recommendation_engine_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori, association_rules\n",
    "from pprint import pprint\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>transaction_time</th>\n",
       "      <th>sales_outlet_id</th>\n",
       "      <th>staff_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>instore_yn</th>\n",
       "      <th>order</th>\n",
       "      <th>line_item_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>line_item_amount</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>promo_item_yn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>12:04:43</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>558</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>15:54:39</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>781</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>14:34:59</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>788</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>16:06:04</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>683</td>\n",
       "      <td>N</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>19:18:37</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>99</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.45</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id transaction_date transaction_time  sales_outlet_id  \\\n",
       "0               7       2019-04-01         12:04:43                3   \n",
       "1              11       2019-04-01         15:54:39                3   \n",
       "2              19       2019-04-01         14:34:59                3   \n",
       "3              32       2019-04-01         16:06:04                3   \n",
       "4              33       2019-04-01         19:18:37                3   \n",
       "\n",
       "   staff_id  customer_id instore_yn  order  line_item_id  product_id  \\\n",
       "0        12          558          N      1             1          52   \n",
       "1        17          781          N      1             1          27   \n",
       "2        17          788          Y      1             1          46   \n",
       "3        12          683          N      1             1          23   \n",
       "4        17           99          Y      1             1          34   \n",
       "\n",
       "   quantity  line_item_amount  unit_price promo_item_yn  \n",
       "0         1              2.50        2.50             N  \n",
       "1         2              7.00        3.50             N  \n",
       "2         2              5.00        2.50             N  \n",
       "3         2              5.00        2.50             N  \n",
       "4         1              2.45        2.45             N  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_reciepts = pd.read_csv(\"datasets/201904 sales reciepts.csv\")\n",
    "sales_reciepts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49894 entries, 0 to 49893\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   transaction_id    49894 non-null  int64  \n",
      " 1   transaction_date  49894 non-null  object \n",
      " 2   transaction_time  49894 non-null  object \n",
      " 3   sales_outlet_id   49894 non-null  int64  \n",
      " 4   staff_id          49894 non-null  int64  \n",
      " 5   customer_id       49894 non-null  int64  \n",
      " 6   instore_yn        49894 non-null  object \n",
      " 7   order             49894 non-null  int64  \n",
      " 8   line_item_id      49894 non-null  int64  \n",
      " 9   product_id        49894 non-null  int64  \n",
      " 10  quantity          49894 non-null  int64  \n",
      " 11  line_item_amount  49894 non-null  float64\n",
      " 12  unit_price        49894 non-null  float64\n",
      " 13  promo_item_yn     49894 non-null  object \n",
      "dtypes: float64(2), int64(8), object(4)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "sales_reciepts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_group</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_type</th>\n",
       "      <th>product</th>\n",
       "      <th>product_description</th>\n",
       "      <th>unit_of_measure</th>\n",
       "      <th>current_wholesale_price</th>\n",
       "      <th>current_retail_price</th>\n",
       "      <th>tax_exempt_yn</th>\n",
       "      <th>promo_yn</th>\n",
       "      <th>new_product_yn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Whole Bean/Teas</td>\n",
       "      <td>Coffee beans</td>\n",
       "      <td>Organic Beans</td>\n",
       "      <td>Brazilian - Organic</td>\n",
       "      <td>It's like Carnival in a cup. Clean and smooth.</td>\n",
       "      <td>12 oz</td>\n",
       "      <td>14.40</td>\n",
       "      <td>$18.00</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Whole Bean/Teas</td>\n",
       "      <td>Coffee beans</td>\n",
       "      <td>House blend Beans</td>\n",
       "      <td>Our Old Time Diner Blend</td>\n",
       "      <td>Out packed blend of beans that is reminiscent ...</td>\n",
       "      <td>12 oz</td>\n",
       "      <td>14.40</td>\n",
       "      <td>$18.00</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Whole Bean/Teas</td>\n",
       "      <td>Coffee beans</td>\n",
       "      <td>Espresso Beans</td>\n",
       "      <td>Espresso Roast</td>\n",
       "      <td>Our house blend for a good espresso shot.</td>\n",
       "      <td>1 lb</td>\n",
       "      <td>11.80</td>\n",
       "      <td>$14.75</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Whole Bean/Teas</td>\n",
       "      <td>Coffee beans</td>\n",
       "      <td>Espresso Beans</td>\n",
       "      <td>Primo Espresso Roast</td>\n",
       "      <td>Our primium single source of hand roasted beans.</td>\n",
       "      <td>1 lb</td>\n",
       "      <td>16.36</td>\n",
       "      <td>$20.45</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Whole Bean/Teas</td>\n",
       "      <td>Coffee beans</td>\n",
       "      <td>Gourmet Beans</td>\n",
       "      <td>Columbian Medium Roast</td>\n",
       "      <td>A smooth cup of coffee any time of day.</td>\n",
       "      <td>1 lb</td>\n",
       "      <td>12.00</td>\n",
       "      <td>$15.00</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id    product_group product_category       product_type  \\\n",
       "0           1  Whole Bean/Teas     Coffee beans      Organic Beans   \n",
       "1           2  Whole Bean/Teas     Coffee beans  House blend Beans   \n",
       "2           3  Whole Bean/Teas     Coffee beans     Espresso Beans   \n",
       "3           4  Whole Bean/Teas     Coffee beans     Espresso Beans   \n",
       "4           5  Whole Bean/Teas     Coffee beans      Gourmet Beans   \n",
       "\n",
       "                    product  \\\n",
       "0       Brazilian - Organic   \n",
       "1  Our Old Time Diner Blend   \n",
       "2            Espresso Roast   \n",
       "3      Primo Espresso Roast   \n",
       "4    Columbian Medium Roast   \n",
       "\n",
       "                                 product_description unit_of_measure  \\\n",
       "0     It's like Carnival in a cup. Clean and smooth.           12 oz   \n",
       "1  Out packed blend of beans that is reminiscent ...           12 oz   \n",
       "2          Our house blend for a good espresso shot.            1 lb   \n",
       "3   Our primium single source of hand roasted beans.            1 lb   \n",
       "4           A smooth cup of coffee any time of day.             1 lb   \n",
       "\n",
       "   current_wholesale_price current_retail_price tax_exempt_yn promo_yn  \\\n",
       "0                    14.40              $18.00              Y        N   \n",
       "1                    14.40              $18.00              Y        N   \n",
       "2                    11.80              $14.75              Y        N   \n",
       "3                    16.36              $20.45              Y        N   \n",
       "4                    12.00              $15.00              Y        N   \n",
       "\n",
       "  new_product_yn  \n",
       "0              N  \n",
       "1              N  \n",
       "2              N  \n",
       "3              N  \n",
       "4              N  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = pd.read_csv(\"datasets/product.csv\")\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 88 entries, 0 to 87\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   product_id               88 non-null     int64  \n",
      " 1   product_group            88 non-null     object \n",
      " 2   product_category         88 non-null     object \n",
      " 3   product_type             88 non-null     object \n",
      " 4   product                  88 non-null     object \n",
      " 5   product_description      88 non-null     object \n",
      " 6   unit_of_measure          88 non-null     object \n",
      " 7   current_wholesale_price  88 non-null     float64\n",
      " 8   current_retail_price     88 non-null     object \n",
      " 9   tax_exempt_yn            88 non-null     object \n",
      " 10  promo_yn                 88 non-null     object \n",
      " 11  new_product_yn           88 non-null     object \n",
      "dtypes: float64(1), int64(1), object(10)\n",
      "memory usage: 8.4+ KB\n"
     ]
    }
   ],
   "source": [
    "products.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging both datasets for further preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>sales_outlet_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>558</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>Tea</td>\n",
       "      <td>Traditional Blend Chai Rg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>781</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Brazilian Lg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>788</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>Tea</td>\n",
       "      <td>Serenity Green Tea Rg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>683</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Our Old Time Diner Blend Rg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Jamaican Coffee River Sm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id transaction_date  sales_outlet_id  customer_id  product_id  \\\n",
       "0               7       2019-04-01                3          558          52   \n",
       "1              11       2019-04-01                3          781          27   \n",
       "2              19       2019-04-01                3          788          46   \n",
       "3              32       2019-04-01                3          683          23   \n",
       "4              33       2019-04-01                3           99          34   \n",
       "\n",
       "   quantity product_category                      product  \n",
       "0         1              Tea    Traditional Blend Chai Rg  \n",
       "1         2           Coffee                 Brazilian Lg  \n",
       "2         2              Tea        Serenity Green Tea Rg  \n",
       "3         2           Coffee  Our Old Time Diner Blend Rg  \n",
       "4         1           Coffee     Jamaican Coffee River Sm  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_sales_reciepts_columns = [\"transaction_id\", \"transaction_date\", \"sales_outlet_id\", \"customer_id\", \"product_id\", \"quantity\"]\n",
    "selected_products_columns = [\"product_id\", \"product_category\", \"product\"]\n",
    "dataset = pd.merge(sales_reciepts[selected_sales_reciepts_columns], products[selected_products_columns], \"left\", \"product_id\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing the merged dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"product\"] = dataset[\"product\"].str.replace(\" Lg\", \"\")\n",
    "dataset[\"product\"] = dataset[\"product\"].str.replace(\" Rg\", \"\")\n",
    "dataset[\"product\"] = dataset[\"product\"].str.replace(\" Sm\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>sales_outlet_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>558</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>Tea</td>\n",
       "      <td>Traditional Blend Chai</td>\n",
       "      <td>7_558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>781</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Brazilian</td>\n",
       "      <td>11_781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>788</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>Tea</td>\n",
       "      <td>Serenity Green Tea</td>\n",
       "      <td>19_788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>683</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Our Old Time Diner Blend</td>\n",
       "      <td>32_683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Jamaican Coffee River</td>\n",
       "      <td>33_99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id transaction_date  sales_outlet_id  customer_id  product_id  \\\n",
       "0               7       2019-04-01                3          558          52   \n",
       "1              11       2019-04-01                3          781          27   \n",
       "2              19       2019-04-01                3          788          46   \n",
       "3              32       2019-04-01                3          683          23   \n",
       "4              33       2019-04-01                3           99          34   \n",
       "\n",
       "   quantity product_category                   product transaction  \n",
       "0         1              Tea    Traditional Blend Chai       7_558  \n",
       "1         2           Coffee                 Brazilian      11_781  \n",
       "2         2              Tea        Serenity Green Tea      19_788  \n",
       "3         2           Coffee  Our Old Time Diner Blend      32_683  \n",
       "4         1           Coffee     Jamaican Coffee River       33_99  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"transaction\"] = dataset[\"transaction_id\"].astype(str) + \"_\" + dataset[\"customer_id\"].astype(str)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>transaction_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>sales_outlet_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product</th>\n",
       "      <th>transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>108</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>108_65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>112</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Espresso shot</td>\n",
       "      <td>112_90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>127</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>127_116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>134</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>189</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Latte</td>\n",
       "      <td>134_189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>135</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>3</td>\n",
       "      <td>131</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>135_131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    transaction_id transaction_date  sales_outlet_id  customer_id  product_id  \\\n",
       "16             108       2019-04-01                3           65          40   \n",
       "17             112       2019-04-01                3           90          37   \n",
       "20             127       2019-04-01                3          116          41   \n",
       "21             134       2019-04-01                3          189          38   \n",
       "22             135       2019-04-01                3          131          40   \n",
       "\n",
       "    quantity product_category        product transaction  \n",
       "16         1           Coffee     Cappuccino      108_65  \n",
       "17         2           Coffee  Espresso shot      112_90  \n",
       "20         2           Coffee     Cappuccino     127_116  \n",
       "21         2           Coffee          Latte     134_189  \n",
       "22         1           Coffee     Cappuccino     135_131  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choosing only a subset products\n",
    "selected_products = [\n",
    "                        'Cappuccino', 'Latte', 'Espresso shot',  \\\n",
    "                        'Dark chocolate','Sugar Free Vanilla syrup', 'Chocolate syrup',\\\n",
    "                        'Carmel syrup', 'Hazelnut syrup', 'Ginger Scone',  \\\n",
    "                        'Chocolate Croissant', 'Jumbo Savory Scone', 'Cranberry Scone', 'Hazelnut Biscotti',\\\n",
    "                        'Croissant', 'Almond Croissant', 'Oatmeal Scone', 'Chocolate Chip Biscotti',\\\n",
    "                        'Ginger Biscotti'\n",
    "                    ]\n",
    "\n",
    "dataset = dataset[dataset[\"product\"].isin(selected_products)]\n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing the transaction that belongs to not loyal clients\n",
    "transaction_counts = dataset[\"transaction\"].value_counts()\n",
    "loyal_clients = transaction_counts[transaction_counts>1].index\n",
    "dataset = dataset[dataset[\"transaction\"].isin(loyal_clients)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category\n",
       "Bakery                3800\n",
       "Coffee                3174\n",
       "Flavours              2246\n",
       "Drinking Chocolate     947\n",
       "Packaged Chocolate      22\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"product_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product\n",
       "Cappuccino                  1290\n",
       "Latte                       1256\n",
       "Dark chocolate               969\n",
       "Chocolate Croissant          636\n",
       "Espresso shot                628\n",
       "Sugar Free Vanilla syrup     605\n",
       "Chocolate syrup              568\n",
       "Carmel syrup                 561\n",
       "Hazelnut syrup               512\n",
       "Ginger Scone                 417\n",
       "Jumbo Savory Scone           357\n",
       "Croissant                    355\n",
       "Chocolate Chip Biscotti      352\n",
       "Cranberry Scone              350\n",
       "Almond Croissant             347\n",
       "Hazelnut Biscotti            338\n",
       "Oatmeal Scone                334\n",
       "Ginger Biscotti              314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"product\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popularity recommendation engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>product_category</th>\n",
       "      <th>number_of_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Almond Croissant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carmel syrup</td>\n",
       "      <td>Flavours</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chocolate Chip Biscotti</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chocolate Croissant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chocolate syrup</td>\n",
       "      <td>Flavours</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cranberry Scone</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Croissant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dark chocolate</td>\n",
       "      <td>Drinking Chocolate</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dark chocolate</td>\n",
       "      <td>Packaged Chocolate</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Espresso shot</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ginger Biscotti</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ginger Scone</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Hazelnut Biscotti</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hazelnut syrup</td>\n",
       "      <td>Flavours</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jumbo Savory Scone</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Latte</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Oatmeal Scone</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sugar Free Vanilla syrup</td>\n",
       "      <td>Flavours</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     product    product_category  number_of_transactions\n",
       "0           Almond Croissant              Bakery                     347\n",
       "1                 Cappuccino              Coffee                    1290\n",
       "2               Carmel syrup            Flavours                     561\n",
       "3    Chocolate Chip Biscotti              Bakery                     352\n",
       "4        Chocolate Croissant              Bakery                     636\n",
       "5            Chocolate syrup            Flavours                     568\n",
       "6            Cranberry Scone              Bakery                     350\n",
       "7                  Croissant              Bakery                     355\n",
       "8             Dark chocolate  Drinking Chocolate                     947\n",
       "9             Dark chocolate  Packaged Chocolate                      22\n",
       "10             Espresso shot              Coffee                     628\n",
       "11           Ginger Biscotti              Bakery                     314\n",
       "12              Ginger Scone              Bakery                     417\n",
       "13         Hazelnut Biscotti              Bakery                     338\n",
       "14            Hazelnut syrup            Flavours                     512\n",
       "15        Jumbo Savory Scone              Bakery                     357\n",
       "16                     Latte              Coffee                    1256\n",
       "17             Oatmeal Scone              Bakery                     334\n",
       "18  Sugar Free Vanilla syrup            Flavours                     605"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popularity_recommendation = dataset.groupby([\"product\", \"product_category\"]).count().reset_index()\n",
    "popularity_recommendation = popularity_recommendation[[\"product\", \"product_category\", \"transaction_id\"]]\n",
    "popularity_recommendation = popularity_recommendation.rename(columns={\"transaction_id\": \"number_of_transactions\"})\n",
    "popularity_recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "popularity_recommendation.to_csv(\"api/recommendation_objects/popularity_recommendation.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apriori recommnedation engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>transaction</th>\n",
       "      <th>product</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>Dark chocolate</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000_0</td>\n",
       "      <td>Oatmeal Scone</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001_8306</td>\n",
       "      <td>Cappuccino</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001_8306</td>\n",
       "      <td>Carmel syrup</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1002_0</td>\n",
       "      <td>Carmel syrup</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  transaction         product  count\n",
       "0      1000_0  Dark chocolate      1\n",
       "1      1000_0   Oatmeal Scone      1\n",
       "2   1001_8306      Cappuccino      1\n",
       "3   1001_8306    Carmel syrup      1\n",
       "4      1002_0    Carmel syrup      1"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_basket = dataset.groupby([\"transaction\",\"product\"])[\"product\"].count().reset_index(name=\"count\")\n",
    "train_basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>product</th>\n",
       "      <th>Almond Croissant</th>\n",
       "      <th>Cappuccino</th>\n",
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       "product      Almond Croissant  Cappuccino  Carmel syrup  \\\n",
       "transaction                                               \n",
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       "1001_8306                 0.0         1.0           1.0   \n",
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       "\n",
       "product      Chocolate Chip Biscotti  Chocolate Croissant  Chocolate syrup  \\\n",
       "transaction                                                                  \n",
       "1000_0                           0.0                  0.0              0.0   \n",
       "1001_8306                        0.0                  0.0              0.0   \n",
       "1002_0                           0.0                  0.0              0.0   \n",
       "1004_5383                        0.0                  0.0              1.0   \n",
       "1005_0                           0.0                  0.0              0.0   \n",
       "\n",
       "product      Cranberry Scone  Croissant  Dark chocolate  Espresso shot  \\\n",
       "transaction                                                              \n",
       "1000_0                   0.0        0.0             1.0            0.0   \n",
       "1001_8306                0.0        0.0             0.0            0.0   \n",
       "1002_0                   0.0        0.0             2.0            1.0   \n",
       "1004_5383                0.0        0.0             0.0            0.0   \n",
       "1005_0                   0.0        0.0             0.0            0.0   \n",
       "\n",
       "product      Ginger Biscotti  Ginger Scone  Hazelnut Biscotti  Hazelnut syrup  \\\n",
       "transaction                                                                     \n",
       "1000_0                   0.0           0.0                0.0             0.0   \n",
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       "1002_0                   0.0           1.0                0.0             0.0   \n",
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       "1005_0                   0.0           0.0                0.0             0.0   \n",
       "\n",
       "product      Jumbo Savory Scone  Latte  Oatmeal Scone  \\\n",
       "transaction                                             \n",
       "1000_0                      0.0    0.0            1.0   \n",
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       "1002_0                      0.0    0.0            0.0   \n",
       "1004_5383                   0.0    1.0            0.0   \n",
       "1005_0                      2.0    1.0            0.0   \n",
       "\n",
       "product      Sugar Free Vanilla syrup  \n",
       "transaction                            \n",
       "1000_0                            0.0  \n",
       "1001_8306                         0.0  \n",
       "1002_0                            0.0  \n",
       "1004_5383                         0.0  \n",
       "1005_0                            0.0  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_basket = train_basket.pivot_table(index=\"transaction\", columns=\"product\", values=\"count\", aggfunc=\"sum\").fillna(0)\n",
    "train_basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
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       "product      Almond Croissant  Cappuccino  Carmel syrup  \\\n",
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       "transaction                                                                  \n",
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       "product      Cranberry Scone  Croissant  Dark chocolate  Espresso shot  \\\n",
       "transaction                                                              \n",
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       "product      Ginger Biscotti  Ginger Scone  Hazelnut Biscotti  Hazelnut syrup  \\\n",
       "transaction                                                                     \n",
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       "transaction                                             \n",
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       "1001_8306                     0      0              0   \n",
       "1002_0                        0      0              0   \n",
       "1004_5383                     0      1              0   \n",
       "1005_0                        1      1              0   \n",
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       "transaction                            \n",
       "1000_0                              0  \n",
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       "1002_0                              0  \n",
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      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def encode_units(x):\n",
    "    if x >=1:\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "train_basket = train_basket.map(encode_units)\n",
    "train_basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Osamih\\Desktop\\AI Chatbot\\.venv\\lib\\site-packages\\mlxtend\\frequent_patterns\\fpcommon.py:109: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
      "  warnings.warn(\n"
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       "    support                   itemsets\n",
       "0  0.115646         (Almond Croissant)\n",
       "1  0.388889               (Cappuccino)\n",
       "2  0.191232             (Carmel syrup)\n",
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       "4  0.135676      (Chocolate Croissant)"
      ]
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   "source": [
    "frequent_items = apriori(train_basket, min_support=0.05, use_colnames=True)\n",
    "frequent_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
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       "      <td>(Cappuccino)</td>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.053288</td>\n",
       "      <td>0.137026</td>\n",
       "      <td>1.184874</td>\n",
       "      <td>0.008314</td>\n",
       "      <td>1.024775</td>\n",
       "      <td>0.255319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.053288</td>\n",
       "      <td>0.460784</td>\n",
       "      <td>1.184874</td>\n",
       "      <td>0.008314</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>0.176432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Dark chocolate)</td>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>0.277022</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.057445</td>\n",
       "      <td>0.207367</td>\n",
       "      <td>1.793115</td>\n",
       "      <td>0.025409</td>\n",
       "      <td>1.115717</td>\n",
       "      <td>0.611791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>(Dark chocolate)</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.277022</td>\n",
       "      <td>0.057445</td>\n",
       "      <td>0.496732</td>\n",
       "      <td>1.793115</td>\n",
       "      <td>0.025409</td>\n",
       "      <td>1.436567</td>\n",
       "      <td>0.500152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Latte)</td>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>0.382086</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.054422</td>\n",
       "      <td>0.142433</td>\n",
       "      <td>1.231629</td>\n",
       "      <td>0.010235</td>\n",
       "      <td>1.031236</td>\n",
       "      <td>0.304358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          antecedents         consequents  antecedent support  \\\n",
       "0        (Cappuccino)  (Almond Croissant)            0.388889   \n",
       "1  (Almond Croissant)        (Cappuccino)            0.115646   \n",
       "2    (Dark chocolate)  (Almond Croissant)            0.277022   \n",
       "3  (Almond Croissant)    (Dark chocolate)            0.115646   \n",
       "4             (Latte)  (Almond Croissant)            0.382086   \n",
       "\n",
       "   consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "0            0.115646  0.053288    0.137026  1.184874  0.008314    1.024775   \n",
       "1            0.388889  0.053288    0.460784  1.184874  0.008314    1.133333   \n",
       "2            0.115646  0.057445    0.207367  1.793115  0.025409    1.115717   \n",
       "3            0.277022  0.057445    0.496732  1.793115  0.025409    1.436567   \n",
       "4            0.115646  0.054422    0.142433  1.231629  0.010235    1.031236   \n",
       "\n",
       "   zhangs_metric  \n",
       "0       0.255319  \n",
       "1       0.176432  \n",
       "2       0.611791  \n",
       "3       0.500152  \n",
       "4       0.304358  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules_basket = association_rules(frequent_items, metric = \"lift\", min_threshold=1)\n",
    "rules_basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Sugar Free Vanilla syrup)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.200302</td>\n",
       "      <td>0.113001</td>\n",
       "      <td>0.290573</td>\n",
       "      <td>1.450674</td>\n",
       "      <td>0.035105</td>\n",
       "      <td>1.127245</td>\n",
       "      <td>0.508361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Chocolate syrup)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.188964</td>\n",
       "      <td>0.109599</td>\n",
       "      <td>0.281827</td>\n",
       "      <td>1.491429</td>\n",
       "      <td>0.036113</td>\n",
       "      <td>1.129304</td>\n",
       "      <td>0.539185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Carmel syrup)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.191232</td>\n",
       "      <td>0.102797</td>\n",
       "      <td>0.264334</td>\n",
       "      <td>1.382270</td>\n",
       "      <td>0.028429</td>\n",
       "      <td>1.099369</td>\n",
       "      <td>0.452540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Hazelnut syrup)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.171580</td>\n",
       "      <td>0.090325</td>\n",
       "      <td>0.232264</td>\n",
       "      <td>1.353682</td>\n",
       "      <td>0.023600</td>\n",
       "      <td>1.079044</td>\n",
       "      <td>0.427539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Ginger Scone)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.133409</td>\n",
       "      <td>0.059713</td>\n",
       "      <td>0.153547</td>\n",
       "      <td>1.150951</td>\n",
       "      <td>0.007832</td>\n",
       "      <td>1.023791</td>\n",
       "      <td>0.214614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Croissant)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.114135</td>\n",
       "      <td>0.056311</td>\n",
       "      <td>0.144801</td>\n",
       "      <td>1.268685</td>\n",
       "      <td>0.011926</td>\n",
       "      <td>1.035859</td>\n",
       "      <td>0.346553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Chocolate Chip Biscotti)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.112623</td>\n",
       "      <td>0.056311</td>\n",
       "      <td>0.144801</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>0.012514</td>\n",
       "      <td>1.037626</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Chocolate Croissant)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.135676</td>\n",
       "      <td>0.056311</td>\n",
       "      <td>0.144801</td>\n",
       "      <td>1.067250</td>\n",
       "      <td>0.003548</td>\n",
       "      <td>1.010669</td>\n",
       "      <td>0.103112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Jumbo Savory Scone)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.114890</td>\n",
       "      <td>0.055178</td>\n",
       "      <td>0.141885</td>\n",
       "      <td>1.234962</td>\n",
       "      <td>0.010498</td>\n",
       "      <td>1.031458</td>\n",
       "      <td>0.311333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Ginger Biscotti)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.106198</td>\n",
       "      <td>0.053666</td>\n",
       "      <td>0.137998</td>\n",
       "      <td>1.299441</td>\n",
       "      <td>0.012367</td>\n",
       "      <td>1.036891</td>\n",
       "      <td>0.377081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Almond Croissant)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.115646</td>\n",
       "      <td>0.053288</td>\n",
       "      <td>0.137026</td>\n",
       "      <td>1.184874</td>\n",
       "      <td>0.008314</td>\n",
       "      <td>1.024775</td>\n",
       "      <td>0.255319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(Cappuccino)</td>\n",
       "      <td>(Cranberry Scone)</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>0.116024</td>\n",
       "      <td>0.052532</td>\n",
       "      <td>0.135083</td>\n",
       "      <td>1.164262</td>\n",
       "      <td>0.007412</td>\n",
       "      <td>1.022035</td>\n",
       "      <td>0.230870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     antecedents                 consequents  antecedent support  \\\n",
       "26  (Cappuccino)  (Sugar Free Vanilla syrup)            0.388889   \n",
       "13  (Cappuccino)           (Chocolate syrup)            0.388889   \n",
       "6   (Cappuccino)              (Carmel syrup)            0.388889   \n",
       "22  (Cappuccino)            (Hazelnut syrup)            0.388889   \n",
       "20  (Cappuccino)              (Ginger Scone)            0.388889   \n",
       "16  (Cappuccino)                 (Croissant)            0.388889   \n",
       "8   (Cappuccino)   (Chocolate Chip Biscotti)            0.388889   \n",
       "10  (Cappuccino)       (Chocolate Croissant)            0.388889   \n",
       "24  (Cappuccino)        (Jumbo Savory Scone)            0.388889   \n",
       "18  (Cappuccino)           (Ginger Biscotti)            0.388889   \n",
       "0   (Cappuccino)          (Almond Croissant)            0.388889   \n",
       "15  (Cappuccino)           (Cranberry Scone)            0.388889   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "26            0.200302  0.113001    0.290573  1.450674  0.035105    1.127245   \n",
       "13            0.188964  0.109599    0.281827  1.491429  0.036113    1.129304   \n",
       "6             0.191232  0.102797    0.264334  1.382270  0.028429    1.099369   \n",
       "22            0.171580  0.090325    0.232264  1.353682  0.023600    1.079044   \n",
       "20            0.133409  0.059713    0.153547  1.150951  0.007832    1.023791   \n",
       "16            0.114135  0.056311    0.144801  1.268685  0.011926    1.035859   \n",
       "8             0.112623  0.056311    0.144801  1.285714  0.012514    1.037626   \n",
       "10            0.135676  0.056311    0.144801  1.067250  0.003548    1.010669   \n",
       "24            0.114890  0.055178    0.141885  1.234962  0.010498    1.031458   \n",
       "18            0.106198  0.053666    0.137998  1.299441  0.012367    1.036891   \n",
       "0             0.115646  0.053288    0.137026  1.184874  0.008314    1.024775   \n",
       "15            0.116024  0.052532    0.135083  1.164262  0.007412    1.022035   \n",
       "\n",
       "    zhangs_metric  \n",
       "26       0.508361  \n",
       "13       0.539185  \n",
       "6        0.452540  \n",
       "22       0.427539  \n",
       "20       0.214614  \n",
       "16       0.346553  \n",
       "8        0.363636  \n",
       "10       0.103112  \n",
       "24       0.311333  \n",
       "18       0.377081  \n",
       "0        0.255319  \n",
       "15       0.230870  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules_basket[rules_basket[\"antecedents\"]=={\"Cappuccino\"}].sort_values(\"confidence\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Cappuccino': 'Coffee',\n",
       " 'Jumbo Savory Scone': 'Bakery',\n",
       " 'Latte': 'Coffee',\n",
       " 'Chocolate Chip Biscotti': 'Bakery',\n",
       " 'Espresso shot': 'Coffee',\n",
       " 'Hazelnut Biscotti': 'Bakery',\n",
       " 'Chocolate Croissant': 'Bakery',\n",
       " 'Dark chocolate': 'Packaged Chocolate',\n",
       " 'Cranberry Scone': 'Bakery',\n",
       " 'Croissant': 'Bakery',\n",
       " 'Almond Croissant': 'Bakery',\n",
       " 'Ginger Biscotti': 'Bakery',\n",
       " 'Oatmeal Scone': 'Bakery',\n",
       " 'Ginger Scone': 'Bakery',\n",
       " 'Chocolate syrup': 'Flavours',\n",
       " 'Hazelnut syrup': 'Flavours',\n",
       " 'Carmel syrup': 'Flavours',\n",
       " 'Sugar Free Vanilla syrup': 'Flavours'}"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_categories_dict = dataset[[\"product\",\"product_category\"]].drop_duplicates().set_index(\"product\").to_dict()[\"product_category\"]\n",
    "product_categories_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Almond Croissant': [{'confidence': 0.4967320261437908,\n",
      "                       'product': 'Almond Croissant',\n",
      "                       'product_category': 'Bakery'},\n",
      "                      {'confidence': 0.47058823529411764,\n",
      "                       'product': 'Almond Croissant',\n",
      "                       'product_category': 'Bakery'},\n",
      "                      {'confidence': 0.46078431372549017,\n",
      "                       'product': 'Almond Croissant',\n",
      "                       'product_category': 'Bakery'}],\n",
      " 'Cappuccino': [{'confidence': 0.29057337220602525,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.2818270165208941,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.26433430515063167,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.23226433430515062,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.15354713313896987,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.14480077745383868,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.14480077745383868,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.14480077745383868,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.14188532555879496,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.1379980563654033,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.13702623906705538,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'},\n",
      "                {'confidence': 0.13508260447035958,\n",
      "                 'product': 'Cappuccino',\n",
      "                 'product_category': 'Coffee'}],\n",
      " 'Carmel syrup': [{'confidence': 0.5632411067193677,\n",
      "                   'product': 'Carmel syrup',\n",
      "                   'product_category': 'Flavours'},\n",
      "                  {'confidence': 0.5375494071146245,\n",
      "                   'product': 'Carmel syrup',\n",
      "                   'product_category': 'Flavours'},\n",
      "                  {'confidence': 0.31620553359683795,\n",
      "                   'product': 'Carmel syrup',\n",
      "                   'product_category': 'Flavours'},\n",
      "                  {'confidence': 0.2885375494071146,\n",
      "                   'product': 'Carmel syrup',\n",
      "                   'product_category': 'Flavours'}],\n",
      " 'Chocolate Chip Biscotti': [{'confidence': 0.5,\n",
      "                              'product': 'Chocolate Chip Biscotti',\n",
      "                              'product_category': 'Bakery'},\n",
      "                             {'confidence': 0.47651006711409394,\n",
      "                              'product': 'Chocolate Chip Biscotti',\n",
      "                              'product_category': 'Bakery'}],\n",
      " 'Chocolate Croissant': [{'confidence': 0.44289693593314766,\n",
      "                          'product': 'Chocolate Croissant',\n",
      "                          'product_category': 'Bakery'},\n",
      "                         {'confidence': 0.415041782729805,\n",
      "                          'product': 'Chocolate Croissant',\n",
      "                          'product_category': 'Bakery'},\n",
      "                         {'confidence': 0.40668523676880225,\n",
      "                          'product': 'Chocolate Croissant',\n",
      "                          'product_category': 'Bakery'}],\n",
      " 'Chocolate syrup': [{'confidence': 0.58,\n",
      "                      'product': 'Chocolate syrup',\n",
      "                      'product_category': 'Flavours'},\n",
      "                     {'confidence': 0.5459999999999999,\n",
      "                      'product': 'Chocolate syrup',\n",
      "                      'product_category': 'Flavours'},\n",
      "                     {'confidence': 0.312,\n",
      "                      'product': 'Chocolate syrup',\n",
      "                      'product_category': 'Flavours'},\n",
      "                     {'confidence': 0.27999999999999997,\n",
      "                      'product': 'Chocolate syrup',\n",
      "                      'product_category': 'Flavours'}],\n",
      " 'Cranberry Scone': [{'confidence': 0.48534201954397393,\n",
      "                      'product': 'Cranberry Scone',\n",
      "                      'product_category': 'Bakery'},\n",
      "                     {'confidence': 0.4527687296416938,\n",
      "                      'product': 'Cranberry Scone',\n",
      "                      'product_category': 'Bakery'},\n",
      "                     {'confidence': 0.4429967426710098,\n",
      "                      'product': 'Cranberry Scone',\n",
      "                      'product_category': 'Bakery'}],\n",
      " 'Croissant': [{'confidence': 0.5,\n",
      "                'product': 'Croissant',\n",
      "                'product_category': 'Bakery'},\n",
      "               {'confidence': 0.49337748344370863,\n",
      "                'product': 'Croissant',\n",
      "                'product_category': 'Bakery'},\n",
      "               {'confidence': 0.4470198675496689,\n",
      "                'product': 'Croissant',\n",
      "                'product_category': 'Bakery'}],\n",
      " 'Dark chocolate': [{'confidence': 0.22100954979536153,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.2169167803547067,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.21555252387448842,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.20736698499317874,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.2032742155525239,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.19918144611186905,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.19372442019099592,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.19099590723055934,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.18553888130968624,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'},\n",
      "                    {'confidence': 0.18417462482946795,\n",
      "                     'product': 'Dark chocolate',\n",
      "                     'product_category': 'Packaged Chocolate'}],\n",
      " 'Espresso shot': [{'confidence': 0.2924187725631769,\n",
      "                    'product': 'Espresso shot',\n",
      "                    'product_category': 'Coffee'},\n",
      "                   {'confidence': 0.2924187725631769,\n",
      "                    'product': 'Espresso shot',\n",
      "                    'product_category': 'Coffee'},\n",
      "                   {'confidence': 0.2888086642599278,\n",
      "                    'product': 'Espresso shot',\n",
      "                    'product_category': 'Coffee'},\n",
      "                   {'confidence': 0.2815884476534296,\n",
      "                    'product': 'Espresso shot',\n",
      "                    'product_category': 'Coffee'},\n",
      "                   {'confidence': 0.26353790613718414,\n",
      "                    'product': 'Espresso shot',\n",
      "                    'product_category': 'Coffee'}],\n",
      " 'Ginger Biscotti': [{'confidence': 0.5088967971530249,\n",
      "                      'product': 'Ginger Biscotti',\n",
      "                      'product_category': 'Bakery'},\n",
      "                     {'confidence': 0.505338078291815,\n",
      "                      'product': 'Ginger Biscotti',\n",
      "                      'product_category': 'Bakery'}],\n",
      " 'Ginger Scone': [{'confidence': 0.4475920679886685,\n",
      "                   'product': 'Ginger Scone',\n",
      "                   'product_category': 'Bakery'},\n",
      "                  {'confidence': 0.4475920679886685,\n",
      "                   'product': 'Ginger Scone',\n",
      "                   'product_category': 'Bakery'},\n",
      "                  {'confidence': 0.41359773371104813,\n",
      "                   'product': 'Ginger Scone',\n",
      "                   'product_category': 'Bakery'}],\n",
      " 'Hazelnut syrup': [{'confidence': 0.5903083700440529,\n",
      "                     'product': 'Hazelnut syrup',\n",
      "                     'product_category': 'Flavours'},\n",
      "                    {'confidence': 0.526431718061674,\n",
      "                     'product': 'Hazelnut syrup',\n",
      "                     'product_category': 'Flavours'},\n",
      "                    {'confidence': 0.32158590308370044,\n",
      "                     'product': 'Hazelnut syrup',\n",
      "                     'product_category': 'Flavours'}],\n",
      " 'Jumbo Savory Scone': [{'confidence': 0.48026315789473684,\n",
      "                         'product': 'Jumbo Savory Scone',\n",
      "                         'product_category': 'Bakery'},\n",
      "                        {'confidence': 0.4473684210526316,\n",
      "                         'product': 'Jumbo Savory Scone',\n",
      "                         'product_category': 'Bakery'}],\n",
      " 'Latte': [{'confidence': 0.28486646884273,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.2818991097922849,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.27002967359050445,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.26508407517309596,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.14935707220573688,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.1444114737883284,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.1444114737883284,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.142433234421365,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.1414441147378833,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'},\n",
      "           {'confidence': 0.13452027695351138,\n",
      "            'product': 'Latte',\n",
      "            'product_category': 'Coffee'}],\n",
      " 'Sugar Free Vanilla syrup': [{'confidence': 0.5641509433962264,\n",
      "                               'product': 'Sugar Free Vanilla syrup',\n",
      "                               'product_category': 'Flavours'},\n",
      "                              {'confidence': 0.5433962264150943,\n",
      "                               'product': 'Sugar Free Vanilla syrup',\n",
      "                               'product_category': 'Flavours'},\n",
      "                              {'confidence': 0.30566037735849055,\n",
      "                               'product': 'Sugar Free Vanilla syrup',\n",
      "                               'product_category': 'Flavours'}]}\n"
     ]
    }
   ],
   "source": [
    "recommendations_json = {}\n",
    "antecedents = rules_basket[\"antecedents\"].unique()\n",
    "for antecedent in antecedents:\n",
    "    df_rec = rules_basket[rules_basket[\"antecedents\"] == antecedent]\n",
    "    df_rec = df_rec.sort_values(\"confidence\", ascending=False)\n",
    "\n",
    "    antecedent = \"_\".join(antecedent)\n",
    "    recommendations_json[antecedent] = []\n",
    "    for _, row in df_rec.iterrows():\n",
    "        consequents = row[\"consequents\"]\n",
    "        for consequent in consequents:\n",
    "            alread_exists = False\n",
    "            for x in recommendations_json[antecedent]:\n",
    "                if consequent ==  x[\"product\"]:\n",
    "                    alread_exists = True\n",
    "            \n",
    "            if alread_exists:\n",
    "                continue\n",
    "            recommendations_json[antecedent].append(\n",
    "                {\n",
    "                    \"product\": antecedent,\n",
    "                    \"product_category\": product_categories_dict[antecedent],\n",
    "                    \"confidence\": row[\"confidence\"]\n",
    "                }\n",
    "            )\n",
    "pprint(recommendations_json)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"api/recommendation_objects/apriori_recommendation.json\", \"w\") as json_file:\n",
    "    json.dump(recommendations_json, json_file)"
   ]
  }
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