IE492 - Pharma Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43c30b9a",
   "metadata": {},
   "source": [
    "# Promotion Planning in Pharma Supply Chains"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49df909c",
   "metadata": {},
   "source": [
    "**Boğaziçi University**<br>\n",
    "**Department of Industrial Engineering**<br>\n",
    "IE492 - Graduation Project<br>\n",
    "10.12.2023<br>\n",
    "\n",
    "**Contributors**<br>\n",
    "Ahmet Yiğit Doğan - 2018402105<br>\n",
    "Yasin Seçal - 2018402135<br>\n",
    "Barış Arslan - 2019402129<br>\n",
    "\n",
    "**Advisors**<br>\n",
    "Prof. Dr. Taner Bilgiç<br>\n",
    "Prof. Dr. Mustafa Gökçe Baydoğan<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0f7d5e",
   "metadata": {},
   "source": [
    "## 1. Project Description\n",
    "\n",
    "In this project transaction data from a pharmaceutical supply chain will be used in order to understand the basic dynamics of the industry. On the basis of data analysis, efficient prediction methods will be designed and promotion strategies will be recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ea43adc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import random\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from dateutil.parser import parse\n",
    "\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.tsa.stattools as sts\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "import scipy.stats\n",
    "import pylab\n",
    "\n",
    "from utils import *\n",
    "from pulp import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7e9be9",
   "metadata": {},
   "source": [
    "## 2. Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30fb2faf",
   "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>Date</th>\n",
       "      <th>LocationId</th>\n",
       "      <th>MainDistributorId</th>\n",
       "      <th>DistributorId</th>\n",
       "      <th>PackageId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>City</th>\n",
       "      <th>District</th>\n",
       "      <th>BrickId</th>\n",
       "      <th>PaidQty</th>\n",
       "      <th>FreeQty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>2873114</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>615</td>\n",
       "      <td>38</td>\n",
       "      <td>Kütahya</td>\n",
       "      <td>MERKEZ</td>\n",
       "      <td>430002</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-16</td>\n",
       "      <td>2872993</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>615</td>\n",
       "      <td>38</td>\n",
       "      <td>Kütahya</td>\n",
       "      <td>ALTINTAŞ</td>\n",
       "      <td>430101</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-18</td>\n",
       "      <td>2918671</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>615</td>\n",
       "      <td>38</td>\n",
       "      <td>Afyon</td>\n",
       "      <td>MERKEZ</td>\n",
       "      <td>30002</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-19</td>\n",
       "      <td>2919833</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>615</td>\n",
       "      <td>38</td>\n",
       "      <td>Uşak</td>\n",
       "      <td>MERKEZ</td>\n",
       "      <td>640001</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>2873114</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>616</td>\n",
       "      <td>38</td>\n",
       "      <td>Kütahya</td>\n",
       "      <td>MERKEZ</td>\n",
       "      <td>430002</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  LocationId  MainDistributorId  DistributorId  PackageId   \n",
       "0  2023-01-02     2873114            2879223        2878715        615  \\\n",
       "1  2023-01-16     2872993            2879223        2878715        615   \n",
       "2  2023-01-18     2918671            2879223        2878715        615   \n",
       "3  2023-01-19     2919833            2879223        2878715        615   \n",
       "4  2023-01-02     2873114            2879223        2878715        616   \n",
       "\n",
       "   ProductId     City  District  BrickId  PaidQty  FreeQty  \n",
       "0         38  Kütahya    MERKEZ   430002       24       12  \n",
       "1         38  Kütahya  ALTINTAŞ   430101        7        3  \n",
       "2         38    Afyon    MERKEZ    30002        7        3  \n",
       "3         38     Uşak    MERKEZ   640001        7        3  \n",
       "4         38  Kütahya    MERKEZ   430002       24       12  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data = pd.read_parquet(\"Data/sale_data.parquet\")\n",
    "promo_data = pd.read_excel(\"Data/promo_data.xlsx\")\n",
    "\n",
    "df_sales = pd.DataFrame(sales_data)\n",
    "df_promo = pd.DataFrame(promo_data)\n",
    "\n",
    "df_sales['Date'] = pd.to_datetime(df_sales['Date']).dt.date\n",
    "\n",
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98bf2cee",
   "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>MProductId</th>\n",
       "      <th>BeginDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>PaidQty</th>\n",
       "      <th>FreeQty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2022-01-31 23:30:00</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2022-01-31 23:30:00</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>2022-01-31 23:30:00</td>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>2022-02-28 23:30:00</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>2022-02-28 23:30:00</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MProductId  BeginDate             EndDate  PaidQty  FreeQty\n",
       "0          14 2022-01-01 2022-01-31 23:30:00        6        2\n",
       "1          14 2022-01-01 2022-01-31 23:30:00       12        5\n",
       "2          14 2022-01-01 2022-01-31 23:30:00       24       12\n",
       "3          14 2022-02-01 2022-02-28 23:30:00        6        2\n",
       "4          14 2022-02-01 2022-02-28 23:30:00       12        5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_promo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fe8c3d",
   "metadata": {},
   "source": [
    "### 2.1. Merged Dataset (Sales Data + Promotion Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4837ee23",
   "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>Date</th>\n",
       "      <th>LocationId</th>\n",
       "      <th>MainDistributorId</th>\n",
       "      <th>DistributorId</th>\n",
       "      <th>PackageId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>City</th>\n",
       "      <th>District</th>\n",
       "      <th>BrickId</th>\n",
       "      <th>PaidQty</th>\n",
       "      <th>FreeQty</th>\n",
       "      <th>isPromoted</th>\n",
       "      <th>Discount</th>\n",
       "      <th>BeginDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>Promo</th>\n",
       "      <th>DStep</th>\n",
       "      <th>WeekStartDate</th>\n",
       "      <th>TotalQty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>2854453</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>568</td>\n",
       "      <td>29</td>\n",
       "      <td>Afyon</td>\n",
       "      <td>DİNAR</td>\n",
       "      <td>30601</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>2867763</td>\n",
       "      <td>2879249</td>\n",
       "      <td>2878795</td>\n",
       "      <td>568</td>\n",
       "      <td>29</td>\n",
       "      <td>Gaziantep</td>\n",
       "      <td>ŞEHİTKAMİL</td>\n",
       "      <td>270202</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>2877675</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>568</td>\n",
       "      <td>29</td>\n",
       "      <td>Uşak</td>\n",
       "      <td>BANAZ</td>\n",
       "      <td>640101</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>2918985</td>\n",
       "      <td>2879249</td>\n",
       "      <td>2878795</td>\n",
       "      <td>568</td>\n",
       "      <td>29</td>\n",
       "      <td>Şanlıurfa</td>\n",
       "      <td>HALİLİYE</td>\n",
       "      <td>630001</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>2854545</td>\n",
       "      <td>2879223</td>\n",
       "      <td>2878715</td>\n",
       "      <td>568</td>\n",
       "      <td>29</td>\n",
       "      <td>Afyon</td>\n",
       "      <td>MERKEZ</td>\n",
       "      <td>30001</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931106</th>\n",
       "      <td>2023-07-25</td>\n",
       "      <td>2869831</td>\n",
       "      <td>2879266</td>\n",
       "      <td>2878725</td>\n",
       "      <td>47280</td>\n",
       "      <td>148</td>\n",
       "      <td>İzmir</td>\n",
       "      <td>KONAK</td>\n",
       "      <td>350817</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-07-24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931107</th>\n",
       "      <td>2023-07-25</td>\n",
       "      <td>2870106</td>\n",
       "      <td>2879266</td>\n",
       "      <td>2878725</td>\n",
       "      <td>36261</td>\n",
       "      <td>148</td>\n",
       "      <td>İzmir</td>\n",
       "      <td>ÖDEMİŞ</td>\n",
       "      <td>351302</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>4-1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>2023-07-24</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931108</th>\n",
       "      <td>2023-07-26</td>\n",
       "      <td>2869422</td>\n",
       "      <td>2879266</td>\n",
       "      <td>2878725</td>\n",
       "      <td>47280</td>\n",
       "      <td>148</td>\n",
       "      <td>İzmir</td>\n",
       "      <td>URLA</td>\n",
       "      <td>351401</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>10-1</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>2023-07-24</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931109</th>\n",
       "      <td>2023-07-26</td>\n",
       "      <td>2870353</td>\n",
       "      <td>2879266</td>\n",
       "      <td>2878725</td>\n",
       "      <td>47280</td>\n",
       "      <td>148</td>\n",
       "      <td>İzmir</td>\n",
       "      <td>KARABAĞLAR</td>\n",
       "      <td>350813</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-07-24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8931110</th>\n",
       "      <td>2023-07-27</td>\n",
       "      <td>2869246</td>\n",
       "      <td>2879266</td>\n",
       "      <td>2878725</td>\n",
       "      <td>47279</td>\n",
       "      <td>148</td>\n",
       "      <td>İzmir</td>\n",
       "      <td>BUCA</td>\n",
       "      <td>350301</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2023-07-24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8931111 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date  LocationId  MainDistributorId  DistributorId  PackageId   \n",
       "0       2023-01-02     2854453            2879223        2878715        568  \\\n",
       "1       2022-06-29     2867763            2879249        2878795        568   \n",
       "2       2023-01-04     2877675            2879223        2878715        568   \n",
       "3       2022-06-29     2918985            2879249        2878795        568   \n",
       "4       2023-01-04     2854545            2879223        2878715        568   \n",
       "...            ...         ...                ...            ...        ...   \n",
       "8931106 2023-07-25     2869831            2879266        2878725      47280   \n",
       "8931107 2023-07-25     2870106            2879266        2878725      36261   \n",
       "8931108 2023-07-26     2869422            2879266        2878725      47280   \n",
       "8931109 2023-07-26     2870353            2879266        2878725      47280   \n",
       "8931110 2023-07-27     2869246            2879266        2878725      47279   \n",
       "\n",
       "         ProductId       City    District  BrickId  PaidQty  FreeQty   \n",
       "0               29      Afyon       DİNAR    30601       10        0  \\\n",
       "1               29  Gaziantep  ŞEHİTKAMİL   270202       15        0   \n",
       "2               29       Uşak       BANAZ   640101        3        0   \n",
       "3               29  Şanlıurfa    HALİLİYE   630001       15        0   \n",
       "4               29      Afyon      MERKEZ    30001       20        0   \n",
       "...            ...        ...         ...      ...      ...      ...   \n",
       "8931106        148      İzmir       KONAK   350817        1        0   \n",
       "8931107        148      İzmir      ÖDEMİŞ   351302        4        1   \n",
       "8931108        148      İzmir        URLA   351401       10        1   \n",
       "8931109        148      İzmir  KARABAĞLAR   350813        3        0   \n",
       "8931110        148      İzmir        BUCA   350301        6        0   \n",
       "\n",
       "         isPromoted  Discount  BeginDate    EndDate Promo     DStep   \n",
       "0                 0  0.000000        NaT        NaT  None  0.000000  \\\n",
       "1                 0  0.000000        NaT        NaT  None  0.000000   \n",
       "2                 0  0.000000        NaT        NaT  None  0.000000   \n",
       "3                 0  0.000000        NaT        NaT  None  0.000000   \n",
       "4                 0  0.000000        NaT        NaT  None  0.000000   \n",
       "...             ...       ...        ...        ...   ...       ...   \n",
       "8931106           0  0.000000        NaT        NaT  None  0.000000   \n",
       "8931107           1  0.200000 2023-07-01 2023-08-01   4-1  0.200000   \n",
       "8931108           1  0.090909 2023-07-01 2023-08-01  10-1  0.090909   \n",
       "8931109           0  0.000000        NaT        NaT  None  0.000000   \n",
       "8931110           0  0.000000        NaT        NaT  None  0.000000   \n",
       "\n",
       "        WeekStartDate  TotalQty  \n",
       "0          2023-01-02        10  \n",
       "1          2022-06-27        15  \n",
       "2          2023-01-02         3  \n",
       "3          2022-06-27        15  \n",
       "4          2023-01-02        20  \n",
       "...               ...       ...  \n",
       "8931106    2023-07-24         1  \n",
       "8931107    2023-07-24         5  \n",
       "8931108    2023-07-24        11  \n",
       "8931109    2023-07-24         3  \n",
       "8931110    2023-07-24         6  \n",
       "\n",
       "[8931111 rows x 19 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"Data/Sales_Data_v2.parquet\")\n",
    "df[\"TotalQty\"] = df[\"PaidQty\"] + df[\"FreeQty\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7262ada5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales = df\n",
    "sales = sales.groupby([sales['Date'].dt.date])['TotalQty'].sum()\n",
    "sales.plot(figsize=(20,5), title=\"TotalQty\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33980702",
   "metadata": {},
   "source": [
    "## 3. Demand Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36ac06d",
   "metadata": {},
   "source": [
    "### 3.1. Forecasting Model 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b080954",
   "metadata": {},
   "source": [
    "No aggregation, all products, single predictor (Free Quantity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68fba92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th>  <td>   0.565</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.565</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.158e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:44:33</td>     <th>  Log-Likelihood:    </th> <td>-3.2987e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>8931111</td>     <th>  AIC:               </th>  <td>6.597e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>8931109</td>     <th>  BIC:               </th>  <td>6.597e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    5.1531</td> <td>    0.003</td> <td> 1570.571</td> <td> 0.000</td> <td>    5.147</td> <td>    5.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FreeQty</th>   <td>    2.6839</td> <td>    0.001</td> <td> 3402.640</td> <td> 0.000</td> <td>    2.682</td> <td>    2.685</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>25864001.967</td> <th>  Durbin-Watson:     </th>      <td>   1.684</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>    <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>18404874264023.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>             <td>38.759</td>    <th>  Prob(JB):          </th>      <td>    0.00</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>7035.228</td>   <th>  Cond. No.          </th>      <td>    4.20</td>     \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &         0.565       \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &         0.565       \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1.158e+07       \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} &         0.00        \\\\\n",
       "\\textbf{Time:}             &     20:44:33     & \\textbf{  Log-Likelihood:    } &    -3.2987e+07      \\\\\n",
       "\\textbf{No. Observations:} &     8931111      & \\textbf{  AIC:               } &     6.597e+07       \\\\\n",
       "\\textbf{Df Residuals:}     &     8931109      & \\textbf{  BIC:               } &     6.597e+07       \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &                     \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &                     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &       5.1531  &        0.003     &  1570.571  &         0.000        &        5.147    &        5.160     \\\\\n",
       "\\textbf{FreeQty}   &       2.6839  &        0.001     &  3402.640  &         0.000        &        2.682    &        2.685     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 25864001.967 & \\textbf{  Durbin-Watson:     } &         1.684       \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000    & \\textbf{  Jarque-Bera (JB):  } & 18404874264023.770  \\\\\n",
       "\\textbf{Skew:}          &    38.759    & \\textbf{  Prob(JB):          } &          0.00       \\\\\n",
       "\\textbf{Kurtosis:}      &   7035.228   & \\textbf{  Cond. No.          } &          4.20       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.565\n",
       "Model:                            OLS   Adj. R-squared:                  0.565\n",
       "Method:                 Least Squares   F-statistic:                 1.158e+07\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        20:44:33   Log-Likelihood:            -3.2987e+07\n",
       "No. Observations:             8931111   AIC:                         6.597e+07\n",
       "Df Residuals:                 8931109   BIC:                         6.597e+07\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.1531      0.003   1570.571      0.000       5.147       5.160\n",
       "FreeQty        2.6839      0.001   3402.640      0.000       2.682       2.685\n",
       "==============================================================================\n",
       "Omnibus:                 25864001.967   Durbin-Watson:                   1.684\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):   18404874264023.770\n",
       "Skew:                          38.759   Prob(JB):                         0.00\n",
       "Kurtosis:                    7035.228   Cond. No.                         4.20\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit = smf.ols('TotalQty ~ FreeQty', df).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a60f291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df.FreeQty, df.PaidQty, '.')\n",
    "ax.plot(df.FreeQty, fit.predict())\n",
    "ax.set(ylabel=\"TotalQty\", xlabel=\"FreeQty\")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a5b3c3e",
   "metadata": {},
   "source": [
    "### 3.2. Forecasting Model 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a17f59a",
   "metadata": {},
   "source": [
    "No aggregation, all products, multiple predictors (Free Quantity, is Promoted, Discount Ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e57f591a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th>  <td>   0.616</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.616</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>4.783e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:00</td>     <th>  Log-Likelihood:    </th> <td>-3.2421e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>8931111</td>     <th>  AIC:               </th>  <td>6.484e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>8931107</td>     <th>  BIC:               </th>  <td>6.484e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>    4.3427</td> <td>    0.003</td> <td> 1300.469</td> <td> 0.000</td> <td>    4.336</td> <td>    4.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FreeQty</th>    <td>    2.7883</td> <td>    0.001</td> <td> 3395.149</td> <td> 0.000</td> <td>    2.787</td> <td>    2.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>isPromoted</th> <td>   16.4981</td> <td>    0.015</td> <td> 1098.697</td> <td> 0.000</td> <td>   16.469</td> <td>   16.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Discount</th>   <td>  -69.7688</td> <td>    0.077</td> <td> -901.883</td> <td> 0.000</td> <td>  -69.920</td> <td>  -69.617</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>27281386.926</td> <th>  Durbin-Watson:     </th>      <td>   1.732</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>    <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>30288662149601.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>             <td>44.974</td>    <th>  Prob(JB):          </th>      <td>    0.00</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>9024.346</td>   <th>  Cond. No.          </th>      <td>    107.</td>     \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &         0.616       \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &         0.616       \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     4.783e+06       \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} &         0.00        \\\\\n",
       "\\textbf{Time:}             &     20:45:00     & \\textbf{  Log-Likelihood:    } &    -3.2421e+07      \\\\\n",
       "\\textbf{No. Observations:} &     8931111      & \\textbf{  AIC:               } &     6.484e+07       \\\\\n",
       "\\textbf{Df Residuals:}     &     8931107      & \\textbf{  BIC:               } &     6.484e+07       \\\\\n",
       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &                     \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &                     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                    & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}  &       4.3427  &        0.003     &  1300.469  &         0.000        &        4.336    &        4.349     \\\\\n",
       "\\textbf{FreeQty}    &       2.7883  &        0.001     &  3395.149  &         0.000        &        2.787    &        2.790     \\\\\n",
       "\\textbf{isPromoted} &      16.4981  &        0.015     &  1098.697  &         0.000        &       16.469    &       16.528     \\\\\n",
       "\\textbf{Discount}   &     -69.7688  &        0.077     &  -901.883  &         0.000        &      -69.920    &      -69.617     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 27281386.926 & \\textbf{  Durbin-Watson:     } &         1.732       \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000    & \\textbf{  Jarque-Bera (JB):  } & 30288662149601.188  \\\\\n",
       "\\textbf{Skew:}          &    44.974    & \\textbf{  Prob(JB):          } &          0.00       \\\\\n",
       "\\textbf{Kurtosis:}      &   9024.346   & \\textbf{  Cond. No.          } &          107.       \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.616\n",
       "Model:                            OLS   Adj. R-squared:                  0.616\n",
       "Method:                 Least Squares   F-statistic:                 4.783e+06\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        20:45:00   Log-Likelihood:            -3.2421e+07\n",
       "No. Observations:             8931111   AIC:                         6.484e+07\n",
       "Df Residuals:                 8931107   BIC:                         6.484e+07\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      4.3427      0.003   1300.469      0.000       4.336       4.349\n",
       "FreeQty        2.7883      0.001   3395.149      0.000       2.787       2.790\n",
       "isPromoted    16.4981      0.015   1098.697      0.000      16.469      16.528\n",
       "Discount     -69.7688      0.077   -901.883      0.000     -69.920     -69.617\n",
       "==============================================================================\n",
       "Omnibus:                 27281386.926   Durbin-Watson:                   1.732\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):   30288662149601.188\n",
       "Skew:                          44.974   Prob(JB):                         0.00\n",
       "Kurtosis:                    9024.346   Cond. No.                         107.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit = smf.ols('TotalQty ~ FreeQty + isPromoted + Discount', df).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275a19db",
   "metadata": {},
   "source": [
    "### 3.3. Forecasting Model 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543e9bf7",
   "metadata": {},
   "source": [
    "Daily aggregation, all products, multiple predictors (Free Quantity, weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ecde54e",
   "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>Date</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>TotalQty</th>\n",
       "      <th>FreeQty</th>\n",
       "      <th>weekday</th>\n",
       "      <th>index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>14</td>\n",
       "      <td>4911</td>\n",
       "      <td>1117</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>15</td>\n",
       "      <td>29604</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>16</td>\n",
       "      <td>8034</td>\n",
       "      <td>1709</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>20</td>\n",
       "      <td>25184</td>\n",
       "      <td>991</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>21</td>\n",
       "      <td>19689</td>\n",
       "      <td>1031</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12067</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>72</td>\n",
       "      <td>2098</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12068</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>76</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12069</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>138</td>\n",
       "      <td>281</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>12069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12070</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>148</td>\n",
       "      <td>418</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>12070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12071</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12072 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  ProductId  TotalQty  FreeQty  weekday  index\n",
       "0      2022-02-01         14      4911     1117        1      0\n",
       "1      2022-02-01         15     29604        9        1      1\n",
       "2      2022-02-01         16      8034     1709        1      2\n",
       "3      2022-02-01         20     25184      991        1      3\n",
       "4      2022-02-01         21     19689     1031        1      4\n",
       "...           ...        ...       ...      ...      ...    ...\n",
       "12067  2023-09-30         72      2098        0        5  12067\n",
       "12068  2023-09-30         76       270        0        5  12068\n",
       "12069  2023-09-30        138       281       10        5  12069\n",
       "12070  2023-09-30        148       418       41        5  12070\n",
       "12071  2023-09-30        163         1        0        5  12071\n",
       "\n",
       "[12072 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prod = df\n",
    "df_prod = df_prod.groupby([df_prod['Date'].dt.date, df_prod[\"ProductId\"]])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "df_prod = df_prod.reset_index()\n",
    "\n",
    "df_prod = df_prod.assign(\n",
    "    weekday = df_prod.Date.apply(lambda x: x.weekday()),\n",
    "    index = np.arange(len(df_prod)))\n",
    "\n",
    "df_prod"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7bfd48",
   "metadata": {},
   "source": [
    "### 3.3.1. Daily Aggregation for All Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a47ff88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th> <td>   0.875</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.874</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2109.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>3.42e-273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:07</td>     <th>  Log-Likelihood:    </th> <td> -6996.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   607</td>      <th>  AIC:               </th> <td>1.400e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   604</td>      <th>  BIC:               </th> <td>1.401e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 5.435e+04</td> <td> 3100.657</td> <td>   17.529</td> <td> 0.000</td> <td> 4.83e+04</td> <td> 6.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FreeQty</th>   <td>    7.9546</td> <td>    0.177</td> <td>   44.832</td> <td> 0.000</td> <td>    7.606</td> <td>    8.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekday</th>   <td>-6666.6834</td> <td>  625.572</td> <td>  -10.657</td> <td> 0.000</td> <td>-7895.244</td> <td>-5438.123</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>36.379</td> <th>  Durbin-Watson:     </th> <td>   1.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  61.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.426</td> <th>  Prob(JB):          </th> <td>4.80e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.303</td> <th>  Cond. No.          </th> <td>3.33e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.33e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &     0.875   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.874   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     2109.   \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} & 3.42e-273   \\\\\n",
       "\\textbf{Time:}             &     20:45:07     & \\textbf{  Log-Likelihood:    } &   -6996.8   \\\\\n",
       "\\textbf{No. Observations:} &         607      & \\textbf{  AIC:               } & 1.400e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &         604      & \\textbf{  BIC:               } & 1.401e+04   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    5.435e+04  &     3100.657     &    17.529  &         0.000        &     4.83e+04    &     6.04e+04     \\\\\n",
       "\\textbf{FreeQty}   &       7.9546  &        0.177     &    44.832  &         0.000        &        7.606    &        8.303     \\\\\n",
       "\\textbf{weekday}   &   -6666.6834  &      625.572     &   -10.657  &         0.000        &    -7895.244    &    -5438.123     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 36.379 & \\textbf{  Durbin-Watson:     } &    1.039  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   61.335  \\\\\n",
       "\\textbf{Skew:}          &  0.426 & \\textbf{  Prob(JB):          } & 4.80e-14  \\\\\n",
       "\\textbf{Kurtosis:}      &  4.303 & \\textbf{  Cond. No.          } & 3.33e+04  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 3.33e+04. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.875\n",
       "Model:                            OLS   Adj. R-squared:                  0.874\n",
       "Method:                 Least Squares   F-statistic:                     2109.\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):          3.42e-273\n",
       "Time:                        20:45:07   Log-Likelihood:                -6996.8\n",
       "No. Observations:                 607   AIC:                         1.400e+04\n",
       "Df Residuals:                     604   BIC:                         1.401e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   5.435e+04   3100.657     17.529      0.000    4.83e+04    6.04e+04\n",
       "FreeQty        7.9546      0.177     44.832      0.000       7.606       8.303\n",
       "weekday    -6666.6834    625.572    -10.657      0.000   -7895.244   -5438.123\n",
       "==============================================================================\n",
       "Omnibus:                       36.379   Durbin-Watson:                   1.039\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               61.335\n",
       "Skew:                           0.426   Prob(JB):                     4.80e-14\n",
       "Kurtosis:                       4.303   Cond. No.                     3.33e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.33e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = df.groupby([df['Date'].dt.date])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "\n",
    "d = d.assign(\n",
    "    weekday = d.index.to_series().apply(lambda x: x.weekday()),\n",
    "    index = np.arange(len(d)))\n",
    "\n",
    "fit = smf.ols('TotalQty ~ FreeQty + weekday', d).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a08674c",
   "metadata": {},
   "source": [
    "### 3.3.2. Daily Aggregation for Single Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba87abd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th> <td>   0.998</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.998</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.899e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:07</td>     <th>  Log-Likelihood:    </th> <td> -4999.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   607</td>      <th>  AIC:               </th> <td>1.001e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   604</td>      <th>  BIC:               </th> <td>1.002e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 2031.4547</td> <td>   72.774</td> <td>   27.915</td> <td> 0.000</td> <td> 1888.535</td> <td> 2174.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FreeQty</th>   <td>   13.4477</td> <td>    0.022</td> <td>  598.953</td> <td> 0.000</td> <td>   13.404</td> <td>   13.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekday</th>   <td> -275.1967</td> <td>   19.069</td> <td>  -14.432</td> <td> 0.000</td> <td> -312.646</td> <td> -237.748</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>334.936</td> <th>  Durbin-Watson:     </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>4692.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 2.126</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>15.940</td>  <th>  Cond. No.          </th> <td>3.68e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.68e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &     0.998   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.998   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } & 1.899e+05   \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
       "\\textbf{Time:}             &     20:45:07     & \\textbf{  Log-Likelihood:    } &   -4999.8   \\\\\n",
       "\\textbf{No. Observations:} &         607      & \\textbf{  AIC:               } & 1.001e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &         604      & \\textbf{  BIC:               } & 1.002e+04   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    2031.4547  &       72.774     &    27.915  &         0.000        &     1888.535    &     2174.375     \\\\\n",
       "\\textbf{FreeQty}   &      13.4477  &        0.022     &   598.953  &         0.000        &       13.404    &       13.492     \\\\\n",
       "\\textbf{weekday}   &    -275.1967  &       19.069     &   -14.432  &         0.000        &     -312.646    &     -237.748     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 334.936 & \\textbf{  Durbin-Watson:     } &    0.945  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 4692.368  \\\\\n",
       "\\textbf{Skew:}          &   2.126 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  15.940 & \\textbf{  Cond. No.          } & 3.68e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 3.68e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.998\n",
       "Model:                            OLS   Adj. R-squared:                  0.998\n",
       "Method:                 Least Squares   F-statistic:                 1.899e+05\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        20:45:07   Log-Likelihood:                -4999.8\n",
       "No. Observations:                 607   AIC:                         1.001e+04\n",
       "Df Residuals:                     604   BIC:                         1.002e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   2031.4547     72.774     27.915      0.000    1888.535    2174.375\n",
       "FreeQty       13.4477      0.022    598.953      0.000      13.404      13.492\n",
       "weekday     -275.1967     19.069    -14.432      0.000    -312.646    -237.748\n",
       "==============================================================================\n",
       "Omnibus:                      334.936   Durbin-Watson:                   0.945\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4692.368\n",
       "Skew:                           2.126   Prob(JB):                         0.00\n",
       "Kurtosis:                      15.940   Cond. No.                     3.68e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.68e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = df_prod[df_prod.ProductId == 39]\n",
    "fit = smf.ols('TotalQty ~ FreeQty + weekday', d).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c74e15",
   "metadata": {},
   "source": [
    "### 3.4. Forecasting Model 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7be5a1d",
   "metadata": {},
   "source": [
    "Daily aggregation, all products, multiple predictors (Free Quantity and dayofmonth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a55e138",
   "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>TotalQty</th>\n",
       "      <th>FreeQty</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>245864</td>\n",
       "      <td>22070</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-02</th>\n",
       "      <td>207367</td>\n",
       "      <td>22039</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-03</th>\n",
       "      <td>175478</td>\n",
       "      <td>18156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-04</th>\n",
       "      <td>140891</td>\n",
       "      <td>13635</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-05</th>\n",
       "      <td>44064</td>\n",
       "      <td>3045</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-26</th>\n",
       "      <td>155265</td>\n",
       "      <td>18971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27</th>\n",
       "      <td>78473</td>\n",
       "      <td>6245</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-28</th>\n",
       "      <td>68954</td>\n",
       "      <td>4579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-29</th>\n",
       "      <td>61609</td>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>18430</td>\n",
       "      <td>768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>607 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TotalQty  FreeQty  1  2  3  4  5  6  7  8  ...  22  23  24  25   \n",
       "Date                                                   ...                   \n",
       "2022-02-01    245864    22070  1  0  0  0  0  0  0  0  ...   0   0   0   0  \\\n",
       "2022-02-02    207367    22039  0  1  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "2022-02-03    175478    18156  0  0  1  0  0  0  0  0  ...   0   0   0   0   \n",
       "2022-02-04    140891    13635  0  0  0  1  0  0  0  0  ...   0   0   0   0   \n",
       "2022-02-05     44064     3045  0  0  0  0  1  0  0  0  ...   0   0   0   0   \n",
       "...              ...      ... .. .. .. .. .. .. .. ..  ...  ..  ..  ..  ..   \n",
       "2023-09-26    155265    18971  0  0  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "2023-09-27     78473     6245  0  0  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "2023-09-28     68954     4579  0  0  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "2023-09-29     61609     3600  0  0  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "2023-09-30     18430      768  0  0  0  0  0  0  0  0  ...   0   0   0   0   \n",
       "\n",
       "            26  27  28  29  30  31  \n",
       "Date                                \n",
       "2022-02-01   0   0   0   0   0   0  \n",
       "2022-02-02   0   0   0   0   0   0  \n",
       "2022-02-03   0   0   0   0   0   0  \n",
       "2022-02-04   0   0   0   0   0   0  \n",
       "2022-02-05   0   0   0   0   0   0  \n",
       "...         ..  ..  ..  ..  ..  ..  \n",
       "2023-09-26   1   0   0   0   0   0  \n",
       "2023-09-27   0   1   0   0   0   0  \n",
       "2023-09-28   0   0   1   0   0   0  \n",
       "2023-09-29   0   0   0   1   0   0  \n",
       "2023-09-30   0   0   0   0   1   0  \n",
       "\n",
       "[607 rows x 33 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = df_prod.groupby([\"Date\"])[[\"TotalQty\", \"FreeQty\"]].sum().reset_index()\n",
    "d[\"Date\"] = pd.to_datetime(d.Date)\n",
    "d[\"dayofmonth\"] = d.Date.dt.day\n",
    "d = d[[\"Date\", \"TotalQty\", \"FreeQty\", \"dayofmonth\"]].set_index(\"Date\")\n",
    "one_hot = pd.get_dummies(d['dayofmonth']).astype(int)\n",
    "d = d.drop('dayofmonth', axis = 1)\n",
    "d = d.join(one_hot)\n",
    "#.mul(d[\"FreeQty\"]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82057255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th> <td>   0.859</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.851</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   112.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>8.37e-222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:10</td>     <th>  Log-Likelihood:    </th> <td> -7033.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   607</td>      <th>  AIC:               </th> <td>1.413e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   575</td>      <th>  BIC:               </th> <td>1.427e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    31</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FreeQty</th> <td>    9.1253</td> <td>    0.157</td> <td>   58.025</td> <td> 0.000</td> <td>    8.816</td> <td>    9.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"1\")</th>  <td> 3.866e+04</td> <td> 6155.551</td> <td>    6.280</td> <td> 0.000</td> <td> 2.66e+04</td> <td> 5.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"2\")</th>  <td> 2.097e+04</td> <td> 6130.107</td> <td>    3.421</td> <td> 0.001</td> <td> 8929.263</td> <td>  3.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"3\")</th>  <td> 2.136e+04</td> <td> 6130.311</td> <td>    3.484</td> <td> 0.001</td> <td> 9318.011</td> <td> 3.34e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"4\")</th>  <td> 1.911e+04</td> <td> 6134.314</td> <td>    3.115</td> <td> 0.002</td> <td> 7057.024</td> <td> 3.12e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"5\")</th>  <td> 2.008e+04</td> <td> 6149.080</td> <td>    3.266</td> <td> 0.001</td> <td> 8007.389</td> <td> 3.22e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"6\")</th>  <td> 2.024e+04</td> <td> 6141.358</td> <td>    3.296</td> <td> 0.001</td> <td> 8179.276</td> <td> 3.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"7\")</th>  <td>  2.43e+04</td> <td> 6159.503</td> <td>    3.944</td> <td> 0.000</td> <td> 1.22e+04</td> <td> 3.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"8\")</th>  <td> 2.086e+04</td> <td> 6215.775</td> <td>    3.357</td> <td> 0.001</td> <td> 8655.383</td> <td> 3.31e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"9\")</th>  <td> 2.033e+04</td> <td> 6182.146</td> <td>    3.289</td> <td> 0.001</td> <td> 8188.537</td> <td> 3.25e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"10\")</th> <td> 2.066e+04</td> <td> 6166.419</td> <td>    3.351</td> <td> 0.001</td> <td> 8550.570</td> <td> 3.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"11\")</th> <td>  1.17e+04</td> <td> 6154.999</td> <td>    1.902</td> <td> 0.058</td> <td> -384.407</td> <td> 2.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"12\")</th> <td> 1.616e+04</td> <td> 6126.864</td> <td>    2.637</td> <td> 0.009</td> <td> 4123.117</td> <td> 2.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"13\")</th> <td> 2.594e+04</td> <td> 6136.016</td> <td>    4.228</td> <td> 0.000</td> <td> 1.39e+04</td> <td>  3.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"14\")</th> <td> 3.592e+04</td> <td> 6137.643</td> <td>    5.852</td> <td> 0.000</td> <td> 2.39e+04</td> <td>  4.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"15\")</th> <td>  2.28e+04</td> <td> 6121.656</td> <td>    3.724</td> <td> 0.000</td> <td> 1.08e+04</td> <td> 3.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"16\")</th> <td> 2.894e+04</td> <td> 6107.151</td> <td>    4.739</td> <td> 0.000</td> <td> 1.69e+04</td> <td> 4.09e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"17\")</th> <td> 3.518e+04</td> <td> 6093.120</td> <td>    5.774</td> <td> 0.000</td> <td> 2.32e+04</td> <td> 4.71e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"18\")</th> <td> 3.329e+04</td> <td> 6080.618</td> <td>    5.475</td> <td> 0.000</td> <td> 2.13e+04</td> <td> 4.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"19\")</th> <td> 2.818e+04</td> <td> 6116.036</td> <td>    4.607</td> <td> 0.000</td> <td> 1.62e+04</td> <td> 4.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"20\")</th> <td> 2.928e+04</td> <td> 6078.187</td> <td>    4.818</td> <td> 0.000</td> <td> 1.73e+04</td> <td> 4.12e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"21\")</th> <td> 3.208e+04</td> <td> 6083.349</td> <td>    5.274</td> <td> 0.000</td> <td> 2.01e+04</td> <td>  4.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"22\")</th> <td>   2.5e+04</td> <td> 6128.680</td> <td>    4.079</td> <td> 0.000</td> <td>  1.3e+04</td> <td>  3.7e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"23\")</th> <td> 2.926e+04</td> <td> 6112.156</td> <td>    4.787</td> <td> 0.000</td> <td> 1.73e+04</td> <td> 4.13e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"24\")</th> <td> 2.703e+04</td> <td> 6089.245</td> <td>    4.439</td> <td> 0.000</td> <td> 1.51e+04</td> <td>  3.9e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"25\")</th> <td> 2.976e+04</td> <td> 6074.781</td> <td>    4.900</td> <td> 0.000</td> <td> 1.78e+04</td> <td> 4.17e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"26\")</th> <td> 2.794e+04</td> <td> 6054.717</td> <td>    4.614</td> <td> 0.000</td> <td>  1.6e+04</td> <td> 3.98e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"27\")</th> <td> 2.433e+04</td> <td> 6087.013</td> <td>    3.998</td> <td> 0.000</td> <td> 1.24e+04</td> <td> 3.63e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"28\")</th> <td>  2.47e+04</td> <td> 6056.181</td> <td>    4.078</td> <td> 0.000</td> <td> 1.28e+04</td> <td> 3.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"29\")</th> <td> 2.262e+04</td> <td> 6369.024</td> <td>    3.552</td> <td> 0.000</td> <td> 1.01e+04</td> <td> 3.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"30\")</th> <td> 1.779e+04</td> <td> 6352.413</td> <td>    2.801</td> <td> 0.005</td> <td> 5313.512</td> <td> 3.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"31\")</th> <td> 2.281e+04</td> <td> 8129.839</td> <td>    2.806</td> <td> 0.005</td> <td> 6845.999</td> <td> 3.88e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.258</td> <th>  Durbin-Watson:     </th> <td>   1.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.016</td> <th>  Jarque-Bera (JB):  </th> <td>  10.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.134</td> <th>  Prob(JB):          </th> <td> 0.00455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.595</td> <th>  Cond. No.          </th> <td>8.99e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 8.99e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &     0.859   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.851   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     112.8   \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} & 8.37e-222   \\\\\n",
       "\\textbf{Time:}             &     20:45:10     & \\textbf{  Log-Likelihood:    } &   -7033.2   \\\\\n",
       "\\textbf{No. Observations:} &         607      & \\textbf{  AIC:               } & 1.413e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &         575      & \\textbf{  BIC:               } & 1.427e+04   \\\\\n",
       "\\textbf{Df Model:}         &          31      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                 & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{FreeQty} &       9.1253  &        0.157     &    58.025  &         0.000        &        8.816    &        9.434     \\\\\n",
       "\\textbf{Q(\"1\")}  &    3.866e+04  &     6155.551     &     6.280  &         0.000        &     2.66e+04    &     5.07e+04     \\\\\n",
       "\\textbf{Q(\"2\")}  &    2.097e+04  &     6130.107     &     3.421  &         0.001        &     8929.263    &      3.3e+04     \\\\\n",
       "\\textbf{Q(\"3\")}  &    2.136e+04  &     6130.311     &     3.484  &         0.001        &     9318.011    &     3.34e+04     \\\\\n",
       "\\textbf{Q(\"4\")}  &    1.911e+04  &     6134.314     &     3.115  &         0.002        &     7057.024    &     3.12e+04     \\\\\n",
       "\\textbf{Q(\"5\")}  &    2.008e+04  &     6149.080     &     3.266  &         0.001        &     8007.389    &     3.22e+04     \\\\\n",
       "\\textbf{Q(\"6\")}  &    2.024e+04  &     6141.358     &     3.296  &         0.001        &     8179.276    &     3.23e+04     \\\\\n",
       "\\textbf{Q(\"7\")}  &     2.43e+04  &     6159.503     &     3.944  &         0.000        &     1.22e+04    &     3.64e+04     \\\\\n",
       "\\textbf{Q(\"8\")}  &    2.086e+04  &     6215.775     &     3.357  &         0.001        &     8655.383    &     3.31e+04     \\\\\n",
       "\\textbf{Q(\"9\")}  &    2.033e+04  &     6182.146     &     3.289  &         0.001        &     8188.537    &     3.25e+04     \\\\\n",
       "\\textbf{Q(\"10\")} &    2.066e+04  &     6166.419     &     3.351  &         0.001        &     8550.570    &     3.28e+04     \\\\\n",
       "\\textbf{Q(\"11\")} &     1.17e+04  &     6154.999     &     1.902  &         0.058        &     -384.407    &     2.38e+04     \\\\\n",
       "\\textbf{Q(\"12\")} &    1.616e+04  &     6126.864     &     2.637  &         0.009        &     4123.117    &     2.82e+04     \\\\\n",
       "\\textbf{Q(\"13\")} &    2.594e+04  &     6136.016     &     4.228  &         0.000        &     1.39e+04    &      3.8e+04     \\\\\n",
       "\\textbf{Q(\"14\")} &    3.592e+04  &     6137.643     &     5.852  &         0.000        &     2.39e+04    &      4.8e+04     \\\\\n",
       "\\textbf{Q(\"15\")} &     2.28e+04  &     6121.656     &     3.724  &         0.000        &     1.08e+04    &     3.48e+04     \\\\\n",
       "\\textbf{Q(\"16\")} &    2.894e+04  &     6107.151     &     4.739  &         0.000        &     1.69e+04    &     4.09e+04     \\\\\n",
       "\\textbf{Q(\"17\")} &    3.518e+04  &     6093.120     &     5.774  &         0.000        &     2.32e+04    &     4.71e+04     \\\\\n",
       "\\textbf{Q(\"18\")} &    3.329e+04  &     6080.618     &     5.475  &         0.000        &     2.13e+04    &     4.52e+04     \\\\\n",
       "\\textbf{Q(\"19\")} &    2.818e+04  &     6116.036     &     4.607  &         0.000        &     1.62e+04    &     4.02e+04     \\\\\n",
       "\\textbf{Q(\"20\")} &    2.928e+04  &     6078.187     &     4.818  &         0.000        &     1.73e+04    &     4.12e+04     \\\\\n",
       "\\textbf{Q(\"21\")} &    3.208e+04  &     6083.349     &     5.274  &         0.000        &     2.01e+04    &      4.4e+04     \\\\\n",
       "\\textbf{Q(\"22\")} &      2.5e+04  &     6128.680     &     4.079  &         0.000        &      1.3e+04    &      3.7e+04     \\\\\n",
       "\\textbf{Q(\"23\")} &    2.926e+04  &     6112.156     &     4.787  &         0.000        &     1.73e+04    &     4.13e+04     \\\\\n",
       "\\textbf{Q(\"24\")} &    2.703e+04  &     6089.245     &     4.439  &         0.000        &     1.51e+04    &      3.9e+04     \\\\\n",
       "\\textbf{Q(\"25\")} &    2.976e+04  &     6074.781     &     4.900  &         0.000        &     1.78e+04    &     4.17e+04     \\\\\n",
       "\\textbf{Q(\"26\")} &    2.794e+04  &     6054.717     &     4.614  &         0.000        &      1.6e+04    &     3.98e+04     \\\\\n",
       "\\textbf{Q(\"27\")} &    2.433e+04  &     6087.013     &     3.998  &         0.000        &     1.24e+04    &     3.63e+04     \\\\\n",
       "\\textbf{Q(\"28\")} &     2.47e+04  &     6056.181     &     4.078  &         0.000        &     1.28e+04    &     3.66e+04     \\\\\n",
       "\\textbf{Q(\"29\")} &    2.262e+04  &     6369.024     &     3.552  &         0.000        &     1.01e+04    &     3.51e+04     \\\\\n",
       "\\textbf{Q(\"30\")} &    1.779e+04  &     6352.413     &     2.801  &         0.005        &     5313.512    &     3.03e+04     \\\\\n",
       "\\textbf{Q(\"31\")} &    2.281e+04  &     8129.839     &     2.806  &         0.005        &     6845.999    &     3.88e+04     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  8.258 & \\textbf{  Durbin-Watson:     } &    1.126  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.016 & \\textbf{  Jarque-Bera (JB):  } &   10.784  \\\\\n",
       "\\textbf{Skew:}          &  0.134 & \\textbf{  Prob(JB):          } &  0.00455  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.595 & \\textbf{  Cond. No.          } & 8.99e+04  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 8.99e+04. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.859\n",
       "Model:                            OLS   Adj. R-squared:                  0.851\n",
       "Method:                 Least Squares   F-statistic:                     112.8\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):          8.37e-222\n",
       "Time:                        20:45:10   Log-Likelihood:                -7033.2\n",
       "No. Observations:                 607   AIC:                         1.413e+04\n",
       "Df Residuals:                     575   BIC:                         1.427e+04\n",
       "Df Model:                          31                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "FreeQty        9.1253      0.157     58.025      0.000       8.816       9.434\n",
       "Q(\"1\")      3.866e+04   6155.551      6.280      0.000    2.66e+04    5.07e+04\n",
       "Q(\"2\")      2.097e+04   6130.107      3.421      0.001    8929.263     3.3e+04\n",
       "Q(\"3\")      2.136e+04   6130.311      3.484      0.001    9318.011    3.34e+04\n",
       "Q(\"4\")      1.911e+04   6134.314      3.115      0.002    7057.024    3.12e+04\n",
       "Q(\"5\")      2.008e+04   6149.080      3.266      0.001    8007.389    3.22e+04\n",
       "Q(\"6\")      2.024e+04   6141.358      3.296      0.001    8179.276    3.23e+04\n",
       "Q(\"7\")       2.43e+04   6159.503      3.944      0.000    1.22e+04    3.64e+04\n",
       "Q(\"8\")      2.086e+04   6215.775      3.357      0.001    8655.383    3.31e+04\n",
       "Q(\"9\")      2.033e+04   6182.146      3.289      0.001    8188.537    3.25e+04\n",
       "Q(\"10\")     2.066e+04   6166.419      3.351      0.001    8550.570    3.28e+04\n",
       "Q(\"11\")      1.17e+04   6154.999      1.902      0.058    -384.407    2.38e+04\n",
       "Q(\"12\")     1.616e+04   6126.864      2.637      0.009    4123.117    2.82e+04\n",
       "Q(\"13\")     2.594e+04   6136.016      4.228      0.000    1.39e+04     3.8e+04\n",
       "Q(\"14\")     3.592e+04   6137.643      5.852      0.000    2.39e+04     4.8e+04\n",
       "Q(\"15\")      2.28e+04   6121.656      3.724      0.000    1.08e+04    3.48e+04\n",
       "Q(\"16\")     2.894e+04   6107.151      4.739      0.000    1.69e+04    4.09e+04\n",
       "Q(\"17\")     3.518e+04   6093.120      5.774      0.000    2.32e+04    4.71e+04\n",
       "Q(\"18\")     3.329e+04   6080.618      5.475      0.000    2.13e+04    4.52e+04\n",
       "Q(\"19\")     2.818e+04   6116.036      4.607      0.000    1.62e+04    4.02e+04\n",
       "Q(\"20\")     2.928e+04   6078.187      4.818      0.000    1.73e+04    4.12e+04\n",
       "Q(\"21\")     3.208e+04   6083.349      5.274      0.000    2.01e+04     4.4e+04\n",
       "Q(\"22\")       2.5e+04   6128.680      4.079      0.000     1.3e+04     3.7e+04\n",
       "Q(\"23\")     2.926e+04   6112.156      4.787      0.000    1.73e+04    4.13e+04\n",
       "Q(\"24\")     2.703e+04   6089.245      4.439      0.000    1.51e+04     3.9e+04\n",
       "Q(\"25\")     2.976e+04   6074.781      4.900      0.000    1.78e+04    4.17e+04\n",
       "Q(\"26\")     2.794e+04   6054.717      4.614      0.000     1.6e+04    3.98e+04\n",
       "Q(\"27\")     2.433e+04   6087.013      3.998      0.000    1.24e+04    3.63e+04\n",
       "Q(\"28\")      2.47e+04   6056.181      4.078      0.000    1.28e+04    3.66e+04\n",
       "Q(\"29\")     2.262e+04   6369.024      3.552      0.000    1.01e+04    3.51e+04\n",
       "Q(\"30\")     1.779e+04   6352.413      2.801      0.005    5313.512    3.03e+04\n",
       "Q(\"31\")     2.281e+04   8129.839      2.806      0.005    6845.999    3.88e+04\n",
       "==============================================================================\n",
       "Omnibus:                        8.258   Durbin-Watson:                   1.126\n",
       "Prob(Omnibus):                  0.016   Jarque-Bera (JB):               10.784\n",
       "Skew:                           0.134   Prob(JB):                      0.00455\n",
       "Kurtosis:                       3.595   Cond. No.                     8.99e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.99e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns = [str(i) for i in list(d.columns)]\n",
    "equation = 'TotalQty ~ FreeQty + Q(\"1\") + Q(\"2\") + Q(\"3\") + Q(\"4\") + Q(\"5\") + Q(\"6\") + Q(\"7\") + Q(\"8\") + Q(\"9\") + Q(\"10\") + Q(\"11\") + Q(\"12\") + Q(\"13\") + Q(\"14\") + Q(\"15\") + Q(\"16\") + Q(\"17\") + Q(\"18\") + Q(\"19\") + Q(\"20\") + Q(\"21\") + Q(\"22\") + Q(\"23\") + Q(\"24\") + Q(\"25\") + Q(\"26\") + Q(\"27\") + Q(\"28\") + Q(\"29\") + Q(\"30\") + Q(\"31\") - 1'\n",
    "# Model: all predictors (31 days and FreeQty) and no intecept\n",
    "fit = smf.ols(equation, d).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b57d4d0",
   "metadata": {},
   "source": [
    "### 3.5. Coefficients of Each Product Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dabdf5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "products = [14,  16,  20,  21,  29,  30,  32,  38,  39,  41,  49, 138, 148]\n",
    "df_prod = df\n",
    "df_prod = df_prod.groupby([df_prod['Date'].dt.date, df_prod[\"ProductId\"]])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "equation = 'TotalQty ~ FreeQty + Q(\"1\") + Q(\"2\") + Q(\"3\") + Q(\"4\") + Q(\"5\") + Q(\"6\") + Q(\"7\") + Q(\"8\") + Q(\"9\") + Q(\"10\") + Q(\"11\") + Q(\"12\") + Q(\"13\") + Q(\"14\") + Q(\"15\") + Q(\"16\") + Q(\"17\") + Q(\"18\") + Q(\"19\") + Q(\"20\") + Q(\"21\") + Q(\"22\") + Q(\"23\") + Q(\"24\") + Q(\"25\") + Q(\"26\") + Q(\"27\") + Q(\"28\") + Q(\"29\") + Q(\"30\") + Q(\"31\") - 1'\n",
    "\n",
    "df_prod = df_prod.reset_index()\n",
    "\n",
    "# Partition-----------------\n",
    "# For cross-validation\n",
    "start = dt.date(2023, 9, 1)\n",
    "end = dt.date(2023, 9, 30)\n",
    "out = df_prod[(df_prod[\"Date\"] >= start) & (df_prod[\"Date\"] <= end)]\n",
    "df_prod = df_prod[(df_prod[\"Date\"] >= end) | (df_prod[\"Date\"] <= start)]\n",
    "#---------------------------\n",
    "\n",
    "stats = []\n",
    "coeff = []\n",
    "pvalues = []\n",
    "\n",
    "for p in products:\n",
    "    d = df_prod[df_prod.ProductId == p]\n",
    "    d[\"Date\"] = pd.to_datetime(d.Date)\n",
    "    d[\"dayofmonth\"] = d.Date.dt.day\n",
    "    d = d[[\"Date\", \"TotalQty\", \"FreeQty\", \"dayofmonth\"]].set_index(\"Date\")\n",
    "    one_hot = pd.get_dummies(d['dayofmonth']).astype(int)\n",
    "    d = d.drop('dayofmonth', axis = 1)\n",
    "    d = d.join(one_hot)\n",
    "    d.columns = [str(i) for i in list(d.columns)]\n",
    "    fit = smf.ols(equation, d).fit()\n",
    "    stats.append([fit.rsquared, fit.rsquared_adj, fit.aic])\n",
    "    coeff.append(fit.params)\n",
    "    pvalues.append(fit.pvalues)\n",
    "\n",
    "stats = pd.DataFrame(stats, columns = [\"R-Squared\", \"Adj R-Squared\", \"AIC\"])\n",
    "coeff = pd.DataFrame(coeff)\n",
    "\n",
    "stats.index = products\n",
    "coeff.index = products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "711ec6b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Promo Sensitivity > 0.01:  0\n",
      "Base Demand > 0.1 Percentage:  0.035\n",
      "Base Demand > 0.01 Percentage:  0.201\n"
     ]
    }
   ],
   "source": [
    "pvalues = pd.DataFrame(pvalues, index=products)\n",
    "print(\"Promo Sensitivity > 0.01: \", pvalues.ge(0.01).sum()[\"FreeQty\"])\n",
    "print(\"Base Demand > 0.1 Percentage: \", round(pvalues.ge(0.1).sum().sum() / (len(products)*31), 3))\n",
    "print(\"Base Demand > 0.01 Percentage: \", round(pvalues.ge(0.01).sum().sum() / (len(products)*31), 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0cc6510",
   "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>R-Squared</th>\n",
       "      <th>Adj R-Squared</th>\n",
       "      <th>AIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.964</td>\n",
       "      <td>0.962</td>\n",
       "      <td>7896.915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.948</td>\n",
       "      <td>0.945</td>\n",
       "      <td>8492.507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.851</td>\n",
       "      <td>0.843</td>\n",
       "      <td>10615.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.828</td>\n",
       "      <td>0.818</td>\n",
       "      <td>10470.676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.500</td>\n",
       "      <td>0.471</td>\n",
       "      <td>11481.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.987</td>\n",
       "      <td>0.986</td>\n",
       "      <td>9268.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.983</td>\n",
       "      <td>0.982</td>\n",
       "      <td>8023.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.981</td>\n",
       "      <td>0.980</td>\n",
       "      <td>9235.921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.998</td>\n",
       "      <td>0.998</td>\n",
       "      <td>9742.279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.980</td>\n",
       "      <td>0.979</td>\n",
       "      <td>8234.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.948</td>\n",
       "      <td>0.946</td>\n",
       "      <td>8105.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.870</td>\n",
       "      <td>0.863</td>\n",
       "      <td>7853.531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.936</td>\n",
       "      <td>0.933</td>\n",
       "      <td>10207.555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     R-Squared  Adj R-Squared        AIC\n",
       "14       0.964          0.962   7896.915\n",
       "16       0.948          0.945   8492.507\n",
       "20       0.851          0.843  10615.739\n",
       "21       0.828          0.818  10470.676\n",
       "29       0.500          0.471  11481.387\n",
       "30       0.987          0.986   9268.301\n",
       "32       0.983          0.982   8023.545\n",
       "38       0.981          0.980   9235.921\n",
       "39       0.998          0.998   9742.279\n",
       "41       0.980          0.979   8234.433\n",
       "49       0.948          0.946   8105.521\n",
       "138      0.870          0.863   7853.531\n",
       "148      0.936          0.933  10207.555"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(stats, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3b13c78",
   "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>FreeQty</th>\n",
       "      <th>Q(\"1\")</th>\n",
       "      <th>Q(\"2\")</th>\n",
       "      <th>Q(\"3\")</th>\n",
       "      <th>Q(\"4\")</th>\n",
       "      <th>Q(\"5\")</th>\n",
       "      <th>Q(\"6\")</th>\n",
       "      <th>Q(\"7\")</th>\n",
       "      <th>Q(\"8\")</th>\n",
       "      <th>Q(\"9\")</th>\n",
       "      <th>...</th>\n",
       "      <th>Q(\"22\")</th>\n",
       "      <th>Q(\"23\")</th>\n",
       "      <th>Q(\"24\")</th>\n",
       "      <th>Q(\"25\")</th>\n",
       "      <th>Q(\"26\")</th>\n",
       "      <th>Q(\"27\")</th>\n",
       "      <th>Q(\"28\")</th>\n",
       "      <th>Q(\"29\")</th>\n",
       "      <th>Q(\"30\")</th>\n",
       "      <th>Q(\"31\")</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.44</td>\n",
       "      <td>198.35</td>\n",
       "      <td>120.19</td>\n",
       "      <td>101.54</td>\n",
       "      <td>89.31</td>\n",
       "      <td>126.44</td>\n",
       "      <td>56.44</td>\n",
       "      <td>111.58</td>\n",
       "      <td>30.85</td>\n",
       "      <td>115.07</td>\n",
       "      <td>...</td>\n",
       "      <td>184.71</td>\n",
       "      <td>169.16</td>\n",
       "      <td>196.27</td>\n",
       "      <td>198.32</td>\n",
       "      <td>200.98</td>\n",
       "      <td>209.28</td>\n",
       "      <td>220.87</td>\n",
       "      <td>172.21</td>\n",
       "      <td>129.14</td>\n",
       "      <td>169.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.73</td>\n",
       "      <td>404.13</td>\n",
       "      <td>217.49</td>\n",
       "      <td>216.17</td>\n",
       "      <td>169.02</td>\n",
       "      <td>211.74</td>\n",
       "      <td>186.19</td>\n",
       "      <td>207.23</td>\n",
       "      <td>143.08</td>\n",
       "      <td>261.42</td>\n",
       "      <td>...</td>\n",
       "      <td>273.82</td>\n",
       "      <td>343.25</td>\n",
       "      <td>323.85</td>\n",
       "      <td>329.40</td>\n",
       "      <td>366.66</td>\n",
       "      <td>333.90</td>\n",
       "      <td>317.61</td>\n",
       "      <td>249.90</td>\n",
       "      <td>181.77</td>\n",
       "      <td>271.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15.64</td>\n",
       "      <td>3447.32</td>\n",
       "      <td>2289.07</td>\n",
       "      <td>1920.47</td>\n",
       "      <td>1172.31</td>\n",
       "      <td>1455.49</td>\n",
       "      <td>1687.54</td>\n",
       "      <td>2041.38</td>\n",
       "      <td>1019.43</td>\n",
       "      <td>1655.60</td>\n",
       "      <td>...</td>\n",
       "      <td>2058.17</td>\n",
       "      <td>2144.65</td>\n",
       "      <td>2092.17</td>\n",
       "      <td>2131.16</td>\n",
       "      <td>2393.26</td>\n",
       "      <td>1944.03</td>\n",
       "      <td>2322.67</td>\n",
       "      <td>2032.54</td>\n",
       "      <td>1376.54</td>\n",
       "      <td>1998.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.17</td>\n",
       "      <td>4050.87</td>\n",
       "      <td>2248.26</td>\n",
       "      <td>2466.20</td>\n",
       "      <td>1771.21</td>\n",
       "      <td>1703.54</td>\n",
       "      <td>1732.02</td>\n",
       "      <td>1922.86</td>\n",
       "      <td>1366.37</td>\n",
       "      <td>1700.51</td>\n",
       "      <td>...</td>\n",
       "      <td>2229.46</td>\n",
       "      <td>2178.97</td>\n",
       "      <td>2331.43</td>\n",
       "      <td>2447.47</td>\n",
       "      <td>2433.29</td>\n",
       "      <td>2199.99</td>\n",
       "      <td>2251.01</td>\n",
       "      <td>1718.90</td>\n",
       "      <td>1409.32</td>\n",
       "      <td>2143.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.60</td>\n",
       "      <td>8788.10</td>\n",
       "      <td>6030.73</td>\n",
       "      <td>6805.57</td>\n",
       "      <td>6932.68</td>\n",
       "      <td>7210.75</td>\n",
       "      <td>7191.13</td>\n",
       "      <td>7608.71</td>\n",
       "      <td>9329.36</td>\n",
       "      <td>6968.30</td>\n",
       "      <td>...</td>\n",
       "      <td>6709.21</td>\n",
       "      <td>5735.04</td>\n",
       "      <td>6242.80</td>\n",
       "      <td>5312.74</td>\n",
       "      <td>4174.69</td>\n",
       "      <td>5639.05</td>\n",
       "      <td>5478.41</td>\n",
       "      <td>5352.52</td>\n",
       "      <td>5311.40</td>\n",
       "      <td>6018.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2.66</td>\n",
       "      <td>1544.73</td>\n",
       "      <td>1036.66</td>\n",
       "      <td>993.61</td>\n",
       "      <td>936.46</td>\n",
       "      <td>857.84</td>\n",
       "      <td>813.18</td>\n",
       "      <td>730.80</td>\n",
       "      <td>552.61</td>\n",
       "      <td>719.76</td>\n",
       "      <td>...</td>\n",
       "      <td>807.34</td>\n",
       "      <td>828.62</td>\n",
       "      <td>768.05</td>\n",
       "      <td>932.75</td>\n",
       "      <td>789.47</td>\n",
       "      <td>767.91</td>\n",
       "      <td>762.67</td>\n",
       "      <td>637.08</td>\n",
       "      <td>408.45</td>\n",
       "      <td>629.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>7.52</td>\n",
       "      <td>229.98</td>\n",
       "      <td>132.83</td>\n",
       "      <td>123.24</td>\n",
       "      <td>126.33</td>\n",
       "      <td>123.35</td>\n",
       "      <td>66.69</td>\n",
       "      <td>153.69</td>\n",
       "      <td>86.16</td>\n",
       "      <td>158.62</td>\n",
       "      <td>...</td>\n",
       "      <td>190.22</td>\n",
       "      <td>196.49</td>\n",
       "      <td>216.94</td>\n",
       "      <td>248.24</td>\n",
       "      <td>242.19</td>\n",
       "      <td>163.32</td>\n",
       "      <td>197.88</td>\n",
       "      <td>167.84</td>\n",
       "      <td>73.45</td>\n",
       "      <td>236.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2.75</td>\n",
       "      <td>836.65</td>\n",
       "      <td>479.30</td>\n",
       "      <td>605.83</td>\n",
       "      <td>509.67</td>\n",
       "      <td>502.57</td>\n",
       "      <td>422.71</td>\n",
       "      <td>396.06</td>\n",
       "      <td>141.05</td>\n",
       "      <td>346.21</td>\n",
       "      <td>...</td>\n",
       "      <td>428.94</td>\n",
       "      <td>466.79</td>\n",
       "      <td>557.60</td>\n",
       "      <td>535.45</td>\n",
       "      <td>563.72</td>\n",
       "      <td>526.59</td>\n",
       "      <td>518.13</td>\n",
       "      <td>380.50</td>\n",
       "      <td>296.12</td>\n",
       "      <td>533.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>13.52</td>\n",
       "      <td>2206.60</td>\n",
       "      <td>1488.31</td>\n",
       "      <td>1497.80</td>\n",
       "      <td>1214.00</td>\n",
       "      <td>1146.52</td>\n",
       "      <td>1113.32</td>\n",
       "      <td>1415.80</td>\n",
       "      <td>1114.73</td>\n",
       "      <td>1359.06</td>\n",
       "      <td>...</td>\n",
       "      <td>989.82</td>\n",
       "      <td>1045.90</td>\n",
       "      <td>1076.08</td>\n",
       "      <td>873.92</td>\n",
       "      <td>1015.28</td>\n",
       "      <td>844.81</td>\n",
       "      <td>1060.91</td>\n",
       "      <td>929.62</td>\n",
       "      <td>757.07</td>\n",
       "      <td>1122.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2.87</td>\n",
       "      <td>298.21</td>\n",
       "      <td>222.41</td>\n",
       "      <td>236.34</td>\n",
       "      <td>248.42</td>\n",
       "      <td>257.99</td>\n",
       "      <td>235.48</td>\n",
       "      <td>237.44</td>\n",
       "      <td>227.55</td>\n",
       "      <td>179.47</td>\n",
       "      <td>...</td>\n",
       "      <td>207.97</td>\n",
       "      <td>193.93</td>\n",
       "      <td>231.55</td>\n",
       "      <td>213.59</td>\n",
       "      <td>209.27</td>\n",
       "      <td>172.22</td>\n",
       "      <td>201.81</td>\n",
       "      <td>163.11</td>\n",
       "      <td>118.68</td>\n",
       "      <td>235.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4.93</td>\n",
       "      <td>308.70</td>\n",
       "      <td>168.04</td>\n",
       "      <td>169.53</td>\n",
       "      <td>124.16</td>\n",
       "      <td>165.33</td>\n",
       "      <td>31.46</td>\n",
       "      <td>233.57</td>\n",
       "      <td>123.78</td>\n",
       "      <td>166.43</td>\n",
       "      <td>...</td>\n",
       "      <td>216.86</td>\n",
       "      <td>249.82</td>\n",
       "      <td>247.99</td>\n",
       "      <td>250.90</td>\n",
       "      <td>274.32</td>\n",
       "      <td>255.84</td>\n",
       "      <td>255.33</td>\n",
       "      <td>211.63</td>\n",
       "      <td>149.36</td>\n",
       "      <td>256.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>17.93</td>\n",
       "      <td>303.21</td>\n",
       "      <td>196.99</td>\n",
       "      <td>214.48</td>\n",
       "      <td>155.06</td>\n",
       "      <td>177.41</td>\n",
       "      <td>197.55</td>\n",
       "      <td>231.28</td>\n",
       "      <td>207.24</td>\n",
       "      <td>205.74</td>\n",
       "      <td>...</td>\n",
       "      <td>146.16</td>\n",
       "      <td>236.13</td>\n",
       "      <td>280.06</td>\n",
       "      <td>219.30</td>\n",
       "      <td>218.58</td>\n",
       "      <td>184.03</td>\n",
       "      <td>187.47</td>\n",
       "      <td>171.52</td>\n",
       "      <td>125.95</td>\n",
       "      <td>-22.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.51</td>\n",
       "      <td>1720.22</td>\n",
       "      <td>1528.49</td>\n",
       "      <td>1458.05</td>\n",
       "      <td>678.54</td>\n",
       "      <td>677.64</td>\n",
       "      <td>938.21</td>\n",
       "      <td>1239.51</td>\n",
       "      <td>1104.47</td>\n",
       "      <td>1289.70</td>\n",
       "      <td>...</td>\n",
       "      <td>564.99</td>\n",
       "      <td>859.22</td>\n",
       "      <td>1145.34</td>\n",
       "      <td>1170.86</td>\n",
       "      <td>924.11</td>\n",
       "      <td>1045.28</td>\n",
       "      <td>998.52</td>\n",
       "      <td>894.16</td>\n",
       "      <td>808.24</td>\n",
       "      <td>905.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FreeQty   Q(\"1\")   Q(\"2\")   Q(\"3\")   Q(\"4\")   Q(\"5\")   Q(\"6\")   Q(\"7\")   \n",
       "14      4.44   198.35   120.19   101.54    89.31   126.44    56.44   111.58  \\\n",
       "16      4.73   404.13   217.49   216.17   169.02   211.74   186.19   207.23   \n",
       "20     15.64  3447.32  2289.07  1920.47  1172.31  1455.49  1687.54  2041.38   \n",
       "21     10.17  4050.87  2248.26  2466.20  1771.21  1703.54  1732.02  1922.86   \n",
       "29     10.60  8788.10  6030.73  6805.57  6932.68  7210.75  7191.13  7608.71   \n",
       "30      2.66  1544.73  1036.66   993.61   936.46   857.84   813.18   730.80   \n",
       "32      7.52   229.98   132.83   123.24   126.33   123.35    66.69   153.69   \n",
       "38      2.75   836.65   479.30   605.83   509.67   502.57   422.71   396.06   \n",
       "39     13.52  2206.60  1488.31  1497.80  1214.00  1146.52  1113.32  1415.80   \n",
       "41      2.87   298.21   222.41   236.34   248.42   257.99   235.48   237.44   \n",
       "49      4.93   308.70   168.04   169.53   124.16   165.33    31.46   233.57   \n",
       "138    17.93   303.21   196.99   214.48   155.06   177.41   197.55   231.28   \n",
       "148     6.51  1720.22  1528.49  1458.05   678.54   677.64   938.21  1239.51   \n",
       "\n",
       "      Q(\"8\")   Q(\"9\")  ...  Q(\"22\")  Q(\"23\")  Q(\"24\")  Q(\"25\")  Q(\"26\")   \n",
       "14     30.85   115.07  ...   184.71   169.16   196.27   198.32   200.98  \\\n",
       "16    143.08   261.42  ...   273.82   343.25   323.85   329.40   366.66   \n",
       "20   1019.43  1655.60  ...  2058.17  2144.65  2092.17  2131.16  2393.26   \n",
       "21   1366.37  1700.51  ...  2229.46  2178.97  2331.43  2447.47  2433.29   \n",
       "29   9329.36  6968.30  ...  6709.21  5735.04  6242.80  5312.74  4174.69   \n",
       "30    552.61   719.76  ...   807.34   828.62   768.05   932.75   789.47   \n",
       "32     86.16   158.62  ...   190.22   196.49   216.94   248.24   242.19   \n",
       "38    141.05   346.21  ...   428.94   466.79   557.60   535.45   563.72   \n",
       "39   1114.73  1359.06  ...   989.82  1045.90  1076.08   873.92  1015.28   \n",
       "41    227.55   179.47  ...   207.97   193.93   231.55   213.59   209.27   \n",
       "49    123.78   166.43  ...   216.86   249.82   247.99   250.90   274.32   \n",
       "138   207.24   205.74  ...   146.16   236.13   280.06   219.30   218.58   \n",
       "148  1104.47  1289.70  ...   564.99   859.22  1145.34  1170.86   924.11   \n",
       "\n",
       "     Q(\"27\")  Q(\"28\")  Q(\"29\")  Q(\"30\")  Q(\"31\")  \n",
       "14    209.28   220.87   172.21   129.14   169.70  \n",
       "16    333.90   317.61   249.90   181.77   271.43  \n",
       "20   1944.03  2322.67  2032.54  1376.54  1998.98  \n",
       "21   2199.99  2251.01  1718.90  1409.32  2143.76  \n",
       "29   5639.05  5478.41  5352.52  5311.40  6018.65  \n",
       "30    767.91   762.67   637.08   408.45   629.36  \n",
       "32    163.32   197.88   167.84    73.45   236.85  \n",
       "38    526.59   518.13   380.50   296.12   533.59  \n",
       "39    844.81  1060.91   929.62   757.07  1122.26  \n",
       "41    172.22   201.81   163.11   118.68   235.83  \n",
       "49    255.84   255.33   211.63   149.36   256.45  \n",
       "138   184.03   187.47   171.52   125.95   -22.85  \n",
       "148  1045.28   998.52   894.16   808.24   905.44  \n",
       "\n",
       "[13 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(coeff, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2139a63",
   "metadata": {},
   "source": [
    "### 3.6. Assessing the Predictive Power of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f09a5c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All products\n",
    "\n",
    "split_date = dt.date(2023, 6, 1)\n",
    "train = df_prod[df_prod[\"Date\"] < split_date]\n",
    "test = df_prod[df_prod[\"Date\"] >= split_date]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 3))\n",
    "train[[\"Date\", \"TotalQty\"]].set_index(\"Date\").plot(ax=ax, label='Training Set', title='Data Train/Test Split')\n",
    "test[[\"Date\", \"TotalQty\"]].set_index(\"Date\").plot(ax=ax, label='Test Set')\n",
    "ax.axvline(split_date, color='black', ls='--')\n",
    "ax.legend(['Training Set', 'Test Set'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58c6c1ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# products = [14,  16,  20,  21,  29,  30,  32,  38,  39,  41,  49, 138, 148]\n",
    "\n",
    "# Single product\n",
    "\n",
    "df_prod = df\n",
    "df_prod = df_prod.groupby([df_prod['Date'].dt.date, df_prod[\"ProductId\"]])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "df_prod = df_prod.reset_index()\n",
    "\n",
    "# Parameters\n",
    "equation = 'TotalQty ~ FreeQty + Q(\"1\") + Q(\"2\") + Q(\"3\") + Q(\"4\") + Q(\"5\") + Q(\"6\") + Q(\"7\") + Q(\"8\") + Q(\"9\") + Q(\"10\") + Q(\"11\") + Q(\"12\") + Q(\"13\") + Q(\"14\") + Q(\"15\") + Q(\"16\") + Q(\"17\") + Q(\"18\") + Q(\"19\") + Q(\"20\") + Q(\"21\") + Q(\"22\") + Q(\"23\") + Q(\"24\") + Q(\"25\") + Q(\"26\") + Q(\"27\") + Q(\"28\") + Q(\"29\") + Q(\"30\") + Q(\"31\") - 1'\n",
    "prod_id = 14\n",
    "split_date = dt.datetime(2023, 6, 1)    # (2023, 6, 1) -> 80-20 split\n",
    "\n",
    "# Data prep.\n",
    "\n",
    "d = df_prod[df_prod.ProductId == prod_id]\n",
    "d[\"Date\"] = pd.to_datetime(d.Date)\n",
    "d[\"dayofmonth\"] = d.Date.dt.day\n",
    "d = d[[\"Date\", \"TotalQty\", \"FreeQty\", \"dayofmonth\"]]\n",
    "one_hot = pd.get_dummies(d['dayofmonth']).astype(int)\n",
    "d = d.drop('dayofmonth', axis = 1)\n",
    "d = d.join(one_hot)\n",
    "d.columns = [str(i) for i in list(d.columns)]\n",
    "train = d[d[\"Date\"] < split_date]\n",
    "test = d[d[\"Date\"] >= split_date]\n",
    "\n",
    "# Fit model, get predictions\n",
    "fit = smf.ols(equation, train).fit()\n",
    "pred = fit.get_prediction(test)\n",
    "test[\"Predictions\"] = pred.predicted\n",
    "\n",
    "# MAPE\n",
    "mape = round(mean_absolute_percentage_error(test.TotalQty, test.Predictions), 3)\n",
    "\n",
    "# Plot\n",
    "ax = test[[\"TotalQty\"]].plot(figsize=(15, 5))\n",
    "test['Predictions'].plot(ax=ax, style='-')\n",
    "plt.legend(['Truth Data', 'Predictions'])\n",
    "ax.set_title('Predictions for Product {}, MAPE {}'.format(prod_id, mape))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bd074bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{14: 1.422,\n",
       " 16: 1.213,\n",
       " 20: 1.53,\n",
       " 21: 2.903,\n",
       " 29: 2.28,\n",
       " 30: 1.54,\n",
       " 32: 0.31,\n",
       " 38: 0.561,\n",
       " 39: 1.353,\n",
       " 41: 0.742,\n",
       " 49: 1.094,\n",
       " 138: 0.402,\n",
       " 148: 1.718}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All MAPEs\n",
    "\n",
    "mape_all = {}\n",
    "\n",
    "for p in products:\n",
    "    d = df_prod[df_prod.ProductId == p]\n",
    "    d[\"Date\"] = pd.to_datetime(d.Date)\n",
    "    d[\"dayofmonth\"] = d.Date.dt.day\n",
    "    d = d[[\"Date\", \"TotalQty\", \"FreeQty\", \"dayofmonth\"]]\n",
    "    one_hot = pd.get_dummies(d['dayofmonth']).astype(int)\n",
    "    d = d.drop('dayofmonth', axis = 1)\n",
    "    d = d.join(one_hot)\n",
    "    d.columns = [str(i) for i in list(d.columns)]\n",
    "    train = d[d[\"Date\"] < split_date]\n",
    "    test = d[d[\"Date\"] >= split_date]\n",
    "\n",
    "    # Fit model, get predictions\n",
    "    fit = smf.ols(equation, train).fit()\n",
    "    pred = fit.get_prediction(test)\n",
    "    test[\"Predictions\"] = pred.predicted\n",
    "\n",
    "    # MAPE\n",
    "    mape = round(mean_absolute_percentage_error(test.TotalQty, test.Predictions), 3)\n",
    "    mape_all[p] = mape\n",
    "\n",
    "mape_all"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61febd1b",
   "metadata": {},
   "source": [
    "### 3.7. Weekday Effect on Promotion Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7cea4350",
   "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>Date</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>TotalQty</th>\n",
       "      <th>FreeQty</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>14</td>\n",
       "      <td>4911</td>\n",
       "      <td>1117</td>\n",
       "      <td>0</td>\n",
       "      <td>1117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>15</td>\n",
       "      <td>29604</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>16</td>\n",
       "      <td>8034</td>\n",
       "      <td>1709</td>\n",
       "      <td>0</td>\n",
       "      <td>1709</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>20</td>\n",
       "      <td>25184</td>\n",
       "      <td>991</td>\n",
       "      <td>0</td>\n",
       "      <td>991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>21</td>\n",
       "      <td>19689</td>\n",
       "      <td>1031</td>\n",
       "      <td>0</td>\n",
       "      <td>1031</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12067</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>72</td>\n",
       "      <td>2098</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12068</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>76</td>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12069</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>138</td>\n",
       "      <td>281</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12070</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>148</td>\n",
       "      <td>418</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12071</th>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12072 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  ProductId  TotalQty  FreeQty  0     1  2  3  4   5  6\n",
       "0      2022-02-01         14      4911     1117  0  1117  0  0  0   0  0\n",
       "1      2022-02-01         15     29604        9  0     9  0  0  0   0  0\n",
       "2      2022-02-01         16      8034     1709  0  1709  0  0  0   0  0\n",
       "3      2022-02-01         20     25184      991  0   991  0  0  0   0  0\n",
       "4      2022-02-01         21     19689     1031  0  1031  0  0  0   0  0\n",
       "...           ...        ...       ...      ... ..   ... .. .. ..  .. ..\n",
       "12067  2023-09-30         72      2098        0  0     0  0  0  0   0  0\n",
       "12068  2023-09-30         76       270        0  0     0  0  0  0   0  0\n",
       "12069  2023-09-30        138       281       10  0     0  0  0  0  10  0\n",
       "12070  2023-09-30        148       418       41  0     0  0  0  0  41  0\n",
       "12071  2023-09-30        163         1        0  0     0  0  0  0   0  0\n",
       "\n",
       "[12072 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = df\n",
    "d = d.groupby([d['Date'].dt.date, d[\"ProductId\"]])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "d = d.reset_index()\n",
    "\n",
    "d = d.assign(\n",
    "    dayofweek = d.Date.apply(lambda x: x.weekday()))\n",
    "one_hot = pd.get_dummies(d['dayofweek']).astype(int)\n",
    "d = d.drop('dayofweek', axis = 1)\n",
    "d = d.join(one_hot.mul(d[\"FreeQty\"], axis=0))\n",
    "d.columns = [str(i) for i in list(d.columns)]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb40344a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>TotalQty</td>     <th>  R-squared:         </th>  <td>   0.289</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.288</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   699.1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 13 Jan 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:45:31</td>     <th>  Log-Likelihood:    </th> <td>-1.2304e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 12072</td>      <th>  AIC:               </th>  <td>2.461e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 12064</td>      <th>  BIC:               </th>  <td>2.462e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 3421.5955</td> <td>   64.780</td> <td>   52.819</td> <td> 0.000</td> <td> 3294.617</td> <td> 3548.574</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"0\")</th>    <td>    3.6397</td> <td>    0.122</td> <td>   29.768</td> <td> 0.000</td> <td>    3.400</td> <td>    3.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"1\")</th>    <td>    3.6576</td> <td>    0.112</td> <td>   32.603</td> <td> 0.000</td> <td>    3.438</td> <td>    3.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"2\")</th>    <td>    4.5698</td> <td>    0.107</td> <td>   42.589</td> <td> 0.000</td> <td>    4.359</td> <td>    4.780</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"3\")</th>    <td>    3.0241</td> <td>    0.152</td> <td>   19.940</td> <td> 0.000</td> <td>    2.727</td> <td>    3.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"4\")</th>    <td>    5.4131</td> <td>    0.163</td> <td>   33.272</td> <td> 0.000</td> <td>    5.094</td> <td>    5.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"5\")</th>    <td>   -4.1370</td> <td>    0.925</td> <td>   -4.471</td> <td> 0.000</td> <td>   -5.951</td> <td>   -2.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Q(\"6\")</th>    <td>  -32.5664</td> <td>    4.892</td> <td>   -6.657</td> <td> 0.000</td> <td>  -42.156</td> <td>  -22.977</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14333.945</td> <th>  Durbin-Watson:     </th>  <td>   2.305</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>4129421.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 5.972</td>   <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>92.816</td>   <th>  Cond. No.          </th>  <td>    614.</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     TotalQty     & \\textbf{  R-squared:         } &      0.289   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.288   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &      699.1   \\\\\n",
       "\\textbf{Date:}             & Sat, 13 Jan 2024 & \\textbf{  Prob (F-statistic):} &      0.00    \\\\\n",
       "\\textbf{Time:}             &     20:45:31     & \\textbf{  Log-Likelihood:    } & -1.2304e+05  \\\\\n",
       "\\textbf{No. Observations:} &       12072      & \\textbf{  AIC:               } &  2.461e+05   \\\\\n",
       "\\textbf{Df Residuals:}     &       12064      & \\textbf{  BIC:               } &  2.462e+05   \\\\\n",
       "\\textbf{Df Model:}         &           7      & \\textbf{                     } &              \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &              \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &    3421.5955  &       64.780     &    52.819  &         0.000        &     3294.617    &     3548.574     \\\\\n",
       "\\textbf{Q(\"0\")}    &       3.6397  &        0.122     &    29.768  &         0.000        &        3.400    &        3.879     \\\\\n",
       "\\textbf{Q(\"1\")}    &       3.6576  &        0.112     &    32.603  &         0.000        &        3.438    &        3.878     \\\\\n",
       "\\textbf{Q(\"2\")}    &       4.5698  &        0.107     &    42.589  &         0.000        &        4.359    &        4.780     \\\\\n",
       "\\textbf{Q(\"3\")}    &       3.0241  &        0.152     &    19.940  &         0.000        &        2.727    &        3.321     \\\\\n",
       "\\textbf{Q(\"4\")}    &       5.4131  &        0.163     &    33.272  &         0.000        &        5.094    &        5.732     \\\\\n",
       "\\textbf{Q(\"5\")}    &      -4.1370  &        0.925     &    -4.471  &         0.000        &       -5.951    &       -2.323     \\\\\n",
       "\\textbf{Q(\"6\")}    &     -32.5664  &        4.892     &    -6.657  &         0.000        &      -42.156    &      -22.977     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 14333.945 & \\textbf{  Durbin-Watson:     } &      2.305   \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000  & \\textbf{  Jarque-Bera (JB):  } & 4129421.700  \\\\\n",
       "\\textbf{Skew:}          &    5.972  & \\textbf{  Prob(JB):          } &       0.00   \\\\\n",
       "\\textbf{Kurtosis:}      &   92.816  & \\textbf{  Cond. No.          } &       614.   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               TotalQty   R-squared:                       0.289\n",
       "Model:                            OLS   Adj. R-squared:                  0.288\n",
       "Method:                 Least Squares   F-statistic:                     699.1\n",
       "Date:                Sat, 13 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        20:45:31   Log-Likelihood:            -1.2304e+05\n",
       "No. Observations:               12072   AIC:                         2.461e+05\n",
       "Df Residuals:                   12064   BIC:                         2.462e+05\n",
       "Df Model:                           7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   3421.5955     64.780     52.819      0.000    3294.617    3548.574\n",
       "Q(\"0\")         3.6397      0.122     29.768      0.000       3.400       3.879\n",
       "Q(\"1\")         3.6576      0.112     32.603      0.000       3.438       3.878\n",
       "Q(\"2\")         4.5698      0.107     42.589      0.000       4.359       4.780\n",
       "Q(\"3\")         3.0241      0.152     19.940      0.000       2.727       3.321\n",
       "Q(\"4\")         5.4131      0.163     33.272      0.000       5.094       5.732\n",
       "Q(\"5\")        -4.1370      0.925     -4.471      0.000      -5.951      -2.323\n",
       "Q(\"6\")       -32.5664      4.892     -6.657      0.000     -42.156     -22.977\n",
       "==============================================================================\n",
       "Omnibus:                    14333.945   Durbin-Watson:                   2.305\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          4129421.700\n",
       "Skew:                           5.972   Prob(JB):                         0.00\n",
       "Kurtosis:                      92.816   Cond. No.                         614.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit = smf.ols('TotalQty ~ Q(\"0\") + Q(\"1\") + Q(\"2\") + Q(\"3\") + Q(\"4\") + Q(\"5\") + Q(\"6\")', d).fit()\n",
    "fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdc725d2",
   "metadata": {},
   "source": [
    "## 4. Optimization Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e020bfe",
   "metadata": {},
   "source": [
    "max $\\;$ $\\sum\\limits _{i} ^{I} \\sum\\limits _{t} ^{T} P_{i,t}$  \n",
    "\n",
    "st.  \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $P_{i,t}+F{i,t} \\le BaseDemand_{i,t} + PromoSensitivity_{i} \\times F_{i,t}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$\n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $F_{i,t} \\le X_{i,t} \\times M$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $F_{i,t} \\ge X_{i,t}-0.5$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$\n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $\\sum\\limits _{t} ^{T} X_{i,t} = L_{i} $  $\\;\\;\\;\\;\\;\\;$  $\\forall{i}$   \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $L_{i} \\le LongestPromotion$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $L_{i} \\ge ShortestPromotion$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $\\sum\\limits _{i} ^{I} X_{i,t} \\le DailyPromotedProductLimit_{i}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{t}$  \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $\\sum\\limits _{t} ^{T} P_{i,t} \\le TotalPaidQuantityLimit_{i}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $\\sum\\limits _{t} ^{T} F_{i,t} \\le TotalFreeQuantityLimit_{i}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i}$   \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $P_{i,t} \\le PaidQuantityLimit_{i}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $F_{i,t} \\le FreeQuantityLimit_{i}$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$ \n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $P_{i,t} \\ge 0$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$\n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $F_{i,t} \\ge 0$  $\\;\\;\\;\\;\\;\\;$  $\\forall{i,t}$\n",
    "\n",
    "$\\;\\;\\;\\;\\;\\;$ $X_{i,t} \\in \\{0,1\\}$\n",
    "\n",
    "\n",
    "\n",
    "**Decision Variables**<br>\n",
    "$P_{i,t}$ - Paid quantity for product i at time t<br>\n",
    "$F_{i,t}$ - Free quantity for product i at time t<br>\n",
    "$X_{i,t}$ - Binary variable that is equal to 1 if the product i is promoted at time t, and 0 otherwise<br>\n",
    "$L_{i}$ -  Promotion length<br>\n",
    "\n",
    "**Parameters**<br>\n",
    "$T$ - Number of days in the time horizon<br>\n",
    "$I$ - Number of product types<br>\n",
    "$Base\\;Demand_{i,t}$ - Demand without promotion, for product i at time t<br>\n",
    "$Promo\\;Sensitivity_{i}$ - Effect on the base demand for a given free quantity of product i<br>\n",
    "$Total\\;Paid\\;Quantity\\;Limit_{i}$ -  Capacity of total paid quantity of product i throughout the time horizon<br>\n",
    "$Total\\;Free\\;Quantity\\;Limit_{i}$ -  Capacity of total free quantity of product i throughout the time horizon<br>\n",
    "$Paid\\;Quantity\\;Limit_{i}$ - Maximum daily amount allowed for the paid quantity of product i<br>\n",
    "$Free\\;Quantity\\;Limit_{i}$ - Maximum daily amount allowed for the free quantity of product i<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e9ad3110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative base demands obtained from the XGB model\n",
    "BaseDemand2 = pd.read_pickle(\"Data/df_prod_agg2.pkl\")\n",
    "BaseDemand2 = BaseDemand2[BaseDemand2['ProductId'].isin(products)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "39205ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "out.reset_index(inplace=True)\n",
    "out = out[out.ProductId.isin(products)].set_index(\"ProductId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d4c4c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "free_qty_multiplier = 1\n",
    "free_ratio = 1\n",
    "\n",
    "out = out.groupby([\"ProductId\"])[[\"TotalQty\", \"FreeQty\"]].sum()\n",
    "free_qty_sep_23 = list(out[\"FreeQty\"]*free_qty_multiplier)\n",
    "paid_qty_sep_23 = list((out[\"TotalQty\"] - out[\"FreeQty\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91484095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.44471891,  4.73016327, 15.63554545, 10.17246193, 10.60159006,\n",
       "        2.66357266,  7.52179049,  2.74826323, 13.52337343,  2.87071144,\n",
       "        4.93317534, 17.92858991,  6.51123691])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = [14,  16,  20,  21,  29,  30,  32,  38,  39,  41,  49, 138, 148]\n",
    "\n",
    "I = len(products)\n",
    "T = 31\n",
    "PHorizon = list(range(0, T))\n",
    "Base_Demand = np.array(coeff.drop(\"FreeQty\", axis=1))\n",
    "Promo_Sensitivity = np.array(coeff.FreeQty)\n",
    "\n",
    "Promo_Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1d9d6a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LP MAXIMIZATION MODEL\n",
    "\n",
    "Promo_Model = LpProblem(\"Pharma_Promotion_Model\", LpMaximize)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a61b2c",
   "metadata": {},
   "source": [
    "### 4.1. Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9c19949",
   "metadata": {},
   "outputs": [],
   "source": [
    "products = [14,  16,  20,  21,  29,  30,  32,  38,  39,  41,  49, 138, 148]\n",
    "\n",
    "# T = 31 # end of promotion horizon\n",
    "PHorizon = list(range(0, T))\n",
    "AugHorizon = list(range(1, T))\n",
    "\n",
    "# I = len(products) # product count \n",
    "Products = list(range(0, I))\n",
    "\n",
    "Total_PaidQty_Limit = paid_qty_sep_23\n",
    "Total_FreeQty_Limit = free_qty_sep_23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d58f6bba",
   "metadata": {},
   "source": [
    "### 4.2. Decision Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7b6218c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paid Quantity\n",
    "P = LpVariable.dicts(\"Paid_Quantity\",(Products,PHorizon),lowBound=0, cat='Integer')\n",
    "\n",
    "# Free Quantity\n",
    "F = LpVariable.dicts(\"Free_Quantity\",(Products,PHorizon),lowBound=0, cat='Integer')\n",
    "\n",
    "# Is Product \"i\" at Time \"t\" Promoted?\n",
    "X = LpVariable.dicts(\"IsPromoted\",(Products,PHorizon),cat='Binary')\n",
    "\n",
    "# Promotion Length\n",
    "Longest_Promo = 31\n",
    "Shortest_Promo = 0\n",
    "L = LpVariable.dicts(\"Promo_Length\",Products,lowBound=Shortest_Promo, upBound=Longest_Promo, cat='Integer')\n",
    "\n",
    "# Promotion Starting Date\n",
    "S = LpVariable.dicts(\"Promo_Start\",(Products,PHorizon),cat='Binary')\n",
    "\n",
    "# Promotion Ending Date\n",
    "E = LpVariable.dicts(\"Promo_End\",(Products,PHorizon),cat='Binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5d6211",
   "metadata": {},
   "source": [
    "### 4.3. Objective Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "20cb476c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Total_Paid_Quantity = lpSum(lpSum(P[i][t] for i in Products) for t in PHorizon)\n",
    "Total_Free_Quantity = lpSum(lpSum(F[i][t] for i in Products) for t in PHorizon)\n",
    "\n",
    "W1 = 100 # average earning from a paid quantity\n",
    "W2 = 75 # average cost for a given free quantity\n",
    "\n",
    "Promo_Model += Total_Paid_Quantity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9b1f1d0",
   "metadata": {},
   "source": [
    "### 4.4. Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "45092429",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DEMAND SATISFACTION\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Promo_Model += P[i][t] + F[i][t] <= Base_Demand[i][t] + Promo_Sensitivity[i]*F[i][t]\n",
    "                                                                                                \n",
    "# NO FREE QTY WITHOUT PROMOTION\n",
    "Big_M = sum(free_qty_sep_23)\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Promo_Model += F[i][t] <= X[i][t]*Big_M\n",
    "        Promo_Model += F[i][t] >= X[i][t]-0.5\n",
    "\n",
    "# TOTAL PAID QUANTITY PER PRODUCT\n",
    "# for i in Products:\n",
    "#    Promo_Model += lpSum(P[i][t] for t in PHorizon) <= Total_PaidQty_Limit[i]\n",
    "    \n",
    "# TOTAL FREE QUANTITY PER PRODUCT\n",
    "for i in Products:\n",
    "    Promo_Model += lpSum(F[i][t] for t in PHorizon) <= Total_FreeQty_Limit[i]\n",
    "\n",
    "# PAID QUANTITY PER PRODUCT\n",
    "# for i in Products:\n",
    "#     for t in PHorizon:\n",
    "#         Promo_Model += P[i][t] <= Total_PaidQty_Limit[i]/T\n",
    "    \n",
    "# FREE QUANTITY PER PRODUCT\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Promo_Model += F[i][t] <= Total_FreeQty_Limit[i]/T\n",
    "# PROMOTED PRODUCTS AT TIME \"t\"\n",
    "for t in PHorizon:\n",
    "    Promo_Model += lpSum(X[i][t] for i in Products) <= 13\n",
    "    \n",
    "# PROMO PERIOD FOR PRODUCT \"i\"  \n",
    "for i in Products:\n",
    "    Promo_Model += lpSum(X[i][t] for t in PHorizon) == L[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a223b88b",
   "metadata": {},
   "source": [
    "### 4.5. Model Status and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e64ef374",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: Optimal\n"
     ]
    }
   ],
   "source": [
    "path_to_cplex = r\"C:/Program Files/IBM/ILOG/CPLEX_Studio2211/cplex/bin/x64_win64/cplex.exe\"\n",
    "solver = CPLEX_CMD(path=path_to_cplex)\n",
    "\n",
    "# solver = GUROBI()\n",
    "solver.solve(Promo_Model)\n",
    "\n",
    "# The status of the solution is printed to the screen\n",
    "print(\"Status:\", LpStatus[Promo_Model.status])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9c6230d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Total Paid Quantity =  1357700.0 \n",
      "\n",
      "Free_Quantity_0_0 = 200.0\n",
      "Free_Quantity_0_1 = 200.0\n",
      "Free_Quantity_0_10 = 200.0\n",
      "Free_Quantity_0_11 = 200.0\n",
      "Free_Quantity_0_12 = 200.0\n",
      "Free_Quantity_0_13 = 200.0\n",
      "Free_Quantity_0_14 = 200.0\n",
      "Free_Quantity_0_15 = 200.0\n",
      "Free_Quantity_0_16 = 200.0\n",
      "Free_Quantity_0_17 = 200.0\n",
      "Free_Quantity_0_18 = 200.0\n",
      "Free_Quantity_0_19 = 200.0\n",
      "Free_Quantity_0_2 = 200.0\n",
      "Free_Quantity_0_20 = 200.0\n",
      "Free_Quantity_0_21 = 200.0\n",
      "Free_Quantity_0_22 = 200.0\n",
      "Free_Quantity_0_23 = 200.0\n",
      "Free_Quantity_0_24 = 200.0\n",
      "Free_Quantity_0_25 = 200.0\n",
      "Free_Quantity_0_26 = 200.0\n",
      "Free_Quantity_0_27 = 200.0\n",
      "Free_Quantity_0_28 = 200.0\n",
      "Free_Quantity_0_29 = 200.0\n",
      "Free_Quantity_0_3 = 200.0\n",
      "Free_Quantity_0_30 = 200.0\n",
      "Free_Quantity_0_4 = 200.0\n",
      "Free_Quantity_0_5 = 200.0\n",
      "Free_Quantity_0_6 = 200.0\n",
      "Free_Quantity_0_7 = 200.0\n",
      "Free_Quantity_0_8 = 200.0\n",
      "Free_Quantity_0_9 = 200.0\n",
      "Free_Quantity_10_0 = 232.0\n",
      "Free_Quantity_10_1 = 232.0\n",
      "Free_Quantity_10_10 = 232.0\n",
      "Free_Quantity_10_11 = 232.0\n",
      "Free_Quantity_10_12 = 232.0\n",
      "Free_Quantity_10_13 = 232.0\n",
      "Free_Quantity_10_14 = 232.0\n",
      "Free_Quantity_10_15 = 232.0\n",
      "Free_Quantity_10_16 = 232.0\n",
      "Free_Quantity_10_17 = 232.0\n",
      "Free_Quantity_10_18 = 232.0\n",
      "Free_Quantity_10_19 = 232.0\n",
      "Free_Quantity_10_2 = 232.0\n",
      "Free_Quantity_10_20 = 232.0\n",
      "Free_Quantity_10_21 = 232.0\n",
      "Free_Quantity_10_22 = 232.0\n",
      "Free_Quantity_10_23 = 232.0\n",
      "Free_Quantity_10_24 = 232.0\n",
      "Free_Quantity_10_25 = 232.0\n",
      "Free_Quantity_10_26 = 232.0\n",
      "Free_Quantity_10_27 = 232.0\n",
      "Free_Quantity_10_28 = 232.0\n",
      "Free_Quantity_10_29 = 232.0\n",
      "Free_Quantity_10_3 = 232.0\n",
      "Free_Quantity_10_30 = 232.0\n",
      "Free_Quantity_10_4 = 232.0\n",
      "Free_Quantity_10_5 = 232.0\n",
      "Free_Quantity_10_6 = 232.0\n",
      "Free_Quantity_10_7 = 232.0\n",
      "Free_Quantity_10_8 = 232.0\n",
      "Free_Quantity_10_9 = 232.0\n",
      "Free_Quantity_11_0 = 29.0\n",
      "Free_Quantity_11_1 = 29.0\n",
      "Free_Quantity_11_10 = 29.0\n",
      "Free_Quantity_11_11 = 29.0\n",
      "Free_Quantity_11_12 = 29.0\n",
      "Free_Quantity_11_13 = 29.0\n",
      "Free_Quantity_11_14 = 29.0\n",
      "Free_Quantity_11_15 = 29.0\n",
      "Free_Quantity_11_16 = 29.0\n",
      "Free_Quantity_11_17 = 29.0\n",
      "Free_Quantity_11_18 = 29.0\n",
      "Free_Quantity_11_19 = 29.0\n",
      "Free_Quantity_11_2 = 29.0\n",
      "Free_Quantity_11_20 = 29.0\n",
      "Free_Quantity_11_21 = 29.0\n",
      "Free_Quantity_11_22 = 29.0\n",
      "Free_Quantity_11_23 = 29.0\n",
      "Free_Quantity_11_24 = 29.0\n",
      "Free_Quantity_11_25 = 29.0\n",
      "Free_Quantity_11_26 = 29.0\n",
      "Free_Quantity_11_27 = 29.0\n",
      "Free_Quantity_11_28 = 29.0\n",
      "Free_Quantity_11_29 = 29.0\n",
      "Free_Quantity_11_3 = 29.0\n",
      "Free_Quantity_11_30 = 29.0\n",
      "Free_Quantity_11_4 = 29.0\n",
      "Free_Quantity_11_5 = 29.0\n",
      "Free_Quantity_11_6 = 29.0\n",
      "Free_Quantity_11_7 = 29.0\n",
      "Free_Quantity_11_8 = 29.0\n",
      "Free_Quantity_11_9 = 29.0\n",
      "Free_Quantity_12_0 = 481.0\n",
      "Free_Quantity_12_1 = 481.0\n",
      "Free_Quantity_12_10 = 481.0\n",
      "Free_Quantity_12_11 = 481.0\n",
      "Free_Quantity_12_12 = 481.0\n",
      "Free_Quantity_12_13 = 481.0\n",
      "Free_Quantity_12_14 = 481.0\n",
      "Free_Quantity_12_15 = 481.0\n",
      "Free_Quantity_12_16 = 481.0\n",
      "Free_Quantity_12_17 = 481.0\n",
      "Free_Quantity_12_18 = 481.0\n",
      "Free_Quantity_12_19 = 481.0\n",
      "Free_Quantity_12_2 = 481.0\n",
      "Free_Quantity_12_20 = 481.0\n",
      "Free_Quantity_12_21 = 481.0\n",
      "Free_Quantity_12_22 = 481.0\n",
      "Free_Quantity_12_23 = 481.0\n",
      "Free_Quantity_12_24 = 481.0\n",
      "Free_Quantity_12_25 = 481.0\n",
      "Free_Quantity_12_26 = 481.0\n",
      "Free_Quantity_12_27 = 481.0\n",
      "Free_Quantity_12_28 = 481.0\n",
      "Free_Quantity_12_29 = 481.0\n",
      "Free_Quantity_12_3 = 481.0\n",
      "Free_Quantity_12_30 = 481.0\n",
      "Free_Quantity_12_4 = 481.0\n",
      "Free_Quantity_12_5 = 481.0\n",
      "Free_Quantity_12_6 = 481.0\n",
      "Free_Quantity_12_7 = 481.0\n",
      "Free_Quantity_12_8 = 481.0\n",
      "Free_Quantity_12_9 = 481.0\n",
      "Free_Quantity_1_0 = 274.0\n",
      "Free_Quantity_1_1 = 274.0\n",
      "Free_Quantity_1_10 = 274.0\n",
      "Free_Quantity_1_11 = 274.0\n",
      "Free_Quantity_1_12 = 274.0\n",
      "Free_Quantity_1_13 = 274.0\n",
      "Free_Quantity_1_14 = 274.0\n",
      "Free_Quantity_1_15 = 274.0\n",
      "Free_Quantity_1_16 = 274.0\n",
      "Free_Quantity_1_17 = 274.0\n",
      "Free_Quantity_1_18 = 274.0\n",
      "Free_Quantity_1_19 = 274.0\n",
      "Free_Quantity_1_2 = 274.0\n",
      "Free_Quantity_1_20 = 274.0\n",
      "Free_Quantity_1_21 = 274.0\n",
      "Free_Quantity_1_22 = 274.0\n",
      "Free_Quantity_1_23 = 274.0\n",
      "Free_Quantity_1_24 = 274.0\n",
      "Free_Quantity_1_25 = 274.0\n",
      "Free_Quantity_1_26 = 274.0\n",
      "Free_Quantity_1_27 = 274.0\n",
      "Free_Quantity_1_28 = 274.0\n",
      "Free_Quantity_1_29 = 274.0\n",
      "Free_Quantity_1_3 = 274.0\n",
      "Free_Quantity_1_30 = 274.0\n",
      "Free_Quantity_1_4 = 274.0\n",
      "Free_Quantity_1_5 = 274.0\n",
      "Free_Quantity_1_6 = 274.0\n",
      "Free_Quantity_1_7 = 274.0\n",
      "Free_Quantity_1_8 = 274.0\n",
      "Free_Quantity_1_9 = 274.0\n",
      "Free_Quantity_2_0 = 349.0\n",
      "Free_Quantity_2_1 = 349.0\n",
      "Free_Quantity_2_10 = 349.0\n",
      "Free_Quantity_2_11 = 349.0\n",
      "Free_Quantity_2_12 = 349.0\n",
      "Free_Quantity_2_13 = 349.0\n",
      "Free_Quantity_2_14 = 349.0\n",
      "Free_Quantity_2_15 = 349.0\n",
      "Free_Quantity_2_16 = 349.0\n",
      "Free_Quantity_2_17 = 349.0\n",
      "Free_Quantity_2_18 = 349.0\n",
      "Free_Quantity_2_19 = 349.0\n",
      "Free_Quantity_2_2 = 349.0\n",
      "Free_Quantity_2_20 = 349.0\n",
      "Free_Quantity_2_21 = 349.0\n",
      "Free_Quantity_2_22 = 349.0\n",
      "Free_Quantity_2_23 = 349.0\n",
      "Free_Quantity_2_24 = 349.0\n",
      "Free_Quantity_2_25 = 349.0\n",
      "Free_Quantity_2_26 = 349.0\n",
      "Free_Quantity_2_27 = 349.0\n",
      "Free_Quantity_2_28 = 349.0\n",
      "Free_Quantity_2_29 = 349.0\n",
      "Free_Quantity_2_3 = 349.0\n",
      "Free_Quantity_2_30 = 349.0\n",
      "Free_Quantity_2_4 = 349.0\n",
      "Free_Quantity_2_5 = 349.0\n",
      "Free_Quantity_2_6 = 349.0\n",
      "Free_Quantity_2_7 = 349.0\n",
      "Free_Quantity_2_8 = 349.0\n",
      "Free_Quantity_2_9 = 349.0\n",
      "Free_Quantity_3_0 = 320.0\n",
      "Free_Quantity_3_1 = 320.0\n",
      "Free_Quantity_3_10 = 320.0\n",
      "Free_Quantity_3_11 = 320.0\n",
      "Free_Quantity_3_12 = 320.0\n",
      "Free_Quantity_3_13 = 320.0\n",
      "Free_Quantity_3_14 = 320.0\n",
      "Free_Quantity_3_15 = 320.0\n",
      "Free_Quantity_3_16 = 320.0\n",
      "Free_Quantity_3_17 = 320.0\n",
      "Free_Quantity_3_18 = 320.0\n",
      "Free_Quantity_3_19 = 320.0\n",
      "Free_Quantity_3_2 = 320.0\n",
      "Free_Quantity_3_20 = 320.0\n",
      "Free_Quantity_3_21 = 320.0\n",
      "Free_Quantity_3_22 = 320.0\n",
      "Free_Quantity_3_23 = 320.0\n",
      "Free_Quantity_3_24 = 320.0\n",
      "Free_Quantity_3_25 = 320.0\n",
      "Free_Quantity_3_26 = 320.0\n",
      "Free_Quantity_3_27 = 320.0\n",
      "Free_Quantity_3_28 = 320.0\n",
      "Free_Quantity_3_29 = 320.0\n",
      "Free_Quantity_3_3 = 320.0\n",
      "Free_Quantity_3_30 = 320.0\n",
      "Free_Quantity_3_4 = 320.0\n",
      "Free_Quantity_3_5 = 320.0\n",
      "Free_Quantity_3_6 = 320.0\n",
      "Free_Quantity_3_7 = 320.0\n",
      "Free_Quantity_3_8 = 320.0\n",
      "Free_Quantity_3_9 = 320.0\n",
      "Free_Quantity_4_0 = 105.0\n",
      "Free_Quantity_4_1 = 105.0\n",
      "Free_Quantity_4_10 = 105.0\n",
      "Free_Quantity_4_11 = 105.0\n",
      "Free_Quantity_4_12 = 105.0\n",
      "Free_Quantity_4_13 = 105.0\n",
      "Free_Quantity_4_14 = 105.0\n",
      "Free_Quantity_4_15 = 105.0\n",
      "Free_Quantity_4_16 = 105.0\n",
      "Free_Quantity_4_17 = 105.0\n",
      "Free_Quantity_4_18 = 105.0\n",
      "Free_Quantity_4_19 = 105.0\n",
      "Free_Quantity_4_2 = 105.0\n",
      "Free_Quantity_4_20 = 105.0\n",
      "Free_Quantity_4_21 = 105.0\n",
      "Free_Quantity_4_22 = 105.0\n",
      "Free_Quantity_4_23 = 105.0\n",
      "Free_Quantity_4_24 = 105.0\n",
      "Free_Quantity_4_25 = 105.0\n",
      "Free_Quantity_4_26 = 105.0\n",
      "Free_Quantity_4_27 = 105.0\n",
      "Free_Quantity_4_28 = 105.0\n",
      "Free_Quantity_4_29 = 105.0\n",
      "Free_Quantity_4_3 = 105.0\n",
      "Free_Quantity_4_30 = 105.0\n",
      "Free_Quantity_4_4 = 105.0\n",
      "Free_Quantity_4_5 = 105.0\n",
      "Free_Quantity_4_6 = 105.0\n",
      "Free_Quantity_4_7 = 105.0\n",
      "Free_Quantity_4_8 = 105.0\n",
      "Free_Quantity_4_9 = 105.0\n",
      "Free_Quantity_5_0 = 1390.0\n",
      "Free_Quantity_5_1 = 1390.0\n",
      "Free_Quantity_5_10 = 1390.0\n",
      "Free_Quantity_5_11 = 1390.0\n",
      "Free_Quantity_5_12 = 1390.0\n",
      "Free_Quantity_5_13 = 1390.0\n",
      "Free_Quantity_5_14 = 1390.0\n",
      "Free_Quantity_5_15 = 1390.0\n",
      "Free_Quantity_5_16 = 1390.0\n",
      "Free_Quantity_5_17 = 1390.0\n",
      "Free_Quantity_5_18 = 1390.0\n",
      "Free_Quantity_5_19 = 1390.0\n",
      "Free_Quantity_5_2 = 1390.0\n",
      "Free_Quantity_5_20 = 1390.0\n",
      "Free_Quantity_5_21 = 1390.0\n",
      "Free_Quantity_5_22 = 1390.0\n",
      "Free_Quantity_5_23 = 1390.0\n",
      "Free_Quantity_5_24 = 1390.0\n",
      "Free_Quantity_5_25 = 1390.0\n",
      "Free_Quantity_5_26 = 1390.0\n",
      "Free_Quantity_5_27 = 1390.0\n",
      "Free_Quantity_5_28 = 1390.0\n",
      "Free_Quantity_5_29 = 1390.0\n",
      "Free_Quantity_5_3 = 1390.0\n",
      "Free_Quantity_5_30 = 1390.0\n",
      "Free_Quantity_5_4 = 1390.0\n",
      "Free_Quantity_5_5 = 1390.0\n",
      "Free_Quantity_5_6 = 1390.0\n",
      "Free_Quantity_5_7 = 1390.0\n",
      "Free_Quantity_5_8 = 1390.0\n",
      "Free_Quantity_5_9 = 1390.0\n",
      "Free_Quantity_6_0 = 259.0\n",
      "Free_Quantity_6_1 = 259.0\n",
      "Free_Quantity_6_10 = 259.0\n",
      "Free_Quantity_6_11 = 259.0\n",
      "Free_Quantity_6_12 = 259.0\n",
      "Free_Quantity_6_13 = 259.0\n",
      "Free_Quantity_6_14 = 259.0\n",
      "Free_Quantity_6_15 = 259.0\n",
      "Free_Quantity_6_16 = 259.0\n",
      "Free_Quantity_6_17 = 259.0\n",
      "Free_Quantity_6_18 = 259.0\n",
      "Free_Quantity_6_19 = 259.0\n",
      "Free_Quantity_6_2 = 259.0\n",
      "Free_Quantity_6_20 = 259.0\n",
      "Free_Quantity_6_21 = 259.0\n",
      "Free_Quantity_6_22 = 259.0\n",
      "Free_Quantity_6_23 = 259.0\n",
      "Free_Quantity_6_24 = 259.0\n",
      "Free_Quantity_6_25 = 259.0\n",
      "Free_Quantity_6_26 = 259.0\n",
      "Free_Quantity_6_27 = 259.0\n",
      "Free_Quantity_6_28 = 259.0\n",
      "Free_Quantity_6_29 = 259.0\n",
      "Free_Quantity_6_3 = 259.0\n",
      "Free_Quantity_6_30 = 259.0\n",
      "Free_Quantity_6_4 = 259.0\n",
      "Free_Quantity_6_5 = 259.0\n",
      "Free_Quantity_6_6 = 259.0\n",
      "Free_Quantity_6_7 = 259.0\n",
      "Free_Quantity_6_8 = 259.0\n",
      "Free_Quantity_6_9 = 259.0\n",
      "Free_Quantity_7_0 = 1692.0\n",
      "Free_Quantity_7_1 = 1692.0\n",
      "Free_Quantity_7_10 = 1692.0\n",
      "Free_Quantity_7_11 = 1692.0\n",
      "Free_Quantity_7_12 = 1692.0\n",
      "Free_Quantity_7_13 = 1692.0\n",
      "Free_Quantity_7_14 = 1692.0\n",
      "Free_Quantity_7_15 = 1692.0\n",
      "Free_Quantity_7_16 = 1692.0\n",
      "Free_Quantity_7_17 = 1692.0\n",
      "Free_Quantity_7_18 = 1692.0\n",
      "Free_Quantity_7_19 = 1692.0\n",
      "Free_Quantity_7_2 = 1692.0\n",
      "Free_Quantity_7_20 = 1692.0\n",
      "Free_Quantity_7_21 = 1692.0\n",
      "Free_Quantity_7_22 = 1692.0\n",
      "Free_Quantity_7_23 = 1692.0\n",
      "Free_Quantity_7_24 = 1692.0\n",
      "Free_Quantity_7_25 = 1692.0\n",
      "Free_Quantity_7_26 = 1692.0\n",
      "Free_Quantity_7_27 = 1692.0\n",
      "Free_Quantity_7_28 = 1692.0\n",
      "Free_Quantity_7_29 = 1692.0\n",
      "Free_Quantity_7_3 = 1692.0\n",
      "Free_Quantity_7_30 = 1692.0\n",
      "Free_Quantity_7_4 = 1692.0\n",
      "Free_Quantity_7_5 = 1692.0\n",
      "Free_Quantity_7_6 = 1692.0\n",
      "Free_Quantity_7_7 = 1692.0\n",
      "Free_Quantity_7_8 = 1692.0\n",
      "Free_Quantity_7_9 = 1692.0\n",
      "Free_Quantity_8_0 = 478.0\n",
      "Free_Quantity_8_1 = 478.0\n",
      "Free_Quantity_8_10 = 478.0\n",
      "Free_Quantity_8_11 = 478.0\n",
      "Free_Quantity_8_12 = 478.0\n",
      "Free_Quantity_8_13 = 478.0\n",
      "Free_Quantity_8_14 = 478.0\n",
      "Free_Quantity_8_15 = 478.0\n",
      "Free_Quantity_8_16 = 478.0\n",
      "Free_Quantity_8_17 = 478.0\n",
      "Free_Quantity_8_18 = 478.0\n",
      "Free_Quantity_8_19 = 478.0\n",
      "Free_Quantity_8_2 = 478.0\n",
      "Free_Quantity_8_20 = 478.0\n",
      "Free_Quantity_8_21 = 478.0\n",
      "Free_Quantity_8_22 = 478.0\n",
      "Free_Quantity_8_23 = 478.0\n",
      "Free_Quantity_8_24 = 478.0\n",
      "Free_Quantity_8_25 = 478.0\n",
      "Free_Quantity_8_26 = 478.0\n",
      "Free_Quantity_8_27 = 478.0\n",
      "Free_Quantity_8_28 = 478.0\n",
      "Free_Quantity_8_29 = 478.0\n",
      "Free_Quantity_8_3 = 478.0\n",
      "Free_Quantity_8_30 = 478.0\n",
      "Free_Quantity_8_4 = 478.0\n",
      "Free_Quantity_8_5 = 478.0\n",
      "Free_Quantity_8_6 = 478.0\n",
      "Free_Quantity_8_7 = 478.0\n",
      "Free_Quantity_8_8 = 478.0\n",
      "Free_Quantity_8_9 = 478.0\n",
      "Free_Quantity_9_0 = 446.0\n",
      "Free_Quantity_9_1 = 446.0\n",
      "Free_Quantity_9_10 = 446.0\n",
      "Free_Quantity_9_11 = 446.0\n",
      "Free_Quantity_9_12 = 446.0\n",
      "Free_Quantity_9_13 = 446.0\n",
      "Free_Quantity_9_14 = 446.0\n",
      "Free_Quantity_9_15 = 446.0\n",
      "Free_Quantity_9_16 = 446.0\n",
      "Free_Quantity_9_17 = 446.0\n",
      "Free_Quantity_9_18 = 446.0\n",
      "Free_Quantity_9_19 = 446.0\n",
      "Free_Quantity_9_2 = 446.0\n",
      "Free_Quantity_9_20 = 446.0\n",
      "Free_Quantity_9_21 = 446.0\n",
      "Free_Quantity_9_22 = 446.0\n",
      "Free_Quantity_9_23 = 446.0\n",
      "Free_Quantity_9_24 = 446.0\n",
      "Free_Quantity_9_25 = 446.0\n",
      "Free_Quantity_9_26 = 446.0\n",
      "Free_Quantity_9_27 = 446.0\n",
      "Free_Quantity_9_28 = 446.0\n",
      "Free_Quantity_9_29 = 446.0\n",
      "Free_Quantity_9_3 = 446.0\n",
      "Free_Quantity_9_30 = 446.0\n",
      "Free_Quantity_9_4 = 446.0\n",
      "Free_Quantity_9_5 = 446.0\n",
      "Free_Quantity_9_6 = 446.0\n",
      "Free_Quantity_9_7 = 446.0\n",
      "Free_Quantity_9_8 = 446.0\n",
      "Free_Quantity_9_9 = 446.0\n",
      "IsPromoted_0_0 = 1.0\n",
      "IsPromoted_0_1 = 1.0\n",
      "IsPromoted_0_10 = 1.0\n",
      "IsPromoted_0_11 = 1.0\n",
      "IsPromoted_0_12 = 1.0\n",
      "IsPromoted_0_13 = 1.0\n",
      "IsPromoted_0_14 = 1.0\n",
      "IsPromoted_0_15 = 1.0\n",
      "IsPromoted_0_16 = 1.0\n",
      "IsPromoted_0_17 = 1.0\n",
      "IsPromoted_0_18 = 1.0\n",
      "IsPromoted_0_19 = 1.0\n",
      "IsPromoted_0_2 = 1.0\n",
      "IsPromoted_0_20 = 1.0\n",
      "IsPromoted_0_21 = 1.0\n",
      "IsPromoted_0_22 = 1.0\n",
      "IsPromoted_0_23 = 1.0\n",
      "IsPromoted_0_24 = 1.0\n",
      "IsPromoted_0_25 = 1.0\n",
      "IsPromoted_0_26 = 1.0\n",
      "IsPromoted_0_27 = 1.0\n",
      "IsPromoted_0_28 = 1.0\n",
      "IsPromoted_0_29 = 1.0\n",
      "IsPromoted_0_3 = 1.0\n",
      "IsPromoted_0_30 = 1.0\n",
      "IsPromoted_0_4 = 1.0\n",
      "IsPromoted_0_5 = 1.0\n",
      "IsPromoted_0_6 = 1.0\n",
      "IsPromoted_0_7 = 1.0\n",
      "IsPromoted_0_8 = 1.0\n",
      "IsPromoted_0_9 = 1.0\n",
      "IsPromoted_10_0 = 1.0\n",
      "IsPromoted_10_1 = 1.0\n",
      "IsPromoted_10_10 = 1.0\n",
      "IsPromoted_10_11 = 1.0\n",
      "IsPromoted_10_12 = 1.0\n",
      "IsPromoted_10_13 = 1.0\n",
      "IsPromoted_10_14 = 1.0\n",
      "IsPromoted_10_15 = 1.0\n",
      "IsPromoted_10_16 = 1.0\n",
      "IsPromoted_10_17 = 1.0\n",
      "IsPromoted_10_18 = 1.0\n",
      "IsPromoted_10_19 = 1.0\n",
      "IsPromoted_10_2 = 1.0\n",
      "IsPromoted_10_20 = 1.0\n",
      "IsPromoted_10_21 = 1.0\n",
      "IsPromoted_10_22 = 1.0\n",
      "IsPromoted_10_23 = 1.0\n",
      "IsPromoted_10_24 = 1.0\n",
      "IsPromoted_10_25 = 1.0\n",
      "IsPromoted_10_26 = 1.0\n",
      "IsPromoted_10_27 = 1.0\n",
      "IsPromoted_10_28 = 1.0\n",
      "IsPromoted_10_29 = 1.0\n",
      "IsPromoted_10_3 = 1.0\n",
      "IsPromoted_10_30 = 1.0\n",
      "IsPromoted_10_4 = 1.0\n",
      "IsPromoted_10_5 = 1.0\n",
      "IsPromoted_10_6 = 1.0\n",
      "IsPromoted_10_7 = 1.0\n",
      "IsPromoted_10_8 = 1.0\n",
      "IsPromoted_10_9 = 1.0\n",
      "IsPromoted_11_0 = 1.0\n",
      "IsPromoted_11_1 = 1.0\n",
      "IsPromoted_11_10 = 1.0\n",
      "IsPromoted_11_11 = 1.0\n",
      "IsPromoted_11_12 = 1.0\n",
      "IsPromoted_11_13 = 1.0\n",
      "IsPromoted_11_14 = 1.0\n",
      "IsPromoted_11_15 = 1.0\n",
      "IsPromoted_11_16 = 1.0\n",
      "IsPromoted_11_17 = 1.0\n",
      "IsPromoted_11_18 = 1.0\n",
      "IsPromoted_11_19 = 1.0\n",
      "IsPromoted_11_2 = 1.0\n",
      "IsPromoted_11_20 = 1.0\n",
      "IsPromoted_11_21 = 1.0\n",
      "IsPromoted_11_22 = 1.0\n",
      "IsPromoted_11_23 = 1.0\n",
      "IsPromoted_11_24 = 1.0\n",
      "IsPromoted_11_25 = 1.0\n",
      "IsPromoted_11_26 = 1.0\n",
      "IsPromoted_11_27 = 1.0\n",
      "IsPromoted_11_28 = 1.0\n",
      "IsPromoted_11_29 = 1.0\n",
      "IsPromoted_11_3 = 1.0\n",
      "IsPromoted_11_30 = 1.0\n",
      "IsPromoted_11_4 = 1.0\n",
      "IsPromoted_11_5 = 1.0\n",
      "IsPromoted_11_6 = 1.0\n",
      "IsPromoted_11_7 = 1.0\n",
      "IsPromoted_11_8 = 1.0\n",
      "IsPromoted_11_9 = 1.0\n",
      "IsPromoted_12_0 = 1.0\n",
      "IsPromoted_12_1 = 1.0\n",
      "IsPromoted_12_10 = 1.0\n",
      "IsPromoted_12_11 = 1.0\n",
      "IsPromoted_12_12 = 1.0\n",
      "IsPromoted_12_13 = 1.0\n",
      "IsPromoted_12_14 = 1.0\n",
      "IsPromoted_12_15 = 1.0\n",
      "IsPromoted_12_16 = 1.0\n",
      "IsPromoted_12_17 = 1.0\n",
      "IsPromoted_12_18 = 1.0\n",
      "IsPromoted_12_19 = 1.0\n",
      "IsPromoted_12_2 = 1.0\n",
      "IsPromoted_12_20 = 1.0\n",
      "IsPromoted_12_21 = 1.0\n",
      "IsPromoted_12_22 = 1.0\n",
      "IsPromoted_12_23 = 1.0\n",
      "IsPromoted_12_24 = 1.0\n",
      "IsPromoted_12_25 = 1.0\n",
      "IsPromoted_12_26 = 1.0\n",
      "IsPromoted_12_27 = 1.0\n",
      "IsPromoted_12_28 = 1.0\n",
      "IsPromoted_12_29 = 1.0\n",
      "IsPromoted_12_3 = 1.0\n",
      "IsPromoted_12_30 = 1.0\n",
      "IsPromoted_12_4 = 1.0\n",
      "IsPromoted_12_5 = 1.0\n",
      "IsPromoted_12_6 = 1.0\n",
      "IsPromoted_12_7 = 1.0\n",
      "IsPromoted_12_8 = 1.0\n",
      "IsPromoted_12_9 = 1.0\n",
      "IsPromoted_1_0 = 1.0\n",
      "IsPromoted_1_1 = 1.0\n",
      "IsPromoted_1_10 = 1.0\n",
      "IsPromoted_1_11 = 1.0\n",
      "IsPromoted_1_12 = 1.0\n",
      "IsPromoted_1_13 = 1.0\n",
      "IsPromoted_1_14 = 1.0\n",
      "IsPromoted_1_15 = 1.0\n",
      "IsPromoted_1_16 = 1.0\n",
      "IsPromoted_1_17 = 1.0\n",
      "IsPromoted_1_18 = 1.0\n",
      "IsPromoted_1_19 = 1.0\n",
      "IsPromoted_1_2 = 1.0\n",
      "IsPromoted_1_20 = 1.0\n",
      "IsPromoted_1_21 = 1.0\n",
      "IsPromoted_1_22 = 1.0\n",
      "IsPromoted_1_23 = 1.0\n",
      "IsPromoted_1_24 = 1.0\n",
      "IsPromoted_1_25 = 1.0\n",
      "IsPromoted_1_26 = 1.0\n",
      "IsPromoted_1_27 = 1.0\n",
      "IsPromoted_1_28 = 1.0\n",
      "IsPromoted_1_29 = 1.0\n",
      "IsPromoted_1_3 = 1.0\n",
      "IsPromoted_1_30 = 1.0\n",
      "IsPromoted_1_4 = 1.0\n",
      "IsPromoted_1_5 = 1.0\n",
      "IsPromoted_1_6 = 1.0\n",
      "IsPromoted_1_7 = 1.0\n",
      "IsPromoted_1_8 = 1.0\n",
      "IsPromoted_1_9 = 1.0\n",
      "IsPromoted_2_0 = 1.0\n",
      "IsPromoted_2_1 = 1.0\n",
      "IsPromoted_2_10 = 1.0\n",
      "IsPromoted_2_11 = 1.0\n",
      "IsPromoted_2_12 = 1.0\n",
      "IsPromoted_2_13 = 1.0\n",
      "IsPromoted_2_14 = 1.0\n",
      "IsPromoted_2_15 = 1.0\n",
      "IsPromoted_2_16 = 1.0\n",
      "IsPromoted_2_17 = 1.0\n",
      "IsPromoted_2_18 = 1.0\n",
      "IsPromoted_2_19 = 1.0\n",
      "IsPromoted_2_2 = 1.0\n",
      "IsPromoted_2_20 = 1.0\n",
      "IsPromoted_2_21 = 1.0\n",
      "IsPromoted_2_22 = 1.0\n",
      "IsPromoted_2_23 = 1.0\n",
      "IsPromoted_2_24 = 1.0\n",
      "IsPromoted_2_25 = 1.0\n",
      "IsPromoted_2_26 = 1.0\n",
      "IsPromoted_2_27 = 1.0\n",
      "IsPromoted_2_28 = 1.0\n",
      "IsPromoted_2_29 = 1.0\n",
      "IsPromoted_2_3 = 1.0\n",
      "IsPromoted_2_30 = 1.0\n",
      "IsPromoted_2_4 = 1.0\n",
      "IsPromoted_2_5 = 1.0\n",
      "IsPromoted_2_6 = 1.0\n",
      "IsPromoted_2_7 = 1.0\n",
      "IsPromoted_2_8 = 1.0\n",
      "IsPromoted_2_9 = 1.0\n",
      "IsPromoted_3_0 = 1.0\n",
      "IsPromoted_3_1 = 1.0\n",
      "IsPromoted_3_10 = 1.0\n",
      "IsPromoted_3_11 = 1.0\n",
      "IsPromoted_3_12 = 1.0\n",
      "IsPromoted_3_13 = 1.0\n",
      "IsPromoted_3_14 = 1.0\n",
      "IsPromoted_3_15 = 1.0\n",
      "IsPromoted_3_16 = 1.0\n",
      "IsPromoted_3_17 = 1.0\n",
      "IsPromoted_3_18 = 1.0\n",
      "IsPromoted_3_19 = 1.0\n",
      "IsPromoted_3_2 = 1.0\n",
      "IsPromoted_3_20 = 1.0\n",
      "IsPromoted_3_21 = 1.0\n",
      "IsPromoted_3_22 = 1.0\n",
      "IsPromoted_3_23 = 1.0\n",
      "IsPromoted_3_24 = 1.0\n",
      "IsPromoted_3_25 = 1.0\n",
      "IsPromoted_3_26 = 1.0\n",
      "IsPromoted_3_27 = 1.0\n",
      "IsPromoted_3_28 = 1.0\n",
      "IsPromoted_3_29 = 1.0\n",
      "IsPromoted_3_3 = 1.0\n",
      "IsPromoted_3_30 = 1.0\n",
      "IsPromoted_3_4 = 1.0\n",
      "IsPromoted_3_5 = 1.0\n",
      "IsPromoted_3_6 = 1.0\n",
      "IsPromoted_3_7 = 1.0\n",
      "IsPromoted_3_8 = 1.0\n",
      "IsPromoted_3_9 = 1.0\n",
      "IsPromoted_4_0 = 1.0\n",
      "IsPromoted_4_1 = 1.0\n",
      "IsPromoted_4_10 = 1.0\n",
      "IsPromoted_4_11 = 1.0\n",
      "IsPromoted_4_12 = 1.0\n",
      "IsPromoted_4_13 = 1.0\n",
      "IsPromoted_4_14 = 1.0\n",
      "IsPromoted_4_15 = 1.0\n",
      "IsPromoted_4_16 = 1.0\n",
      "IsPromoted_4_17 = 1.0\n",
      "IsPromoted_4_18 = 1.0\n",
      "IsPromoted_4_19 = 1.0\n",
      "IsPromoted_4_2 = 1.0\n",
      "IsPromoted_4_20 = 1.0\n",
      "IsPromoted_4_21 = 1.0\n",
      "IsPromoted_4_22 = 1.0\n",
      "IsPromoted_4_23 = 1.0\n",
      "IsPromoted_4_24 = 1.0\n",
      "IsPromoted_4_25 = 1.0\n",
      "IsPromoted_4_26 = 1.0\n",
      "IsPromoted_4_27 = 1.0\n",
      "IsPromoted_4_28 = 1.0\n",
      "IsPromoted_4_29 = 1.0\n",
      "IsPromoted_4_3 = 1.0\n",
      "IsPromoted_4_30 = 1.0\n",
      "IsPromoted_4_4 = 1.0\n",
      "IsPromoted_4_5 = 1.0\n",
      "IsPromoted_4_6 = 1.0\n",
      "IsPromoted_4_7 = 1.0\n",
      "IsPromoted_4_8 = 1.0\n",
      "IsPromoted_4_9 = 1.0\n",
      "IsPromoted_5_0 = 1.0\n",
      "IsPromoted_5_1 = 1.0\n",
      "IsPromoted_5_10 = 1.0\n",
      "IsPromoted_5_11 = 1.0\n",
      "IsPromoted_5_12 = 1.0\n",
      "IsPromoted_5_13 = 1.0\n",
      "IsPromoted_5_14 = 1.0\n",
      "IsPromoted_5_15 = 1.0\n",
      "IsPromoted_5_16 = 1.0\n",
      "IsPromoted_5_17 = 1.0\n",
      "IsPromoted_5_18 = 1.0\n",
      "IsPromoted_5_19 = 1.0\n",
      "IsPromoted_5_2 = 1.0\n",
      "IsPromoted_5_20 = 1.0\n",
      "IsPromoted_5_21 = 1.0\n",
      "IsPromoted_5_22 = 1.0\n",
      "IsPromoted_5_23 = 1.0\n",
      "IsPromoted_5_24 = 1.0\n",
      "IsPromoted_5_25 = 1.0\n",
      "IsPromoted_5_26 = 1.0\n",
      "IsPromoted_5_27 = 1.0\n",
      "IsPromoted_5_28 = 1.0\n",
      "IsPromoted_5_29 = 1.0\n",
      "IsPromoted_5_3 = 1.0\n",
      "IsPromoted_5_30 = 1.0\n",
      "IsPromoted_5_4 = 1.0\n",
      "IsPromoted_5_5 = 1.0\n",
      "IsPromoted_5_6 = 1.0\n",
      "IsPromoted_5_7 = 1.0\n",
      "IsPromoted_5_8 = 1.0\n",
      "IsPromoted_5_9 = 1.0\n",
      "IsPromoted_6_0 = 1.0\n",
      "IsPromoted_6_1 = 1.0\n",
      "IsPromoted_6_10 = 1.0\n",
      "IsPromoted_6_11 = 1.0\n",
      "IsPromoted_6_12 = 1.0\n",
      "IsPromoted_6_13 = 1.0\n",
      "IsPromoted_6_14 = 1.0\n",
      "IsPromoted_6_15 = 1.0\n",
      "IsPromoted_6_16 = 1.0\n",
      "IsPromoted_6_17 = 1.0\n",
      "IsPromoted_6_18 = 1.0\n",
      "IsPromoted_6_19 = 1.0\n",
      "IsPromoted_6_2 = 1.0\n",
      "IsPromoted_6_20 = 1.0\n",
      "IsPromoted_6_21 = 1.0\n",
      "IsPromoted_6_22 = 1.0\n",
      "IsPromoted_6_23 = 1.0\n",
      "IsPromoted_6_24 = 1.0\n",
      "IsPromoted_6_25 = 1.0\n",
      "IsPromoted_6_26 = 1.0\n",
      "IsPromoted_6_27 = 1.0\n",
      "IsPromoted_6_28 = 1.0\n",
      "IsPromoted_6_29 = 1.0\n",
      "IsPromoted_6_3 = 1.0\n",
      "IsPromoted_6_30 = 1.0\n",
      "IsPromoted_6_4 = 1.0\n",
      "IsPromoted_6_5 = 1.0\n",
      "IsPromoted_6_6 = 1.0\n",
      "IsPromoted_6_7 = 1.0\n",
      "IsPromoted_6_8 = 1.0\n",
      "IsPromoted_6_9 = 1.0\n",
      "IsPromoted_7_0 = 1.0\n",
      "IsPromoted_7_1 = 1.0\n",
      "IsPromoted_7_10 = 1.0\n",
      "IsPromoted_7_11 = 1.0\n",
      "IsPromoted_7_12 = 1.0\n",
      "IsPromoted_7_13 = 1.0\n",
      "IsPromoted_7_14 = 1.0\n",
      "IsPromoted_7_15 = 1.0\n",
      "IsPromoted_7_16 = 1.0\n",
      "IsPromoted_7_17 = 1.0\n",
      "IsPromoted_7_18 = 1.0\n",
      "IsPromoted_7_19 = 1.0\n",
      "IsPromoted_7_2 = 1.0\n",
      "IsPromoted_7_20 = 1.0\n",
      "IsPromoted_7_21 = 1.0\n",
      "IsPromoted_7_22 = 1.0\n",
      "IsPromoted_7_23 = 1.0\n",
      "IsPromoted_7_24 = 1.0\n",
      "IsPromoted_7_25 = 1.0\n",
      "IsPromoted_7_26 = 1.0\n",
      "IsPromoted_7_27 = 1.0\n",
      "IsPromoted_7_28 = 1.0\n",
      "IsPromoted_7_29 = 1.0\n",
      "IsPromoted_7_3 = 1.0\n",
      "IsPromoted_7_30 = 1.0\n",
      "IsPromoted_7_4 = 1.0\n",
      "IsPromoted_7_5 = 1.0\n",
      "IsPromoted_7_6 = 1.0\n",
      "IsPromoted_7_7 = 1.0\n",
      "IsPromoted_7_8 = 1.0\n",
      "IsPromoted_7_9 = 1.0\n",
      "IsPromoted_8_0 = 1.0\n",
      "IsPromoted_8_1 = 1.0\n",
      "IsPromoted_8_10 = 1.0\n",
      "IsPromoted_8_11 = 1.0\n",
      "IsPromoted_8_12 = 1.0\n",
      "IsPromoted_8_13 = 1.0\n",
      "IsPromoted_8_14 = 1.0\n",
      "IsPromoted_8_15 = 1.0\n",
      "IsPromoted_8_16 = 1.0\n",
      "IsPromoted_8_17 = 1.0\n",
      "IsPromoted_8_18 = 1.0\n",
      "IsPromoted_8_19 = 1.0\n",
      "IsPromoted_8_2 = 1.0\n",
      "IsPromoted_8_20 = 1.0\n",
      "IsPromoted_8_21 = 1.0\n",
      "IsPromoted_8_22 = 1.0\n",
      "IsPromoted_8_23 = 1.0\n",
      "IsPromoted_8_24 = 1.0\n",
      "IsPromoted_8_25 = 1.0\n",
      "IsPromoted_8_26 = 1.0\n",
      "IsPromoted_8_27 = 1.0\n",
      "IsPromoted_8_28 = 1.0\n",
      "IsPromoted_8_29 = 1.0\n",
      "IsPromoted_8_3 = 1.0\n",
      "IsPromoted_8_30 = 1.0\n",
      "IsPromoted_8_4 = 1.0\n",
      "IsPromoted_8_5 = 1.0\n",
      "IsPromoted_8_6 = 1.0\n",
      "IsPromoted_8_7 = 1.0\n",
      "IsPromoted_8_8 = 1.0\n",
      "IsPromoted_8_9 = 1.0\n",
      "IsPromoted_9_0 = 1.0\n",
      "IsPromoted_9_1 = 1.0\n",
      "IsPromoted_9_10 = 1.0\n",
      "IsPromoted_9_11 = 1.0\n",
      "IsPromoted_9_12 = 1.0\n",
      "IsPromoted_9_13 = 1.0\n",
      "IsPromoted_9_14 = 1.0\n",
      "IsPromoted_9_15 = 1.0\n",
      "IsPromoted_9_16 = 1.0\n",
      "IsPromoted_9_17 = 1.0\n",
      "IsPromoted_9_18 = 1.0\n",
      "IsPromoted_9_19 = 1.0\n",
      "IsPromoted_9_2 = 1.0\n",
      "IsPromoted_9_20 = 1.0\n",
      "IsPromoted_9_21 = 1.0\n",
      "IsPromoted_9_22 = 1.0\n",
      "IsPromoted_9_23 = 1.0\n",
      "IsPromoted_9_24 = 1.0\n",
      "IsPromoted_9_25 = 1.0\n",
      "IsPromoted_9_26 = 1.0\n",
      "IsPromoted_9_27 = 1.0\n",
      "IsPromoted_9_28 = 1.0\n",
      "IsPromoted_9_29 = 1.0\n",
      "IsPromoted_9_3 = 1.0\n",
      "IsPromoted_9_30 = 1.0\n",
      "IsPromoted_9_4 = 1.0\n",
      "IsPromoted_9_5 = 1.0\n",
      "IsPromoted_9_6 = 1.0\n",
      "IsPromoted_9_7 = 1.0\n",
      "IsPromoted_9_8 = 1.0\n",
      "IsPromoted_9_9 = 1.0\n",
      "Paid_Quantity_0_0 = 887.0\n",
      "Paid_Quantity_0_1 = 809.0\n",
      "Paid_Quantity_0_10 = 766.0\n",
      "Paid_Quantity_0_11 = 783.0\n",
      "Paid_Quantity_0_12 = 777.0\n",
      "Paid_Quantity_0_13 = 808.0\n",
      "Paid_Quantity_0_14 = 788.0\n",
      "Paid_Quantity_0_15 = 865.0\n",
      "Paid_Quantity_0_16 = 869.0\n",
      "Paid_Quantity_0_17 = 875.0\n",
      "Paid_Quantity_0_18 = 848.0\n",
      "Paid_Quantity_0_19 = 880.0\n",
      "Paid_Quantity_0_2 = 790.0\n",
      "Paid_Quantity_0_20 = 881.0\n",
      "Paid_Quantity_0_21 = 873.0\n",
      "Paid_Quantity_0_22 = 858.0\n",
      "Paid_Quantity_0_23 = 885.0\n",
      "Paid_Quantity_0_24 = 887.0\n",
      "Paid_Quantity_0_25 = 889.0\n",
      "Paid_Quantity_0_26 = 898.0\n",
      "Paid_Quantity_0_27 = 909.0\n",
      "Paid_Quantity_0_28 = 861.0\n",
      "Paid_Quantity_0_29 = 818.0\n",
      "Paid_Quantity_0_3 = 778.0\n",
      "Paid_Quantity_0_30 = 858.0\n",
      "Paid_Quantity_0_4 = 815.0\n",
      "Paid_Quantity_0_5 = 745.0\n",
      "Paid_Quantity_0_6 = 800.0\n",
      "Paid_Quantity_0_7 = 719.0\n",
      "Paid_Quantity_0_8 = 804.0\n",
      "Paid_Quantity_0_9 = 837.0\n",
      "Paid_Quantity_10_0 = 1221.0\n",
      "Paid_Quantity_10_1 = 1080.0\n",
      "Paid_Quantity_10_10 = 990.0\n",
      "Paid_Quantity_10_11 = 1026.0\n",
      "Paid_Quantity_10_12 = 1066.0\n",
      "Paid_Quantity_10_13 = 1132.0\n",
      "Paid_Quantity_10_14 = 1040.0\n",
      "Paid_Quantity_10_15 = 1160.0\n",
      "Paid_Quantity_10_16 = 1173.0\n",
      "Paid_Quantity_10_17 = 1222.0\n",
      "Paid_Quantity_10_18 = 1150.0\n",
      "Paid_Quantity_10_19 = 1154.0\n",
      "Paid_Quantity_10_2 = 1082.0\n",
      "Paid_Quantity_10_20 = 1184.0\n",
      "Paid_Quantity_10_21 = 1129.0\n",
      "Paid_Quantity_10_22 = 1162.0\n",
      "Paid_Quantity_10_23 = 1160.0\n",
      "Paid_Quantity_10_24 = 1163.0\n",
      "Paid_Quantity_10_25 = 1186.0\n",
      "Paid_Quantity_10_26 = 1168.0\n",
      "Paid_Quantity_10_27 = 1167.0\n",
      "Paid_Quantity_10_28 = 1124.0\n",
      "Paid_Quantity_10_29 = 1061.0\n",
      "Paid_Quantity_10_3 = 1036.0\n",
      "Paid_Quantity_10_30 = 1168.0\n",
      "Paid_Quantity_10_4 = 1077.0\n",
      "Paid_Quantity_10_5 = 943.0\n",
      "Paid_Quantity_10_6 = 1146.0\n",
      "Paid_Quantity_10_7 = 1036.0\n",
      "Paid_Quantity_10_8 = 1078.0\n",
      "Paid_Quantity_10_9 = 1122.0\n",
      "Paid_Quantity_11_0 = 794.0\n",
      "Paid_Quantity_11_1 = 687.0\n",
      "Paid_Quantity_11_10 = 698.0\n",
      "Paid_Quantity_11_11 = 709.0\n",
      "Paid_Quantity_11_12 = 727.0\n",
      "Paid_Quantity_11_13 = 702.0\n",
      "Paid_Quantity_11_14 = 686.0\n",
      "Paid_Quantity_11_15 = 703.0\n",
      "Paid_Quantity_11_16 = 760.0\n",
      "Paid_Quantity_11_17 = 697.0\n",
      "Paid_Quantity_11_18 = 693.0\n",
      "Paid_Quantity_11_19 = 727.0\n",
      "Paid_Quantity_11_2 = 705.0\n",
      "Paid_Quantity_11_20 = 717.0\n",
      "Paid_Quantity_11_21 = 637.0\n",
      "Paid_Quantity_11_22 = 727.0\n",
      "Paid_Quantity_11_23 = 770.0\n",
      "Paid_Quantity_11_24 = 710.0\n",
      "Paid_Quantity_11_25 = 709.0\n",
      "Paid_Quantity_11_26 = 674.0\n",
      "Paid_Quantity_11_27 = 678.0\n",
      "Paid_Quantity_11_28 = 662.0\n",
      "Paid_Quantity_11_29 = 616.0\n",
      "Paid_Quantity_11_3 = 645.0\n",
      "Paid_Quantity_11_30 = 468.0\n",
      "Paid_Quantity_11_4 = 668.0\n",
      "Paid_Quantity_11_5 = 688.0\n",
      "Paid_Quantity_11_6 = 722.0\n",
      "Paid_Quantity_11_7 = 698.0\n",
      "Paid_Quantity_11_8 = 696.0\n",
      "Paid_Quantity_11_9 = 719.0\n",
      "Paid_Quantity_12_0 = 4371.0\n",
      "Paid_Quantity_12_1 = 4179.0\n",
      "Paid_Quantity_12_10 = 3808.0\n",
      "Paid_Quantity_12_11 = 3711.0\n",
      "Paid_Quantity_12_12 = 4119.0\n",
      "Paid_Quantity_12_13 = 4011.0\n",
      "Paid_Quantity_12_14 = 3801.0\n",
      "Paid_Quantity_12_15 = 4191.0\n",
      "Paid_Quantity_12_16 = 4171.0\n",
      "Paid_Quantity_12_17 = 3935.0\n",
      "Paid_Quantity_12_18 = 3758.0\n",
      "Paid_Quantity_12_19 = 3967.0\n",
      "Paid_Quantity_12_2 = 4108.0\n",
      "Paid_Quantity_12_20 = 3923.0\n",
      "Paid_Quantity_12_21 = 3215.0\n",
      "Paid_Quantity_12_22 = 3510.0\n",
      "Paid_Quantity_12_23 = 3796.0\n",
      "Paid_Quantity_12_24 = 3821.0\n",
      "Paid_Quantity_12_25 = 3575.0\n",
      "Paid_Quantity_12_26 = 3696.0\n",
      "Paid_Quantity_12_27 = 3649.0\n",
      "Paid_Quantity_12_28 = 3545.0\n",
      "Paid_Quantity_12_29 = 3459.0\n",
      "Paid_Quantity_12_3 = 3329.0\n",
      "Paid_Quantity_12_30 = 3556.0\n",
      "Paid_Quantity_12_4 = 3328.0\n",
      "Paid_Quantity_12_5 = 3589.0\n",
      "Paid_Quantity_12_6 = 3890.0\n",
      "Paid_Quantity_12_7 = 3755.0\n",
      "Paid_Quantity_12_8 = 3940.0\n",
      "Paid_Quantity_12_9 = 4124.0\n",
      "Paid_Quantity_1_0 = 1426.0\n",
      "Paid_Quantity_1_1 = 1239.0\n",
      "Paid_Quantity_1_10 = 1199.0\n",
      "Paid_Quantity_1_11 = 1201.0\n",
      "Paid_Quantity_1_12 = 1212.0\n",
      "Paid_Quantity_1_13 = 1259.0\n",
      "Paid_Quantity_1_14 = 1238.0\n",
      "Paid_Quantity_1_15 = 1322.0\n",
      "Paid_Quantity_1_16 = 1377.0\n",
      "Paid_Quantity_1_17 = 1354.0\n",
      "Paid_Quantity_1_18 = 1346.0\n",
      "Paid_Quantity_1_19 = 1372.0\n",
      "Paid_Quantity_1_2 = 1238.0\n",
      "Paid_Quantity_1_20 = 1353.0\n",
      "Paid_Quantity_1_21 = 1295.0\n",
      "Paid_Quantity_1_22 = 1365.0\n",
      "Paid_Quantity_1_23 = 1345.0\n",
      "Paid_Quantity_1_24 = 1351.0\n",
      "Paid_Quantity_1_25 = 1388.0\n",
      "Paid_Quantity_1_26 = 1355.0\n",
      "Paid_Quantity_1_27 = 1339.0\n",
      "Paid_Quantity_1_28 = 1271.0\n",
      "Paid_Quantity_1_29 = 1203.0\n",
      "Paid_Quantity_1_3 = 1191.0\n",
      "Paid_Quantity_1_30 = 1293.0\n",
      "Paid_Quantity_1_4 = 1233.0\n",
      "Paid_Quantity_1_5 = 1208.0\n",
      "Paid_Quantity_1_6 = 1229.0\n",
      "Paid_Quantity_1_7 = 1165.0\n",
      "Paid_Quantity_1_8 = 1283.0\n",
      "Paid_Quantity_1_9 = 1249.0\n",
      "Paid_Quantity_2_0 = 8555.0\n",
      "Paid_Quantity_2_1 = 7396.0\n",
      "Paid_Quantity_2_10 = 6466.0\n",
      "Paid_Quantity_2_11 = 6418.0\n",
      "Paid_Quantity_2_12 = 6559.0\n",
      "Paid_Quantity_2_13 = 7138.0\n",
      "Paid_Quantity_2_14 = 6502.0\n",
      "Paid_Quantity_2_15 = 7324.0\n",
      "Paid_Quantity_2_16 = 7508.0\n",
      "Paid_Quantity_2_17 = 7848.0\n",
      "Paid_Quantity_2_18 = 7170.0\n",
      "Paid_Quantity_2_19 = 7504.0\n",
      "Paid_Quantity_2_2 = 7028.0\n",
      "Paid_Quantity_2_20 = 7537.0\n",
      "Paid_Quantity_2_21 = 7165.0\n",
      "Paid_Quantity_2_22 = 7252.0\n",
      "Paid_Quantity_2_23 = 7199.0\n",
      "Paid_Quantity_2_24 = 7238.0\n",
      "Paid_Quantity_2_25 = 7501.0\n",
      "Paid_Quantity_2_26 = 7051.0\n",
      "Paid_Quantity_2_27 = 7430.0\n",
      "Paid_Quantity_2_28 = 7140.0\n",
      "Paid_Quantity_2_29 = 6484.0\n",
      "Paid_Quantity_2_3 = 6280.0\n",
      "Paid_Quantity_2_30 = 7106.0\n",
      "Paid_Quantity_2_4 = 6563.0\n",
      "Paid_Quantity_2_5 = 6795.0\n",
      "Paid_Quantity_2_6 = 7149.0\n",
      "Paid_Quantity_2_7 = 6127.0\n",
      "Paid_Quantity_2_8 = 6763.0\n",
      "Paid_Quantity_2_9 = 6653.0\n",
      "Paid_Quantity_3_0 = 6986.0\n",
      "Paid_Quantity_3_1 = 5183.0\n",
      "Paid_Quantity_3_10 = 4508.0\n",
      "Paid_Quantity_3_11 = 4538.0\n",
      "Paid_Quantity_3_12 = 4695.0\n",
      "Paid_Quantity_3_13 = 5348.0\n",
      "Paid_Quantity_3_14 = 4703.0\n",
      "Paid_Quantity_3_15 = 5197.0\n",
      "Paid_Quantity_3_16 = 5537.0\n",
      "Paid_Quantity_3_17 = 5639.0\n",
      "Paid_Quantity_3_18 = 5055.0\n",
      "Paid_Quantity_3_19 = 5314.0\n",
      "Paid_Quantity_3_2 = 5401.0\n",
      "Paid_Quantity_3_20 = 5455.0\n",
      "Paid_Quantity_3_21 = 5164.0\n",
      "Paid_Quantity_3_22 = 5114.0\n",
      "Paid_Quantity_3_23 = 5266.0\n",
      "Paid_Quantity_3_24 = 5382.0\n",
      "Paid_Quantity_3_25 = 5368.0\n",
      "Paid_Quantity_3_26 = 5135.0\n",
      "Paid_Quantity_3_27 = 5186.0\n",
      "Paid_Quantity_3_28 = 4654.0\n",
      "Paid_Quantity_3_29 = 4344.0\n",
      "Paid_Quantity_3_3 = 4706.0\n",
      "Paid_Quantity_3_30 = 5078.0\n",
      "Paid_Quantity_3_4 = 4638.0\n",
      "Paid_Quantity_3_5 = 4667.0\n",
      "Paid_Quantity_3_6 = 4858.0\n",
      "Paid_Quantity_3_7 = 4301.0\n",
      "Paid_Quantity_3_8 = 4635.0\n",
      "Paid_Quantity_3_9 = 4890.0\n",
      "Paid_Quantity_4_0 = 9796.0\n",
      "Paid_Quantity_4_1 = 7038.0\n",
      "Paid_Quantity_4_10 = 6772.0\n",
      "Paid_Quantity_4_11 = 6599.0\n",
      "Paid_Quantity_4_12 = 7460.0\n",
      "Paid_Quantity_4_13 = 8403.0\n",
      "Paid_Quantity_4_14 = 7304.0\n",
      "Paid_Quantity_4_15 = 6353.0\n",
      "Paid_Quantity_4_16 = 6561.0\n",
      "Paid_Quantity_4_17 = 6288.0\n",
      "Paid_Quantity_4_18 = 5908.0\n",
      "Paid_Quantity_4_19 = 7747.0\n",
      "Paid_Quantity_4_2 = 7813.0\n",
      "Paid_Quantity_4_20 = 7976.0\n",
      "Paid_Quantity_4_21 = 7717.0\n",
      "Paid_Quantity_4_22 = 6743.0\n",
      "Paid_Quantity_4_23 = 7250.0\n",
      "Paid_Quantity_4_24 = 6320.0\n",
      "Paid_Quantity_4_25 = 5182.0\n",
      "Paid_Quantity_4_26 = 6647.0\n",
      "Paid_Quantity_4_27 = 6486.0\n",
      "Paid_Quantity_4_28 = 6360.0\n",
      "Paid_Quantity_4_29 = 6319.0\n",
      "Paid_Quantity_4_3 = 7940.0\n",
      "Paid_Quantity_4_30 = 7026.0\n",
      "Paid_Quantity_4_4 = 8218.0\n",
      "Paid_Quantity_4_5 = 8199.0\n",
      "Paid_Quantity_4_6 = 8616.0\n",
      "Paid_Quantity_4_7 = 10337.0\n",
      "Paid_Quantity_4_8 = 7976.0\n",
      "Paid_Quantity_4_9 = 7513.0\n",
      "Paid_Quantity_5_0 = 3857.0\n",
      "Paid_Quantity_5_1 = 3349.0\n",
      "Paid_Quantity_5_10 = 2857.0\n",
      "Paid_Quantity_5_11 = 3035.0\n",
      "Paid_Quantity_5_12 = 3178.0\n",
      "Paid_Quantity_5_13 = 3213.0\n",
      "Paid_Quantity_5_14 = 3181.0\n",
      "Paid_Quantity_5_15 = 3226.0\n",
      "Paid_Quantity_5_16 = 3360.0\n",
      "Paid_Quantity_5_17 = 3253.0\n",
      "Paid_Quantity_5_18 = 3202.0\n",
      "Paid_Quantity_5_19 = 3187.0\n",
      "Paid_Quantity_5_2 = 3305.0\n",
      "Paid_Quantity_5_20 = 3170.0\n",
      "Paid_Quantity_5_21 = 3119.0\n",
      "Paid_Quantity_5_22 = 3140.0\n",
      "Paid_Quantity_5_23 = 3080.0\n",
      "Paid_Quantity_5_24 = 3245.0\n",
      "Paid_Quantity_5_25 = 3101.0\n",
      "Paid_Quantity_5_26 = 3080.0\n",
      "Paid_Quantity_5_27 = 3075.0\n",
      "Paid_Quantity_5_28 = 2949.0\n",
      "Paid_Quantity_5_29 = 2720.0\n",
      "Paid_Quantity_5_3 = 3248.0\n",
      "Paid_Quantity_5_30 = 2941.0\n",
      "Paid_Quantity_5_4 = 3170.0\n",
      "Paid_Quantity_5_5 = 3125.0\n",
      "Paid_Quantity_5_6 = 3043.0\n",
      "Paid_Quantity_5_7 = 2864.0\n",
      "Paid_Quantity_5_8 = 3032.0\n",
      "Paid_Quantity_5_9 = 3171.0\n",
      "Paid_Quantity_6_0 = 1919.0\n",
      "Paid_Quantity_6_1 = 1821.0\n",
      "Paid_Quantity_6_10 = 1815.0\n",
      "Paid_Quantity_6_11 = 1809.0\n",
      "Paid_Quantity_6_12 = 1762.0\n",
      "Paid_Quantity_6_13 = 1873.0\n",
      "Paid_Quantity_6_14 = 1842.0\n",
      "Paid_Quantity_6_15 = 1895.0\n",
      "Paid_Quantity_6_16 = 1932.0\n",
      "Paid_Quantity_6_17 = 1960.0\n",
      "Paid_Quantity_6_18 = 1875.0\n",
      "Paid_Quantity_6_19 = 1906.0\n",
      "Paid_Quantity_6_2 = 1812.0\n",
      "Paid_Quantity_6_20 = 1921.0\n",
      "Paid_Quantity_6_21 = 1879.0\n",
      "Paid_Quantity_6_22 = 1885.0\n",
      "Paid_Quantity_6_23 = 1906.0\n",
      "Paid_Quantity_6_24 = 1937.0\n",
      "Paid_Quantity_6_25 = 1931.0\n",
      "Paid_Quantity_6_26 = 1852.0\n",
      "Paid_Quantity_6_27 = 1887.0\n",
      "Paid_Quantity_6_28 = 1856.0\n",
      "Paid_Quantity_6_29 = 1762.0\n",
      "Paid_Quantity_6_3 = 1815.0\n",
      "Paid_Quantity_6_30 = 1925.0\n",
      "Paid_Quantity_6_4 = 1812.0\n",
      "Paid_Quantity_6_5 = 1755.0\n",
      "Paid_Quantity_6_6 = 1842.0\n",
      "Paid_Quantity_6_7 = 1775.0\n",
      "Paid_Quantity_6_8 = 1847.0\n",
      "Paid_Quantity_6_9 = 1805.0\n",
      "Paid_Quantity_7_0 = 3794.0\n",
      "Paid_Quantity_7_1 = 3437.0\n",
      "Paid_Quantity_7_10 = 3224.0\n",
      "Paid_Quantity_7_11 = 3242.0\n",
      "Paid_Quantity_7_12 = 3279.0\n",
      "Paid_Quantity_7_13 = 3482.0\n",
      "Paid_Quantity_7_14 = 3207.0\n",
      "Paid_Quantity_7_15 = 3373.0\n",
      "Paid_Quantity_7_16 = 3550.0\n",
      "Paid_Quantity_7_17 = 3525.0\n",
      "Paid_Quantity_7_18 = 3502.0\n",
      "Paid_Quantity_7_19 = 3527.0\n",
      "Paid_Quantity_7_2 = 3563.0\n",
      "Paid_Quantity_7_20 = 3515.0\n",
      "Paid_Quantity_7_21 = 3387.0\n",
      "Paid_Quantity_7_22 = 3424.0\n",
      "Paid_Quantity_7_23 = 3515.0\n",
      "Paid_Quantity_7_24 = 3493.0\n",
      "Paid_Quantity_7_25 = 3521.0\n",
      "Paid_Quantity_7_26 = 3484.0\n",
      "Paid_Quantity_7_27 = 3476.0\n",
      "Paid_Quantity_7_28 = 3338.0\n",
      "Paid_Quantity_7_29 = 3254.0\n",
      "Paid_Quantity_7_3 = 3467.0\n",
      "Paid_Quantity_7_30 = 3491.0\n",
      "Paid_Quantity_7_4 = 3460.0\n",
      "Paid_Quantity_7_5 = 3380.0\n",
      "Paid_Quantity_7_6 = 3354.0\n",
      "Paid_Quantity_7_7 = 3099.0\n",
      "Paid_Quantity_7_8 = 3304.0\n",
      "Paid_Quantity_7_9 = 3348.0\n",
      "Paid_Quantity_8_0 = 8192.0\n",
      "Paid_Quantity_8_1 = 7474.0\n",
      "Paid_Quantity_8_10 = 7156.0\n",
      "Paid_Quantity_8_11 = 6990.0\n",
      "Paid_Quantity_8_12 = 7138.0\n",
      "Paid_Quantity_8_13 = 7789.0\n",
      "Paid_Quantity_8_14 = 7298.0\n",
      "Paid_Quantity_8_15 = 7156.0\n",
      "Paid_Quantity_8_16 = 7089.0\n",
      "Paid_Quantity_8_17 = 7145.0\n",
      "Paid_Quantity_8_18 = 6785.0\n",
      "Paid_Quantity_8_19 = 7052.0\n",
      "Paid_Quantity_8_2 = 7483.0\n",
      "Paid_Quantity_8_20 = 7034.0\n",
      "Paid_Quantity_8_21 = 6975.0\n",
      "Paid_Quantity_8_22 = 7032.0\n",
      "Paid_Quantity_8_23 = 7062.0\n",
      "Paid_Quantity_8_24 = 6860.0\n",
      "Paid_Quantity_8_25 = 7001.0\n",
      "Paid_Quantity_8_26 = 6830.0\n",
      "Paid_Quantity_8_27 = 7047.0\n",
      "Paid_Quantity_8_28 = 6915.0\n",
      "Paid_Quantity_8_29 = 6743.0\n",
      "Paid_Quantity_8_3 = 7200.0\n",
      "Paid_Quantity_8_30 = 7108.0\n",
      "Paid_Quantity_8_4 = 7132.0\n",
      "Paid_Quantity_8_5 = 7099.0\n",
      "Paid_Quantity_8_6 = 7401.0\n",
      "Paid_Quantity_8_7 = 7100.0\n",
      "Paid_Quantity_8_8 = 7345.0\n",
      "Paid_Quantity_8_9 = 7221.0\n",
      "Paid_Quantity_9_0 = 1132.0\n",
      "Paid_Quantity_9_1 = 1056.0\n",
      "Paid_Quantity_9_10 = 1038.0\n",
      "Paid_Quantity_9_11 = 1058.0\n",
      "Paid_Quantity_9_12 = 1042.0\n",
      "Paid_Quantity_9_13 = 1121.0\n",
      "Paid_Quantity_9_14 = 955.0\n",
      "Paid_Quantity_9_15 = 996.0\n",
      "Paid_Quantity_9_16 = 1080.0\n",
      "Paid_Quantity_9_17 = 1058.0\n",
      "Paid_Quantity_9_18 = 997.0\n",
      "Paid_Quantity_9_19 = 1057.0\n",
      "Paid_Quantity_9_2 = 1070.0\n",
      "Paid_Quantity_9_20 = 1015.0\n",
      "Paid_Quantity_9_21 = 1042.0\n",
      "Paid_Quantity_9_22 = 1028.0\n",
      "Paid_Quantity_9_23 = 1065.0\n",
      "Paid_Quantity_9_24 = 1047.0\n",
      "Paid_Quantity_9_25 = 1043.0\n",
      "Paid_Quantity_9_26 = 1006.0\n",
      "Paid_Quantity_9_27 = 1036.0\n",
      "Paid_Quantity_9_28 = 997.0\n",
      "Paid_Quantity_9_29 = 953.0\n",
      "Paid_Quantity_9_3 = 1082.0\n",
      "Paid_Quantity_9_30 = 1070.0\n",
      "Paid_Quantity_9_4 = 1092.0\n",
      "Paid_Quantity_9_5 = 1069.0\n",
      "Paid_Quantity_9_6 = 1071.0\n",
      "Paid_Quantity_9_7 = 1061.0\n",
      "Paid_Quantity_9_8 = 1013.0\n",
      "Paid_Quantity_9_9 = 1046.0\n",
      "Promo_Length_0 = 31.0\n",
      "Promo_Length_1 = 31.0\n",
      "Promo_Length_10 = 31.0\n",
      "Promo_Length_11 = 31.0\n",
      "Promo_Length_12 = 31.0\n",
      "Promo_Length_2 = 31.0\n",
      "Promo_Length_3 = 31.0\n",
      "Promo_Length_4 = 31.0\n",
      "Promo_Length_5 = 31.0\n",
      "Promo_Length_6 = 31.0\n",
      "Promo_Length_7 = 31.0\n",
      "Promo_Length_8 = 31.0\n",
      "Promo_Length_9 = 31.0\n"
     ]
    }
   ],
   "source": [
    "# OPTIMIZED OBJECTIVE FUNCTION\n",
    "print(\"Optimal Total Paid Quantity = \", pulp.value(Promo_Model.objective),\"\\n\")\n",
    "\n",
    "# PRINTS VARIABLES AND OPTIMAL VALUES\n",
    "for v in Promo_Model.variables():\n",
    "    print(v.name, \"=\", v.varValue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3501b7",
   "metadata": {},
   "source": [
    "### 4.6. Promo Ratio Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "27b8e2b5",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1087</td>\n",
       "      <td>1009</td>\n",
       "      <td>990</td>\n",
       "      <td>978</td>\n",
       "      <td>1015</td>\n",
       "      <td>945</td>\n",
       "      <td>1000</td>\n",
       "      <td>919</td>\n",
       "      <td>1004</td>\n",
       "      <td>1037</td>\n",
       "      <td>...</td>\n",
       "      <td>1073</td>\n",
       "      <td>1058</td>\n",
       "      <td>1085</td>\n",
       "      <td>1087</td>\n",
       "      <td>1089</td>\n",
       "      <td>1098</td>\n",
       "      <td>1109</td>\n",
       "      <td>1061</td>\n",
       "      <td>1018</td>\n",
       "      <td>1058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1700</td>\n",
       "      <td>1513</td>\n",
       "      <td>1512</td>\n",
       "      <td>1465</td>\n",
       "      <td>1507</td>\n",
       "      <td>1482</td>\n",
       "      <td>1503</td>\n",
       "      <td>1439</td>\n",
       "      <td>1557</td>\n",
       "      <td>1523</td>\n",
       "      <td>...</td>\n",
       "      <td>1569</td>\n",
       "      <td>1639</td>\n",
       "      <td>1619</td>\n",
       "      <td>1625</td>\n",
       "      <td>1662</td>\n",
       "      <td>1629</td>\n",
       "      <td>1613</td>\n",
       "      <td>1545</td>\n",
       "      <td>1477</td>\n",
       "      <td>1567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8904</td>\n",
       "      <td>7745</td>\n",
       "      <td>7377</td>\n",
       "      <td>6629</td>\n",
       "      <td>6912</td>\n",
       "      <td>7144</td>\n",
       "      <td>7498</td>\n",
       "      <td>6476</td>\n",
       "      <td>7112</td>\n",
       "      <td>7002</td>\n",
       "      <td>...</td>\n",
       "      <td>7514</td>\n",
       "      <td>7601</td>\n",
       "      <td>7548</td>\n",
       "      <td>7587</td>\n",
       "      <td>7850</td>\n",
       "      <td>7400</td>\n",
       "      <td>7779</td>\n",
       "      <td>7489</td>\n",
       "      <td>6833</td>\n",
       "      <td>7455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7306</td>\n",
       "      <td>5503</td>\n",
       "      <td>5721</td>\n",
       "      <td>5026</td>\n",
       "      <td>4958</td>\n",
       "      <td>4987</td>\n",
       "      <td>5178</td>\n",
       "      <td>4621</td>\n",
       "      <td>4955</td>\n",
       "      <td>5210</td>\n",
       "      <td>...</td>\n",
       "      <td>5484</td>\n",
       "      <td>5434</td>\n",
       "      <td>5586</td>\n",
       "      <td>5702</td>\n",
       "      <td>5688</td>\n",
       "      <td>5455</td>\n",
       "      <td>5506</td>\n",
       "      <td>4974</td>\n",
       "      <td>4664</td>\n",
       "      <td>5398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9901</td>\n",
       "      <td>7143</td>\n",
       "      <td>7918</td>\n",
       "      <td>8045</td>\n",
       "      <td>8323</td>\n",
       "      <td>8304</td>\n",
       "      <td>8721</td>\n",
       "      <td>10442</td>\n",
       "      <td>8081</td>\n",
       "      <td>7618</td>\n",
       "      <td>...</td>\n",
       "      <td>7822</td>\n",
       "      <td>6848</td>\n",
       "      <td>7355</td>\n",
       "      <td>6425</td>\n",
       "      <td>5287</td>\n",
       "      <td>6752</td>\n",
       "      <td>6591</td>\n",
       "      <td>6465</td>\n",
       "      <td>6424</td>\n",
       "      <td>7131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5247</td>\n",
       "      <td>4739</td>\n",
       "      <td>4695</td>\n",
       "      <td>4638</td>\n",
       "      <td>4560</td>\n",
       "      <td>4515</td>\n",
       "      <td>4433</td>\n",
       "      <td>4254</td>\n",
       "      <td>4422</td>\n",
       "      <td>4561</td>\n",
       "      <td>...</td>\n",
       "      <td>4509</td>\n",
       "      <td>4530</td>\n",
       "      <td>4470</td>\n",
       "      <td>4635</td>\n",
       "      <td>4491</td>\n",
       "      <td>4470</td>\n",
       "      <td>4465</td>\n",
       "      <td>4339</td>\n",
       "      <td>4110</td>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2178</td>\n",
       "      <td>2080</td>\n",
       "      <td>2071</td>\n",
       "      <td>2074</td>\n",
       "      <td>2071</td>\n",
       "      <td>2014</td>\n",
       "      <td>2101</td>\n",
       "      <td>2034</td>\n",
       "      <td>2106</td>\n",
       "      <td>2064</td>\n",
       "      <td>...</td>\n",
       "      <td>2138</td>\n",
       "      <td>2144</td>\n",
       "      <td>2165</td>\n",
       "      <td>2196</td>\n",
       "      <td>2190</td>\n",
       "      <td>2111</td>\n",
       "      <td>2146</td>\n",
       "      <td>2115</td>\n",
       "      <td>2021</td>\n",
       "      <td>2184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>5486</td>\n",
       "      <td>5129</td>\n",
       "      <td>5255</td>\n",
       "      <td>5159</td>\n",
       "      <td>5152</td>\n",
       "      <td>5072</td>\n",
       "      <td>5046</td>\n",
       "      <td>4791</td>\n",
       "      <td>4996</td>\n",
       "      <td>5040</td>\n",
       "      <td>...</td>\n",
       "      <td>5079</td>\n",
       "      <td>5116</td>\n",
       "      <td>5207</td>\n",
       "      <td>5185</td>\n",
       "      <td>5213</td>\n",
       "      <td>5176</td>\n",
       "      <td>5168</td>\n",
       "      <td>5030</td>\n",
       "      <td>4946</td>\n",
       "      <td>5183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>8670</td>\n",
       "      <td>7952</td>\n",
       "      <td>7961</td>\n",
       "      <td>7678</td>\n",
       "      <td>7610</td>\n",
       "      <td>7577</td>\n",
       "      <td>7879</td>\n",
       "      <td>7578</td>\n",
       "      <td>7823</td>\n",
       "      <td>7699</td>\n",
       "      <td>...</td>\n",
       "      <td>7453</td>\n",
       "      <td>7510</td>\n",
       "      <td>7540</td>\n",
       "      <td>7338</td>\n",
       "      <td>7479</td>\n",
       "      <td>7308</td>\n",
       "      <td>7525</td>\n",
       "      <td>7393</td>\n",
       "      <td>7221</td>\n",
       "      <td>7586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1578</td>\n",
       "      <td>1502</td>\n",
       "      <td>1516</td>\n",
       "      <td>1528</td>\n",
       "      <td>1538</td>\n",
       "      <td>1515</td>\n",
       "      <td>1517</td>\n",
       "      <td>1507</td>\n",
       "      <td>1459</td>\n",
       "      <td>1492</td>\n",
       "      <td>...</td>\n",
       "      <td>1488</td>\n",
       "      <td>1474</td>\n",
       "      <td>1511</td>\n",
       "      <td>1493</td>\n",
       "      <td>1489</td>\n",
       "      <td>1452</td>\n",
       "      <td>1482</td>\n",
       "      <td>1443</td>\n",
       "      <td>1399</td>\n",
       "      <td>1516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1453</td>\n",
       "      <td>1312</td>\n",
       "      <td>1314</td>\n",
       "      <td>1268</td>\n",
       "      <td>1309</td>\n",
       "      <td>1175</td>\n",
       "      <td>1378</td>\n",
       "      <td>1268</td>\n",
       "      <td>1310</td>\n",
       "      <td>1354</td>\n",
       "      <td>...</td>\n",
       "      <td>1361</td>\n",
       "      <td>1394</td>\n",
       "      <td>1392</td>\n",
       "      <td>1395</td>\n",
       "      <td>1418</td>\n",
       "      <td>1400</td>\n",
       "      <td>1399</td>\n",
       "      <td>1356</td>\n",
       "      <td>1293</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>823</td>\n",
       "      <td>716</td>\n",
       "      <td>734</td>\n",
       "      <td>674</td>\n",
       "      <td>697</td>\n",
       "      <td>717</td>\n",
       "      <td>751</td>\n",
       "      <td>727</td>\n",
       "      <td>725</td>\n",
       "      <td>748</td>\n",
       "      <td>...</td>\n",
       "      <td>666</td>\n",
       "      <td>756</td>\n",
       "      <td>799</td>\n",
       "      <td>739</td>\n",
       "      <td>738</td>\n",
       "      <td>703</td>\n",
       "      <td>707</td>\n",
       "      <td>691</td>\n",
       "      <td>645</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>4852</td>\n",
       "      <td>4660</td>\n",
       "      <td>4589</td>\n",
       "      <td>3810</td>\n",
       "      <td>3809</td>\n",
       "      <td>4070</td>\n",
       "      <td>4371</td>\n",
       "      <td>4236</td>\n",
       "      <td>4421</td>\n",
       "      <td>4605</td>\n",
       "      <td>...</td>\n",
       "      <td>3696</td>\n",
       "      <td>3991</td>\n",
       "      <td>4277</td>\n",
       "      <td>4302</td>\n",
       "      <td>4056</td>\n",
       "      <td>4177</td>\n",
       "      <td>4130</td>\n",
       "      <td>4026</td>\n",
       "      <td>3940</td>\n",
       "      <td>4037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1     2     3     4     5     6     7      8     9     10  ...    22   \n",
       "14   1087  1009   990   978  1015   945  1000    919  1004  1037  ...  1073  \\\n",
       "16   1700  1513  1512  1465  1507  1482  1503   1439  1557  1523  ...  1569   \n",
       "20   8904  7745  7377  6629  6912  7144  7498   6476  7112  7002  ...  7514   \n",
       "21   7306  5503  5721  5026  4958  4987  5178   4621  4955  5210  ...  5484   \n",
       "29   9901  7143  7918  8045  8323  8304  8721  10442  8081  7618  ...  7822   \n",
       "30   5247  4739  4695  4638  4560  4515  4433   4254  4422  4561  ...  4509   \n",
       "32   2178  2080  2071  2074  2071  2014  2101   2034  2106  2064  ...  2138   \n",
       "38   5486  5129  5255  5159  5152  5072  5046   4791  4996  5040  ...  5079   \n",
       "39   8670  7952  7961  7678  7610  7577  7879   7578  7823  7699  ...  7453   \n",
       "41   1578  1502  1516  1528  1538  1515  1517   1507  1459  1492  ...  1488   \n",
       "49   1453  1312  1314  1268  1309  1175  1378   1268  1310  1354  ...  1361   \n",
       "138   823   716   734   674   697   717   751    727   725   748  ...   666   \n",
       "148  4852  4660  4589  3810  3809  4070  4371   4236  4421  4605  ...  3696   \n",
       "\n",
       "       23    24    25    26    27    28    29    30    31  \n",
       "14   1058  1085  1087  1089  1098  1109  1061  1018  1058  \n",
       "16   1639  1619  1625  1662  1629  1613  1545  1477  1567  \n",
       "20   7601  7548  7587  7850  7400  7779  7489  6833  7455  \n",
       "21   5434  5586  5702  5688  5455  5506  4974  4664  5398  \n",
       "29   6848  7355  6425  5287  6752  6591  6465  6424  7131  \n",
       "30   4530  4470  4635  4491  4470  4465  4339  4110  4331  \n",
       "32   2144  2165  2196  2190  2111  2146  2115  2021  2184  \n",
       "38   5116  5207  5185  5213  5176  5168  5030  4946  5183  \n",
       "39   7510  7540  7338  7479  7308  7525  7393  7221  7586  \n",
       "41   1474  1511  1493  1489  1452  1482  1443  1399  1516  \n",
       "49   1394  1392  1395  1418  1400  1399  1356  1293  1400  \n",
       "138   756   799   739   738   703   707   691   645   497  \n",
       "148  3991  4277  4302  4056  4177  4130  4026  3940  4037  \n",
       "\n",
       "[13 rows x 31 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total_Qty = np.zeros((I, T), dtype = int)\n",
    "Total_Recommended_Free_Qty = 0\n",
    "\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Total_Qty[i][t] = P[i][t].varValue + F[i][t].varValue\n",
    "        Total_Recommended_Free_Qty += F[i][t].varValue\n",
    "\n",
    "df_Total_Qty = pd.DataFrame(Total_Qty,\n",
    "                 index = products,\n",
    "                 columns = range(1,T+1))\n",
    "\n",
    "df_Total_Qty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "51649cb4",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>198.348940</td>\n",
       "      <td>120.186977</td>\n",
       "      <td>101.540478</td>\n",
       "      <td>89.306218</td>\n",
       "      <td>126.436547</td>\n",
       "      <td>56.439115</td>\n",
       "      <td>111.584946</td>\n",
       "      <td>30.850803</td>\n",
       "      <td>115.066337</td>\n",
       "      <td>148.622169</td>\n",
       "      <td>...</td>\n",
       "      <td>184.710199</td>\n",
       "      <td>169.157908</td>\n",
       "      <td>196.265902</td>\n",
       "      <td>198.319443</td>\n",
       "      <td>200.982718</td>\n",
       "      <td>209.281050</td>\n",
       "      <td>220.867247</td>\n",
       "      <td>172.212998</td>\n",
       "      <td>129.144989</td>\n",
       "      <td>169.700109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>404.133660</td>\n",
       "      <td>217.494959</td>\n",
       "      <td>216.171420</td>\n",
       "      <td>169.020521</td>\n",
       "      <td>211.744759</td>\n",
       "      <td>186.190959</td>\n",
       "      <td>207.232691</td>\n",
       "      <td>143.082545</td>\n",
       "      <td>261.419079</td>\n",
       "      <td>227.425668</td>\n",
       "      <td>...</td>\n",
       "      <td>273.816960</td>\n",
       "      <td>343.254973</td>\n",
       "      <td>323.846449</td>\n",
       "      <td>329.397052</td>\n",
       "      <td>366.655386</td>\n",
       "      <td>333.900904</td>\n",
       "      <td>317.606689</td>\n",
       "      <td>249.895919</td>\n",
       "      <td>181.767069</td>\n",
       "      <td>271.431344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3447.322602</td>\n",
       "      <td>2289.072155</td>\n",
       "      <td>1920.469237</td>\n",
       "      <td>1172.306428</td>\n",
       "      <td>1455.485577</td>\n",
       "      <td>1687.539385</td>\n",
       "      <td>2041.376519</td>\n",
       "      <td>1019.430103</td>\n",
       "      <td>1655.601969</td>\n",
       "      <td>1545.527079</td>\n",
       "      <td>...</td>\n",
       "      <td>2058.168174</td>\n",
       "      <td>2144.651863</td>\n",
       "      <td>2092.173916</td>\n",
       "      <td>2131.160002</td>\n",
       "      <td>2393.255906</td>\n",
       "      <td>1944.034906</td>\n",
       "      <td>2322.668198</td>\n",
       "      <td>2032.541616</td>\n",
       "      <td>1376.537642</td>\n",
       "      <td>1998.976613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4050.868206</td>\n",
       "      <td>2248.264829</td>\n",
       "      <td>2466.199975</td>\n",
       "      <td>1771.214468</td>\n",
       "      <td>1703.540644</td>\n",
       "      <td>1732.017047</td>\n",
       "      <td>1922.858264</td>\n",
       "      <td>1366.366125</td>\n",
       "      <td>1700.507763</td>\n",
       "      <td>1955.662886</td>\n",
       "      <td>...</td>\n",
       "      <td>2229.463526</td>\n",
       "      <td>2178.966926</td>\n",
       "      <td>2331.428541</td>\n",
       "      <td>2447.465076</td>\n",
       "      <td>2433.294016</td>\n",
       "      <td>2199.986409</td>\n",
       "      <td>2251.005738</td>\n",
       "      <td>1718.899732</td>\n",
       "      <td>1409.323640</td>\n",
       "      <td>2143.763764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8788.099250</td>\n",
       "      <td>6030.732239</td>\n",
       "      <td>6805.566580</td>\n",
       "      <td>6932.680073</td>\n",
       "      <td>7210.753815</td>\n",
       "      <td>7191.129627</td>\n",
       "      <td>7608.710342</td>\n",
       "      <td>9329.355148</td>\n",
       "      <td>6968.299432</td>\n",
       "      <td>6505.049805</td>\n",
       "      <td>...</td>\n",
       "      <td>6709.207046</td>\n",
       "      <td>5735.044549</td>\n",
       "      <td>6242.796083</td>\n",
       "      <td>5312.743166</td>\n",
       "      <td>4174.688955</td>\n",
       "      <td>5639.051983</td>\n",
       "      <td>5478.408874</td>\n",
       "      <td>5352.517411</td>\n",
       "      <td>5311.395713</td>\n",
       "      <td>6018.654758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1544.728142</td>\n",
       "      <td>1036.656925</td>\n",
       "      <td>993.610126</td>\n",
       "      <td>936.457994</td>\n",
       "      <td>857.839912</td>\n",
       "      <td>813.179080</td>\n",
       "      <td>730.797762</td>\n",
       "      <td>552.611209</td>\n",
       "      <td>719.759088</td>\n",
       "      <td>859.190665</td>\n",
       "      <td>...</td>\n",
       "      <td>807.341556</td>\n",
       "      <td>828.624398</td>\n",
       "      <td>768.046573</td>\n",
       "      <td>932.745301</td>\n",
       "      <td>789.469014</td>\n",
       "      <td>767.908989</td>\n",
       "      <td>762.668709</td>\n",
       "      <td>637.077003</td>\n",
       "      <td>408.447574</td>\n",
       "      <td>629.359449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>229.980561</td>\n",
       "      <td>132.825374</td>\n",
       "      <td>123.236070</td>\n",
       "      <td>126.332103</td>\n",
       "      <td>123.352374</td>\n",
       "      <td>66.694942</td>\n",
       "      <td>153.685953</td>\n",
       "      <td>86.155734</td>\n",
       "      <td>158.617577</td>\n",
       "      <td>115.991200</td>\n",
       "      <td>...</td>\n",
       "      <td>190.218971</td>\n",
       "      <td>196.494281</td>\n",
       "      <td>216.943294</td>\n",
       "      <td>248.241728</td>\n",
       "      <td>242.190340</td>\n",
       "      <td>163.319156</td>\n",
       "      <td>197.883104</td>\n",
       "      <td>167.843792</td>\n",
       "      <td>73.445197</td>\n",
       "      <td>236.848232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>836.654478</td>\n",
       "      <td>479.300147</td>\n",
       "      <td>605.828624</td>\n",
       "      <td>509.668223</td>\n",
       "      <td>502.569940</td>\n",
       "      <td>422.709858</td>\n",
       "      <td>396.055623</td>\n",
       "      <td>141.049069</td>\n",
       "      <td>346.209829</td>\n",
       "      <td>390.006076</td>\n",
       "      <td>...</td>\n",
       "      <td>428.943280</td>\n",
       "      <td>466.794486</td>\n",
       "      <td>557.595601</td>\n",
       "      <td>535.447026</td>\n",
       "      <td>563.718780</td>\n",
       "      <td>526.587823</td>\n",
       "      <td>518.127627</td>\n",
       "      <td>380.504952</td>\n",
       "      <td>296.123714</td>\n",
       "      <td>533.590169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2206.600714</td>\n",
       "      <td>1488.310140</td>\n",
       "      <td>1497.795492</td>\n",
       "      <td>1214.002386</td>\n",
       "      <td>1146.519522</td>\n",
       "      <td>1113.320866</td>\n",
       "      <td>1415.796910</td>\n",
       "      <td>1114.725976</td>\n",
       "      <td>1359.061700</td>\n",
       "      <td>1235.642648</td>\n",
       "      <td>...</td>\n",
       "      <td>989.816282</td>\n",
       "      <td>1045.899104</td>\n",
       "      <td>1076.075851</td>\n",
       "      <td>873.923309</td>\n",
       "      <td>1015.280199</td>\n",
       "      <td>844.807865</td>\n",
       "      <td>1060.909188</td>\n",
       "      <td>929.618361</td>\n",
       "      <td>757.073513</td>\n",
       "      <td>1122.264996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>298.212952</td>\n",
       "      <td>222.414709</td>\n",
       "      <td>236.343736</td>\n",
       "      <td>248.417351</td>\n",
       "      <td>257.988277</td>\n",
       "      <td>235.483574</td>\n",
       "      <td>237.444020</td>\n",
       "      <td>227.553484</td>\n",
       "      <td>179.467266</td>\n",
       "      <td>212.599559</td>\n",
       "      <td>...</td>\n",
       "      <td>207.972273</td>\n",
       "      <td>193.928631</td>\n",
       "      <td>231.548825</td>\n",
       "      <td>213.590279</td>\n",
       "      <td>209.266373</td>\n",
       "      <td>172.217258</td>\n",
       "      <td>201.814966</td>\n",
       "      <td>163.105946</td>\n",
       "      <td>118.682459</td>\n",
       "      <td>235.831745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>308.704651</td>\n",
       "      <td>168.037447</td>\n",
       "      <td>169.525175</td>\n",
       "      <td>124.163564</td>\n",
       "      <td>165.329740</td>\n",
       "      <td>31.457952</td>\n",
       "      <td>233.572468</td>\n",
       "      <td>123.779602</td>\n",
       "      <td>166.431735</td>\n",
       "      <td>210.119737</td>\n",
       "      <td>...</td>\n",
       "      <td>216.864660</td>\n",
       "      <td>249.816693</td>\n",
       "      <td>247.986161</td>\n",
       "      <td>250.896862</td>\n",
       "      <td>274.319959</td>\n",
       "      <td>255.842235</td>\n",
       "      <td>255.326570</td>\n",
       "      <td>211.626322</td>\n",
       "      <td>149.364564</td>\n",
       "      <td>256.449294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>303.209715</td>\n",
       "      <td>196.987853</td>\n",
       "      <td>214.480330</td>\n",
       "      <td>155.055573</td>\n",
       "      <td>177.412678</td>\n",
       "      <td>197.548117</td>\n",
       "      <td>231.277320</td>\n",
       "      <td>207.243494</td>\n",
       "      <td>205.739770</td>\n",
       "      <td>228.134554</td>\n",
       "      <td>...</td>\n",
       "      <td>146.160741</td>\n",
       "      <td>236.127078</td>\n",
       "      <td>280.059440</td>\n",
       "      <td>219.300116</td>\n",
       "      <td>218.582039</td>\n",
       "      <td>184.025560</td>\n",
       "      <td>187.472949</td>\n",
       "      <td>171.516156</td>\n",
       "      <td>125.947822</td>\n",
       "      <td>-22.845208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1720.222524</td>\n",
       "      <td>1528.490919</td>\n",
       "      <td>1458.047419</td>\n",
       "      <td>678.537396</td>\n",
       "      <td>677.640418</td>\n",
       "      <td>938.214065</td>\n",
       "      <td>1239.508879</td>\n",
       "      <td>1104.465701</td>\n",
       "      <td>1289.703162</td>\n",
       "      <td>1473.626820</td>\n",
       "      <td>...</td>\n",
       "      <td>564.990153</td>\n",
       "      <td>859.223870</td>\n",
       "      <td>1145.343794</td>\n",
       "      <td>1170.863049</td>\n",
       "      <td>924.110612</td>\n",
       "      <td>1045.280878</td>\n",
       "      <td>998.521299</td>\n",
       "      <td>894.158027</td>\n",
       "      <td>808.243231</td>\n",
       "      <td>905.444038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0            1            2            3            4    \n",
       "14    198.348940   120.186977   101.540478    89.306218   126.436547  \\\n",
       "16    404.133660   217.494959   216.171420   169.020521   211.744759   \n",
       "20   3447.322602  2289.072155  1920.469237  1172.306428  1455.485577   \n",
       "21   4050.868206  2248.264829  2466.199975  1771.214468  1703.540644   \n",
       "29   8788.099250  6030.732239  6805.566580  6932.680073  7210.753815   \n",
       "30   1544.728142  1036.656925   993.610126   936.457994   857.839912   \n",
       "32    229.980561   132.825374   123.236070   126.332103   123.352374   \n",
       "38    836.654478   479.300147   605.828624   509.668223   502.569940   \n",
       "39   2206.600714  1488.310140  1497.795492  1214.002386  1146.519522   \n",
       "41    298.212952   222.414709   236.343736   248.417351   257.988277   \n",
       "49    308.704651   168.037447   169.525175   124.163564   165.329740   \n",
       "138   303.209715   196.987853   214.480330   155.055573   177.412678   \n",
       "148  1720.222524  1528.490919  1458.047419   678.537396   677.640418   \n",
       "\n",
       "              5            6            7            8            9   ...   \n",
       "14     56.439115   111.584946    30.850803   115.066337   148.622169  ...  \\\n",
       "16    186.190959   207.232691   143.082545   261.419079   227.425668  ...   \n",
       "20   1687.539385  2041.376519  1019.430103  1655.601969  1545.527079  ...   \n",
       "21   1732.017047  1922.858264  1366.366125  1700.507763  1955.662886  ...   \n",
       "29   7191.129627  7608.710342  9329.355148  6968.299432  6505.049805  ...   \n",
       "30    813.179080   730.797762   552.611209   719.759088   859.190665  ...   \n",
       "32     66.694942   153.685953    86.155734   158.617577   115.991200  ...   \n",
       "38    422.709858   396.055623   141.049069   346.209829   390.006076  ...   \n",
       "39   1113.320866  1415.796910  1114.725976  1359.061700  1235.642648  ...   \n",
       "41    235.483574   237.444020   227.553484   179.467266   212.599559  ...   \n",
       "49     31.457952   233.572468   123.779602   166.431735   210.119737  ...   \n",
       "138   197.548117   231.277320   207.243494   205.739770   228.134554  ...   \n",
       "148   938.214065  1239.508879  1104.465701  1289.703162  1473.626820  ...   \n",
       "\n",
       "              21           22           23           24           25   \n",
       "14    184.710199   169.157908   196.265902   198.319443   200.982718  \\\n",
       "16    273.816960   343.254973   323.846449   329.397052   366.655386   \n",
       "20   2058.168174  2144.651863  2092.173916  2131.160002  2393.255906   \n",
       "21   2229.463526  2178.966926  2331.428541  2447.465076  2433.294016   \n",
       "29   6709.207046  5735.044549  6242.796083  5312.743166  4174.688955   \n",
       "30    807.341556   828.624398   768.046573   932.745301   789.469014   \n",
       "32    190.218971   196.494281   216.943294   248.241728   242.190340   \n",
       "38    428.943280   466.794486   557.595601   535.447026   563.718780   \n",
       "39    989.816282  1045.899104  1076.075851   873.923309  1015.280199   \n",
       "41    207.972273   193.928631   231.548825   213.590279   209.266373   \n",
       "49    216.864660   249.816693   247.986161   250.896862   274.319959   \n",
       "138   146.160741   236.127078   280.059440   219.300116   218.582039   \n",
       "148   564.990153   859.223870  1145.343794  1170.863049   924.110612   \n",
       "\n",
       "              26           27           28           29           30  \n",
       "14    209.281050   220.867247   172.212998   129.144989   169.700109  \n",
       "16    333.900904   317.606689   249.895919   181.767069   271.431344  \n",
       "20   1944.034906  2322.668198  2032.541616  1376.537642  1998.976613  \n",
       "21   2199.986409  2251.005738  1718.899732  1409.323640  2143.763764  \n",
       "29   5639.051983  5478.408874  5352.517411  5311.395713  6018.654758  \n",
       "30    767.908989   762.668709   637.077003   408.447574   629.359449  \n",
       "32    163.319156   197.883104   167.843792    73.445197   236.848232  \n",
       "38    526.587823   518.127627   380.504952   296.123714   533.590169  \n",
       "39    844.807865  1060.909188   929.618361   757.073513  1122.264996  \n",
       "41    172.217258   201.814966   163.105946   118.682459   235.831745  \n",
       "49    255.842235   255.326570   211.626322   149.364564   256.449294  \n",
       "138   184.025560   187.472949   171.516156   125.947822   -22.845208  \n",
       "148  1045.280878   998.521299   894.158027   808.243231   905.444038  \n",
       "\n",
       "[13 rows x 31 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(Base_Demand, index=products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28c4659f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended Paid Quantity:  1357700.0\n",
      "Real Paid Quantity:  1177976\n",
      "Recommended Free Quantity:  193905.0\n",
      "Real Free Quantity:  194125\n"
     ]
    }
   ],
   "source": [
    "print(\"Recommended Paid Quantity: \", pulp.value(Promo_Model.objective))\n",
    "print(\"Real Paid Quantity: \", sum(paid_qty_sep_23))\n",
    "print(\"Recommended Free Quantity: \", round(Total_Recommended_Free_Qty, 0))\n",
    "print(\"Real Free Quantity: \", sum(free_qty_sep_23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2a5c8316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 25860,  39899, 218849, 156945, 226867,  97476,  57613, 106015,\n",
       "       221852,  32396,  34606,  21492, 117830], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Paid_Qty = np.zeros((I, T), dtype = int)\n",
    "\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Paid_Qty[i][t] = P[i][t].varValue\n",
    "\n",
    "df_Paid_Qty = pd.DataFrame(Paid_Qty,\n",
    "                 index = products,\n",
    "                 columns = range(1,T+1))\n",
    "\n",
    "np.array(df_Paid_Qty.sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9ad61529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.1286662 , 1.06542231, 1.27685432, 1.16351222, 1.42989411,\n",
       "       1.03491989, 1.02423111, 1.05162135, 1.06940849, 0.83137013,\n",
       "       1.04717524, 1.04102688, 1.16307534])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df_Paid_Qty.sum(axis=1)) / np.array(paid_qty_sep_23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce58a172",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.26</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1     2     3     4     5     6     7     8     9     10  ...    22   \n",
       "14   0.18  0.20  0.20  0.20  0.20  0.21  0.20  0.22  0.20  0.19  ...  0.19  \\\n",
       "16   0.16  0.18  0.18  0.19  0.18  0.18  0.18  0.19  0.18  0.18  ...  0.17   \n",
       "20   0.04  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  0.05  ...  0.05   \n",
       "21   0.04  0.06  0.06  0.06  0.06  0.06  0.06  0.07  0.06  0.06  ...  0.06   \n",
       "29   0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  ...  0.01   \n",
       "30   0.26  0.29  0.30  0.30  0.30  0.31  0.31  0.33  0.31  0.30  ...  0.31   \n",
       "32   0.12  0.12  0.13  0.12  0.13  0.13  0.12  0.13  0.12  0.13  ...  0.12   \n",
       "38   0.31  0.33  0.32  0.33  0.33  0.33  0.34  0.35  0.34  0.34  ...  0.33   \n",
       "39   0.06  0.06  0.06  0.06  0.06  0.06  0.06  0.06  0.06  0.06  ...  0.06   \n",
       "41   0.28  0.30  0.29  0.29  0.29  0.29  0.29  0.30  0.31  0.30  ...  0.30   \n",
       "49   0.16  0.18  0.18  0.18  0.18  0.20  0.17  0.18  0.18  0.17  ...  0.17   \n",
       "138  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  ...  0.04   \n",
       "148  0.10  0.10  0.10  0.13  0.13  0.12  0.11  0.11  0.11  0.10  ...  0.13   \n",
       "\n",
       "       23    24    25    26    27    28    29    30    31  \n",
       "14   0.19  0.18  0.18  0.18  0.18  0.18  0.19  0.20  0.19  \n",
       "16   0.17  0.17  0.17  0.16  0.17  0.17  0.18  0.19  0.17  \n",
       "20   0.05  0.05  0.05  0.04  0.05  0.04  0.05  0.05  0.05  \n",
       "21   0.06  0.06  0.06  0.06  0.06  0.06  0.06  0.07  0.06  \n",
       "29   0.02  0.01  0.02  0.02  0.02  0.02  0.02  0.02  0.01  \n",
       "30   0.31  0.31  0.30  0.31  0.31  0.31  0.32  0.34  0.32  \n",
       "32   0.12  0.12  0.12  0.12  0.12  0.12  0.12  0.13  0.12  \n",
       "38   0.33  0.32  0.33  0.32  0.33  0.33  0.34  0.34  0.33  \n",
       "39   0.06  0.06  0.07  0.06  0.07  0.06  0.06  0.07  0.06  \n",
       "41   0.30  0.30  0.30  0.30  0.31  0.30  0.31  0.32  0.29  \n",
       "49   0.17  0.17  0.17  0.16  0.17  0.17  0.17  0.18  0.17  \n",
       "138  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.04  0.06  \n",
       "148  0.12  0.11  0.11  0.12  0.12  0.12  0.12  0.12  0.12  \n",
       "\n",
       "[13 rows x 31 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Promo_Ratio = np.zeros((I, T), dtype = float)\n",
    "for i in Products:\n",
    "    for t in PHorizon:\n",
    "        Promo_Ratio[i][t] = F[i][t].varValue/(P[i][t].varValue + F[i][t].varValue)\n",
    "\n",
    "df_Promo_Ratio = pd.DataFrame(Promo_Ratio,\n",
    "                 index = products,\n",
    "                 columns = range(1,T+1))\n",
    "\n",
    "# with pd.option_context('display.max_rows', None,\n",
    "#                        'display.max_columns', None,\n",
    "#                        'display.precision', 2):\n",
    "#     print(df_Promo_Ratio)\n",
    "\n",
    "round(df_Promo_Ratio, 2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}