Other   \n",
       "1             NaN             NaN          NaN          Other         Other   \n",
       "2             NaN             NaN          NaN          Other         Other   \n",
       "3             1.0               B  17928414209            NaN           NaN   \n",
       "4             1.0               A   4615867097            NaN           NaN   \n",
       "\n",
       "  First_purchase Last_purchase  \n",
       "0     2001-05-22    2001-05-22  \n",
       "1     2001-05-22    2006-01-18  \n",
       "2     2001-05-22    2009-01-08  \n",
       "3     2001-05-22    2009-01-08  \n",
       "4     2001-05-22    2009-01-08  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(50):\n",
    "  df['tmp'] = df.groupby('User_id')['Last_purchase'].shift(1)\n",
    "  df['Last_purchase'] = np.where(pd.isna(df['Last_purchase']), df['tmp'], df['Last_purchase'])\n",
    "df = df.drop('tmp', axis=1)\n",
    "df.head()\n",
    "\n",
    "#Create last purchase column by base_date:IF record is equivalent to purchase action, last_purchase = base_date, ELSE last_purchase=most recent purchase_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "MSD8No7Z0NVn"
   },
   "outputs": [],
   "source": [
    "df['base_date'] = pd.to_datetime(df['base_date'])\n",
    "df['First_purchase'] = pd.to_datetime(df['First_purchase'])\n",
    "df['Last_purchase'] = pd.to_datetime(df['Last_purchase']) # convert string to date time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
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    "outputId": "ac1250a9-18d4-47b5-9e16-d28558521a3b"
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    {
     "data": {
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      "text/plain": [
       "                User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0 2001-05-22           NaN   \n",
       "1  10000099596358040339           1995     0.0 2006-01-18           NaN   \n",
       "2  10000099596358040339           1995     0.0 2009-01-08           NaN   \n",
       "3  10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "4  10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             NaN             NaN          NaN          Other         Other   \n",
       "1             NaN             NaN          NaN          Other         Other   \n",
       "2             NaN             NaN          NaN          Other         Other   \n",
       "3             1.0               B  17928414209            NaN           NaN   \n",
       "4             1.0               A   4615867097            NaN           NaN   \n",
       "\n",
       "  First_purchase Last_purchase  Purchase_count  \n",
       "0     2001-05-22    2001-05-22               1  \n",
       "1     2001-05-22    2006-01-18               2  \n",
       "2     2001-05-22    2009-01-08               3  \n",
       "3     2001-05-22    2009-01-08               3  \n",
       "4     2001-05-22    2009-01-08               3  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Purchase_count'] = np.where(pd.notna(df['Product_series']), 1, 0)\n",
    "df['Purchase_count'] = df.groupby('User_id')['Purchase_count'].cumsum().values\n",
    "df.head() #calculate total purchase number up until base_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "4Zlyr7QX3_Up"
   },
   "outputs": [],
   "source": [
    "df['Frequency'] = np.where(df['Purchase_count']==1, [np.nan] * df.shape[0], (df['Last_purchase'] - df['First_purchase']).dt.days / (df['Purchase_count'] - 1))\n",
    "#calculate frequency, IF purchase_count=1, set nan, ELSE calculate interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "fpSXoXXd5GsE"
   },
   "outputs": [],
   "source": [
    "df['Recency'] = (df['base_date'] - df['Last_purchase']).dt.days\n",
    "#how long is from last_purchase to base_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    "id": "binTj2Z15Pzu",
    "outputId": "b8910f4d-6a37-48aa-b654-2c9fe99b2cc8"
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       "      <td>NaN</td>\n",
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       "      <td>813.0</td>\n",
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      "text/plain": [
       "                User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0 2001-05-22           NaN   \n",
       "1  10000099596358040339           1995     0.0 2006-01-18           NaN   \n",
       "2  10000099596358040339           1995     0.0 2009-01-08           NaN   \n",
       "3  10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "4  10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             NaN             NaN          NaN          Other         Other   \n",
       "1             NaN             NaN          NaN          Other         Other   \n",
       "2             NaN             NaN          NaN          Other         Other   \n",
       "3             1.0               B  17928414209            NaN           NaN   \n",
       "4             1.0               A   4615867097            NaN           NaN   \n",
       "\n",
       "  First_purchase Last_purchase  Purchase_count  Frequency  Recency  \n",
       "0     2001-05-22    2001-05-22               1        NaN      0.0  \n",
       "1     2001-05-22    2006-01-18               2     1702.0      0.0  \n",
       "2     2001-05-22    2009-01-08               3     1394.0      0.0  \n",
       "3     2001-05-22    2009-01-08               3     1394.0    813.0  \n",
       "4     2001-05-22    2009-01-08               3     1394.0    996.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "fH9WkULF51kO"
   },
   "outputs": [],
   "source": [
    "df['Last_product_model'] = df['Product_model']\n",
    "df['Last_product_series'] = df['Product_series']\n",
    "for i in range(50):\n",
    "  df['tmp1'] = df.groupby('User_id')['Last_product_model'].shift(1)\n",
    "  df['Last_product_model'] = np.where(pd.isna(df['Last_product_model']), df['tmp1'], df['Last_product_model'])\n",
    "  df['tmp2'] = df.groupby('User_id')['Last_product_series'].shift(1)\n",
    "  df['Last_product_series'] = np.where(pd.isna(df['Last_product_series']), df['tmp2'], df['Last_product_series'])\n",
    "df = df.drop(['tmp1','tmp2'], axis=1)\n",
    "# find out the most recent product belong to which series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "j80FVD3x7HSP",
    "outputId": "d21035bd-a6f2-4938-aa66-2383bb9b9dee",
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2004-06-04</td>\n",
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       "      <td>1992</td>\n",
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       "      <td>2056813848</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2004-06-04</td>\n",
       "      <td>2017-06-07</td>\n",
       "      <td>7</td>\n",
       "      <td>791.833333</td>\n",
       "      <td>969.0</td>\n",
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       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>558972 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0       10000099596358040339           1995     0.0 2001-05-22           NaN   \n",
       "1       10000099596358040339           1995     0.0 2006-01-18           NaN   \n",
       "2       10000099596358040339           1995     0.0 2009-01-08           NaN   \n",
       "3       10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "4       10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "...                      ...            ...     ...        ...           ...   \n",
       "558967   9999892773214337297           1992     0.0 2018-08-01          post   \n",
       "558968   9999892773214337297           1992     0.0 2019-02-01          post   \n",
       "558969   9999892773214337297           1992     0.0 2019-08-01          post   \n",
       "558970   9999892773214337297           1992     0.0 2020-02-01          post   \n",
       "558971   9999892773214337297           1992     0.0 2020-08-01          post   \n",
       "\n",
       "        Campaign_model Campaign_series  Campaign_id Product_series  \\\n",
       "0                  NaN             NaN          NaN          Other   \n",
       "1                  NaN             NaN          NaN          Other   \n",
       "2                  NaN             NaN          NaN          Other   \n",
       "3                  1.0               B  17928414209            NaN   \n",
       "4                  1.0               A   4615867097            NaN   \n",
       "...                ...             ...          ...            ...   \n",
       "558967             8.0               A   3291887422            NaN   \n",
       "558968             9.0               B  10530765771            NaN   \n",
       "558969             9.0               A   7037657696            NaN   \n",
       "558970            10.0               B   2056813848            NaN   \n",
       "558971            10.0               A   8942250407            NaN   \n",
       "\n",
       "       Product_model First_purchase Last_purchase  Purchase_count  \\\n",
       "0              Other     2001-05-22    2001-05-22               1   \n",
       "1              Other     2001-05-22    2006-01-18               2   \n",
       "2              Other     2001-05-22    2009-01-08               3   \n",
       "3                NaN     2001-05-22    2009-01-08               3   \n",
       "4                NaN     2001-05-22    2009-01-08               3   \n",
       "...              ...            ...           ...             ...   \n",
       "558967           NaN     2004-06-04    2017-06-07               7   \n",
       "558968           NaN     2004-06-04    2017-06-07               7   \n",
       "558969           NaN     2004-06-04    2017-06-07               7   \n",
       "558970           NaN     2004-06-04    2017-06-07               7   \n",
       "558971           NaN     2004-06-04    2017-06-07               7   \n",
       "\n",
       "          Frequency  Recency Last_product_model Last_product_series  \n",
       "0               NaN      0.0              Other               Other  \n",
       "1       1702.000000      0.0              Other               Other  \n",
       "2       1394.000000      0.0              Other               Other  \n",
       "3       1394.000000    813.0              Other               Other  \n",
       "4       1394.000000    996.0              Other               Other  \n",
       "...             ...      ...                ...                 ...  \n",
       "558967   791.833333    420.0              Other               Other  \n",
       "558968   791.833333    604.0              Other               Other  \n",
       "558969   791.833333    785.0              Other               Other  \n",
       "558970   791.833333    969.0              Other               Other  \n",
       "558971   791.833333   1151.0              Other               Other  \n",
       "\n",
       "[558972 rows x 17 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "interact = pd.merge(interact, campaign, on='Campaign_id', how='left')\n",
    "#merge interact vs campaign table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "interact_sum = interact.groupby(['User_id','Campaign_id','Start_date','End_date','Campaign_type','Model','Series'])['Interact_cnt'].sum().reset_index()\n",
    "# sum user for each campaign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                User_id  Campaign_id  Start_date    End_date Campaign_type  \\\n",
       "0  10000099596358040339  10069732865  2016-09-01  2016-10-01          post   \n",
       "1  10000099596358040339  10109409942  2015-01-30  2015-03-31           pre   \n",
       "2  10000099596358040339  10530765771  2019-02-01  2019-03-03          post   \n",
       "3  10000099596358040339  10677889990  2012-09-01  2012-10-01          post   \n",
       "4  10000099596358040339  11247919067  2012-03-01  2012-04-30           pre   \n",
       "\n",
       "   Model Series  Interact_cnt  \n",
       "0      6      A             8  \n",
       "1      5      B            14  \n",
       "2      9      B            10  \n",
       "3      2      A            11  \n",
       "4      2      B            22  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>Start_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>14</td>\n",
       "      <td>2015-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>2012-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>8</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-08-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                User_id  Model Series  Interact_cnt  Start_date\n",
       "0  10000099596358040339      5      B            14  2015-04-01\n",
       "1  10000099596358040339      2      B            22  2012-05-01\n",
       "2  10000099596358040339      7      B            24  2017-04-01\n",
       "3  10000099596358040339     10      A            18  2020-08-01\n",
       "4  10000099596358040339      8      A            18  2018-08-01"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum = interact_sum[interact_sum['Campaign_type']=='pre'][['User_id','Model','Series','Interact_cnt']]\n",
    "interact_sum = pd.merge(interact_sum, campaign[campaign['Campaign_type']=='post'][['Start_date','Model','Series']], on=['Model','Series'], how='left')\n",
    "interact_sum.head()\n",
    "# filter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Model</th>\n",
       "      <th>Series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>Start_date</th>\n",
       "      <th>tmp</th>\n",
       "      <th>Interact_sum</th>\n",
       "      <th>tmp_sum</th>\n",
       "      <th>Interact_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>14</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>22</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>24</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>8</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                User_id  Model Series  Interact_cnt  Start_date  tmp  \\\n",
       "0  10000099596358040339      5      B            14  2015-04-01    1   \n",
       "1  10000099596358040339      2      B            22  2012-05-01    1   \n",
       "2  10000099596358040339      7      B            24  2017-04-01    1   \n",
       "3  10000099596358040339     10      A            18  2020-08-01    1   \n",
       "4  10000099596358040339      8      A            18  2018-08-01    1   \n",
       "\n",
       "   Interact_sum  tmp_sum  Interact_avg  \n",
       "0            14        1          14.0  \n",
       "1            36        2          18.0  \n",
       "2            60        3          20.0  \n",
       "3            18        1          18.0  \n",
       "4            36        2          18.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum['tmp'] = [1] * interact_sum.shape[0]\n",
    "interact_sum['Interact_sum'] = interact_sum.groupby(['User_id','Series'])['Interact_cnt'].cumsum()\n",
    "interact_sum['tmp_sum'] = interact_sum.groupby(['User_id','Series'])['tmp'].cumsum()\n",
    "interact_sum['Interact_avg'] = interact_sum['Interact_sum'] / interact_sum['tmp_sum']\n",
    "interact_sum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Model</th>\n",
       "      <th>Series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>Start_date</th>\n",
       "      <th>tmp</th>\n",
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       "      <th>tmp_sum</th>\n",
       "      <th>Interact_avg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1</td>\n",
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       "      <td>2011-10-01</td>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
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       "      <td>16.222222</td>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>3</td>\n",
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       "      <td>2013-09-01</td>\n",
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       "      <td>2014-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>10</td>\n",
       "      <td>16.800000</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>8</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 User_id  Model Series  Interact_cnt  Start_date  tmp  \\\n",
       "11  10000099596358040339      1      A            12  2011-10-01    1   \n",
       "17  10000099596358040339      2      A            18  2012-09-01    1   \n",
       "8   10000099596358040339      3      A            14  2013-09-01    1   \n",
       "18  10000099596358040339      4      A            22  2014-10-01    1   \n",
       "16  10000099596358040339      5      A            12  2015-08-01    1   \n",
       "\n",
       "    Interact_sum  tmp_sum  Interact_avg  \n",
       "11           104        6     17.333333  \n",
       "17           146        9     16.222222  \n",
       "8             92        5     18.400000  \n",
       "18           168       10     16.800000  \n",
       "16           128        8     16.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum = interact_sum.sort_values(['User_id','Series','Start_date'])\n",
    "interact_sum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Interact_avg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1</td>\n",
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       "      <th>17</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
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       "      <td>2012-09-01</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>10000099596358040339</td>\n",
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       "      <td>18.400000</td>\n",
       "      <td>14.666667</td>\n",
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       "      <th>18</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>168</td>\n",
       "      <td>10</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>8</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 User_id  Model Series  Interact_cnt  Start_date  tmp  \\\n",
       "11  10000099596358040339      1      A            12  2011-10-01    1   \n",
       "17  10000099596358040339      2      A            18  2012-09-01    1   \n",
       "8   10000099596358040339      3      A            14  2013-09-01    1   \n",
       "18  10000099596358040339      4      A            22  2014-10-01    1   \n",
       "16  10000099596358040339      5      A            12  2015-08-01    1   \n",
       "\n",
       "    Interact_sum  tmp_sum  Interact_avg  Interact_avg_last3  \n",
       "11           104        6     17.333333                 NaN  \n",
       "17           146        9     16.222222                 NaN  \n",
       "8             92        5     18.400000           14.666667  \n",
       "18           168       10     16.800000           18.000000  \n",
       "16           128        8     16.000000           16.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum['Interact_avg_last3'] = interact_sum.groupby(['User_id','Series'])['Interact_cnt'].rolling(3).mean().values\n",
    "interact_sum.head()\n",
    "# count the last 3 interacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Campaign_model</th>\n",
       "      <th>Campaign_series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>base_date</th>\n",
       "      <th>Interact_sum</th>\n",
       "      <th>Interact_avg</th>\n",
       "      <th>Interact_avg_last3</th>\n",
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       "  <tbody>\n",
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       "      <th>11</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1</td>\n",
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       "      <td>2011-10-01</td>\n",
       "      <td>104</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
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       "      <td>2012-09-01</td>\n",
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       "      <th>8</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>92</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>14.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>168</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>128</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 User_id  Campaign_model Campaign_series  Interact_cnt  \\\n",
       "11  10000099596358040339               1               A            12   \n",
       "17  10000099596358040339               2               A            18   \n",
       "8   10000099596358040339               3               A            14   \n",
       "18  10000099596358040339               4               A            22   \n",
       "16  10000099596358040339               5               A            12   \n",
       "\n",
       "     base_date  Interact_sum  Interact_avg  Interact_avg_last3  \n",
       "11  2011-10-01           104     17.333333                 NaN  \n",
       "17  2012-09-01           146     16.222222                 NaN  \n",
       "8   2013-09-01            92     18.400000           14.666667  \n",
       "18  2014-10-01           168     16.800000           18.000000  \n",
       "16  2015-08-01           128     16.000000           16.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum = interact_sum.rename(columns={'Start_date':'base_date','Model':'Campaign_model','Series':'Campaign_series'})\n",
    "interact_sum = interact_sum.drop(['tmp','tmp_sum'], axis=1)\n",
    "interact_sum.head()\n",
    "# rename columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Campaign_model</th>\n",
       "      <th>Campaign_series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>base_date</th>\n",
       "      <th>Interact_avg</th>\n",
       "      <th>Interact_avg_last3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>18</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>16.222222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>14</td>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>14.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>22</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 User_id  Campaign_model Campaign_series  Interact_cnt  \\\n",
       "11  10000099596358040339               1               A            12   \n",
       "17  10000099596358040339               2               A            18   \n",
       "8   10000099596358040339               3               A            14   \n",
       "18  10000099596358040339               4               A            22   \n",
       "16  10000099596358040339               5               A            12   \n",
       "\n",
       "    base_date  Interact_avg  Interact_avg_last3  \n",
       "11 2011-10-01     17.333333                 NaN  \n",
       "17 2012-09-01     16.222222                 NaN  \n",
       "8  2013-09-01     18.400000           14.666667  \n",
       "18 2014-10-01     16.800000           18.000000  \n",
       "16 2015-08-01     16.000000           16.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact_sum['base_date'] = pd.to_datetime(interact_sum['base_date'])\n",
    "interact_sum = interact_sum.drop(['Interact_sum'], axis=1)\n",
    "interact_sum.head()\n",
    "#convert string to date time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>User_id</th>\n",
       "      <th>Year_of_birth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>base_date</th>\n",
       "      <th>Campaign_type</th>\n",
       "      <th>Campaign_model</th>\n",
       "      <th>Campaign_series</th>\n",
       "      <th>Campaign_id</th>\n",
       "      <th>Product_series</th>\n",
       "      <th>Product_model</th>\n",
       "      <th>First_purchase</th>\n",
       "      <th>Last_purchase</th>\n",
       "      <th>Purchase_count</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Last_product_model</th>\n",
       "      <th>Last_product_series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>Interact_avg</th>\n",
       "      <th>Interact_avg_last3</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>10000099596358040339</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>post</td>\n",
       "      <td>1.0</td>\n",
       "      <td>A</td>\n",
       "      <td>4615867097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>12</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-01</td>\n",
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       "      <td>12746050356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>post</td>\n",
       "      <td>2.0</td>\n",
       "      <td>A</td>\n",
       "      <td>10677889990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>18</td>\n",
       "      <td>16.222222</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>post</td>\n",
       "      <td>3.0</td>\n",
       "      <td>B</td>\n",
       "      <td>13144273147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "1  10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "2  10000099596358040339           1995     0.0 2012-05-01          post   \n",
       "3  10000099596358040339           1995     0.0 2012-09-01          post   \n",
       "4  10000099596358040339           1995     0.0 2013-04-01          post   \n",
       "\n",
       "   Campaign_model Campaign_series  Campaign_id Product_series Product_model  \\\n",
       "0             1.0               B  17928414209            NaN           NaN   \n",
       "1             1.0               A   4615867097            NaN           NaN   \n",
       "2             2.0               B  12746050356            NaN           NaN   \n",
       "3             2.0               A  10677889990            NaN           NaN   \n",
       "4             3.0               B  13144273147            NaN           NaN   \n",
       "\n",
       "  First_purchase Last_purchase  Purchase_count  Frequency  Recency  \\\n",
       "0     2001-05-22    2009-01-08               3     1394.0    813.0   \n",
       "1     2001-05-22    2009-01-08               3     1394.0    996.0   \n",
       "2     2001-05-22    2009-01-08               3     1394.0   1209.0   \n",
       "3     2001-05-22    2009-01-08               3     1394.0   1332.0   \n",
       "4     2001-05-22    2009-01-08               3     1394.0   1544.0   \n",
       "\n",
       "  Last_product_model Last_product_series  Interact_cnt  Interact_avg  \\\n",
       "0              Other               Other            22     20.500000   \n",
       "1              Other               Other            12     17.333333   \n",
       "2              Other               Other            22     18.000000   \n",
       "3              Other               Other            18     16.222222   \n",
       "4              Other               Other            22     22.000000   \n",
       "\n",
       "   Interact_avg_last3  \n",
       "0                 NaN  \n",
       "1                 NaN  \n",
       "2                 NaN  \n",
       "3                 NaN  \n",
       "4                22.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df[pd.notna(df['Campaign_model'])], interact_sum, on=['User_id','Campaign_model','Campaign_series','base_date'])\n",
    "df.head()\n",
    "#merge RFM vs interact parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Product_series','Product_model'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Purchase_date</th>\n",
       "      <th>Campaign_series</th>\n",
       "      <th>Campaign_model</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>2006-10-06</td>\n",
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       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>2007-10-11</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>2012-04-08</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3713087409444908179</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               User_id Purchase_date Campaign_series Campaign_model\n",
       "0  3713087409444908179    2006-10-06           Other          Other\n",
       "1  3713087409444908179    2007-10-11           Other          Other\n",
       "2  3713087409444908179    2012-04-08           Other          Other\n",
       "3  3713087409444908179    2014-04-04           Other          Other\n",
       "4  3713087409444908179    2016-11-07           Other          Other"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_df = purchase[['User_id','base_date','Product_series','Product_model']].rename(columns={'base_date':'Purchase_date','Product_series':'Campaign_series','Product_model':'Campaign_model'})\n",
    "tmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp_df['Purchase_date'] = pd.to_datetime(tmp_df['Purchase_date'])\n",
    "df['Campaign_model'] = df['Campaign_model'].astype(int).astype(str)\n",
    "tmp_df['Campaign_model'] = tmp_df['Campaign_model'].astype(str)\n",
    "df = pd.merge(df, tmp_df, on=['User_id','Campaign_series','Campaign_model'], how='left')\n",
    "#label extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Interact_cnt</th>\n",
       "      <th>Interact_avg</th>\n",
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       "      <th>0</th>\n",
       "      <td>10000099596358040339</td>\n",
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       "      <td>1394.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>post</td>\n",
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       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>12</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>post</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>12746050356</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>post</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>10677889990</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>18</td>\n",
       "      <td>16.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>post</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>13144273147</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>3</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "1  10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "2  10000099596358040339           1995     0.0 2012-05-01          post   \n",
       "3  10000099596358040339           1995     0.0 2012-09-01          post   \n",
       "4  10000099596358040339           1995     0.0 2013-04-01          post   \n",
       "\n",
       "  Campaign_model Campaign_series  Campaign_id First_purchase Last_purchase  \\\n",
       "0              1               B  17928414209     2001-05-22    2009-01-08   \n",
       "1              1               A   4615867097     2001-05-22    2009-01-08   \n",
       "2              2               B  12746050356     2001-05-22    2009-01-08   \n",
       "3              2               A  10677889990     2001-05-22    2009-01-08   \n",
       "4              3               B  13144273147     2001-05-22    2009-01-08   \n",
       "\n",
       "   Purchase_count  Frequency  Recency Last_product_model Last_product_series  \\\n",
       "0               3     1394.0    813.0              Other               Other   \n",
       "1               3     1394.0    996.0              Other               Other   \n",
       "2               3     1394.0   1209.0              Other               Other   \n",
       "3               3     1394.0   1332.0              Other               Other   \n",
       "4               3     1394.0   1544.0              Other               Other   \n",
       "\n",
       "   Interact_cnt  Interact_avg  Interact_avg_last3 Purchase_date  \n",
       "0            22     20.500000                 NaN           NaT  \n",
       "1            12     17.333333                 NaN           NaT  \n",
       "2            22     18.000000                 NaN           NaT  \n",
       "3            18     16.222222                 NaN           NaT  \n",
       "4            22     22.000000                22.0           NaT  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Delay_purchase'] = (df['Purchase_date'] - df['base_date']).dt.days\n",
    "df['Label'] = np.where((df['Delay_purchase']>=90)|(pd.isna(df['Delay_purchase'])), [0] * df.shape[0], [1] * df.shape[0])\n",
    "#label extration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Delay_purchase'] = (df['Purchase_date'] - df['base_date']).dt.days\n",
    "df['Label'] = np.where((df['Delay_purchase']>=90)|(pd.isna(df['Delay_purchase'])), [0] * df.shape[0], [1] * df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>User_id</th>\n",
       "      <th>Year_of_birth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>base_date</th>\n",
       "      <th>Campaign_type</th>\n",
       "      <th>Campaign_model</th>\n",
       "      <th>Campaign_series</th>\n",
       "      <th>Campaign_id</th>\n",
       "      <th>First_purchase</th>\n",
       "      <th>Last_purchase</th>\n",
       "      <th>...</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Last_product_model</th>\n",
       "      <th>Last_product_series</th>\n",
       "      <th>Interact_cnt</th>\n",
       "      <th>Interact_avg</th>\n",
       "      <th>Interact_avg_last3</th>\n",
       "      <th>Purchase_date</th>\n",
       "      <th>Delay_purchase</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>post</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>17928414209</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>813.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-10-01</td>\n",
       "      <td>post</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>4615867097</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>12</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>post</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>12746050356</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>post</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>10677889990</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>18</td>\n",
       "      <td>16.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000099596358040339</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-04-01</td>\n",
       "      <td>post</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>13144273147</td>\n",
       "      <td>2001-05-22</td>\n",
       "      <td>2009-01-08</td>\n",
       "      <td>...</td>\n",
       "      <td>1394.0</td>\n",
       "      <td>1544.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>22</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                User_id  Year_of_birth  Gender  base_date Campaign_type  \\\n",
       "0  10000099596358040339           1995     0.0 2011-04-01          post   \n",
       "1  10000099596358040339           1995     0.0 2011-10-01          post   \n",
       "2  10000099596358040339           1995     0.0 2012-05-01          post   \n",
       "3  10000099596358040339           1995     0.0 2012-09-01          post   \n",
       "4  10000099596358040339           1995     0.0 2013-04-01          post   \n",
       "\n",
       "  Campaign_model Campaign_series  Campaign_id First_purchase Last_purchase  \\\n",
       "0              1               B  17928414209     2001-05-22    2009-01-08   \n",
       "1              1               A   4615867097     2001-05-22    2009-01-08   \n",
       "2              2               B  12746050356     2001-05-22    2009-01-08   \n",
       "3              2               A  10677889990     2001-05-22    2009-01-08   \n",
       "4              3               B  13144273147     2001-05-22    2009-01-08   \n",
       "\n",
       "   ...  Frequency  Recency  Last_product_model Last_product_series  \\\n",
       "0  ...     1394.0    813.0               Other               Other   \n",
       "1  ...     1394.0    996.0               Other               Other   \n",
       "2  ...     1394.0   1209.0               Other               Other   \n",
       "3  ...     1394.0   1332.0               Other               Other   \n",
       "4  ...     1394.0   1544.0               Other               Other   \n",
       "\n",
       "  Interact_cnt  Interact_avg  Interact_avg_last3  Purchase_date  \\\n",
       "0           22     20.500000                 NaN            NaT   \n",
       "1           12     17.333333                 NaN            NaT   \n",
       "2           22     18.000000                 NaN            NaT   \n",
       "3           18     16.222222                 NaN            NaT   \n",
       "4           22     22.000000                22.0            NaT   \n",
       "\n",
       "  Delay_purchase  Label  \n",
       "0            NaN      0  \n",
       "1            NaN      0  \n",
       "2            NaN      0  \n",
       "3            NaN      0  \n",
       "4            NaN      0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Last_is_A'] = np.where(df['Last_product_series']=='A', 1, 0)\n",
    "df['Last_is_B'] = np.where(df['Last_product_series']=='B', 1, 0)\n",
    "df['Interact_recent_vs_last3'] = df['Interact_cnt'] / df['Interact_avg_last3']\n",
    "#one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['base_date'].dt.year - df['Year_of_birth'] #calculate user age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_id', 'Year_of_birth', 'Gender', 'base_date', 'Campaign_type',\n",
       "       'Campaign_model', 'Campaign_series', 'Campaign_id', 'First_purchase',\n",
       "       'Last_purchase', 'Purchase_count', 'Frequency', 'Recency',\n",
       "       'Last_product_model', 'Last_product_series', 'Interact_cnt',\n",
       "       'Interact_avg', 'Interact_avg_last3', 'Purchase_date', 'Delay_purchase',\n",
       "       'Label', 'Last_is_A', 'Last_is_B', 'Interact_recent_vs_last3', 'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_cols = ['Age','Purchase_count','Last_is_A','Last_is_B','Interact_cnt','Interact_avg']\n",
    "label_col = 'Label'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalization\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_A = df[df['Campaign_series']=='A']\n",
    "df_B = df[df['Campaign_series']=='B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((199999, 25), (199997, 25), (399996, 25))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_A.shape, df_B.shape, df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2011-10-01T00:00:00.000000000', '2012-09-01T00:00:00.000000000',\n",
       "       '2013-09-01T00:00:00.000000000', '2014-10-01T00:00:00.000000000',\n",
       "       '2015-08-01T00:00:00.000000000', '2016-09-01T00:00:00.000000000',\n",
       "       '2017-09-01T00:00:00.000000000', '2018-08-01T00:00:00.000000000',\n",
       "       '2019-08-01T00:00:00.000000000', '2020-08-01T00:00:00.000000000'],\n",
       "      dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_A['base_date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_A_train = df_A[df_A['base_date']<'2020-01-01']\n",
    "df_A_test = df_A[df_A['base_date']>'2020-01-01']\n",
    "df_B_train = df_B[df_B['base_date']<'2020-01-01']\n",
    "df_B_test = df_B[df_B['base_date']>'2020-01-01']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((179999, 25), (20000, 25), (199999, 25))"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_A_train.shape, df_A_test.shape, df_A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((179998, 25), (19999, 25), (199997, 25))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_B_train.shape, df_B_test.shape, df_B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_A_train = df_A_train[feature_cols]\n",
    "y_A_train = df_A_train[label_col]\n",
    "X_B_train = df_B_train[feature_cols]\n",
    "y_B_train = df_B_train[label_col]\n",
    "X_A_test = df_A_test[feature_cols]\n",
    "y_A_test = df_A_test[label_col]\n",
    "X_B_test = df_B_test[feature_cols]\n",
    "y_B_test = df_B_test[label_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_normalizer = MinMaxScaler().fit(X=X_A_train)\n",
    "B_normalizer = MinMaxScaler().fit(X=X_B_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_A_train_norm = A_normalizer.transform(X_A_train)\n",
    "X_A_test_norm = A_normalizer.transform(X_A_test)\n",
    "X_B_train_norm = B_normalizer.transform(X_B_train)\n",
    "X_B_test_norm = B_normalizer.transform(X_B_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_A = LogisticRegression().fit(X_A_train_norm, y_A_train)\n",
    "logreg_B = LogisticRegression().fit(X_B_train_norm, y_B_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_A_test_prediction = logreg_A.predict(X_A_test_norm)\n",
    "X_A_test_prediction_proba = logreg_A.predict_proba(X_A_test_norm)[:,1]\n",
    "X_B_test_prediction = logreg_B.predict(X_B_test_norm)\n",
    "X_B_test_prediction_proba = logreg_B.predict_proba(X_B_test_norm)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8570155560938364, 0.8439835513011886)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_A_test, X_A_test_prediction_proba), roc_auc_score(y_B_test, X_B_test_prediction_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huyenctt\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x16f6b3a1a50>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(logreg_A, X_A_test_norm, y_A_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\huyenctt\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x16f212bd7e0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(logreg_B, X_B_test_norm, y_B_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "A_res = pd.DataFrame({'actual': y_A_test, 'prediction': X_A_test_prediction_proba})\n",
    "A_pop = []\n",
    "A_tp = []\n",
    "A_tn = []\n",
    "A_fp = []\n",
    "A_fn = []\n",
    "for i in range(1, 100):\n",
    "    threshold = np.quantile(X_A_test_prediction_proba, i / 100)\n",
    "    A_pop.append(A_res[A_res['prediction'] >= threshold].shape[0])\n",
    "    A_tp.append(A_res[(A_res['prediction'] >= threshold) & (A_res['actual'] == 1)].shape[0])\n",
    "    A_tn.append(A_res[(A_res['prediction'] < threshold) & (A_res['actual'] == 0)].shape[0])\n",
    "    A_fp.append(A_res[(A_res['prediction'] >= threshold) & (A_res['actual'] == 0)].shape[0])\n",
    "    A_fn.append(A_res[(A_res['prediction'] < threshold) & (A_res['actual'] == 1)].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(A_pop), np.array(A_tp) / (np.array(A_tp) + np.array(A_fp)), label='precision')\n",
    "plt.plot(np.array(A_pop), np.array(A_tp) / (np.array(A_tp) + np.array(A_fn)), label='recall')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_res = pd.DataFrame({'actual': y_B_test, 'prediction': X_B_test_prediction_proba})\n",
    "B_pop = []\n",
    "B_tp = []\n",
    "B_tn = []\n",
    "B_fp = []\n",
    "B_fn = []\n",
    "for i in range(1, 100):\n",
    "    threshold = np.quantile(X_B_test_prediction_proba, i / 100)\n",
    "    B_pop.append(B_res[B_res['prediction'] >= threshold].shape[0])\n",
    "    B_tp.append(B_res[(B_res['prediction'] >= threshold) & (B_res['actual'] == 1)].shape[0])\n",
    "    B_tn.append(B_res[(B_res['prediction'] < threshold) & (B_res['actual'] == 0)].shape[0])\n",
    "    B_fp.append(B_res[(B_res['prediction'] >= threshold) & (B_res['actual'] == 0)].shape[0])\n",
    "    B_fn.append(B_res[(B_res['prediction'] < threshold) & (B_res['actual'] == 1)].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(B_pop), np.array(B_tp) / (np.array(B_tp) + np.array(B_fp)), label='precision')\n",
    "plt.plot(np.array(B_pop), np.array(B_tp) / (np.array(B_tp) + np.array(B_fn)), label='recall')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025485254852548527"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Label']==1].shape[0]/ df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Feature Engineering.ipynb",
   "provenance": []
  },
  "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}