Grocery Basket Analysis and Recommendation-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grocery Product Recommendation using Apriori Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from mlxtend import frequent_patterns \n",
    "from mlxtend.preprocessing import TransactionEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shrimp</td>\n",
       "      <td>almonds</td>\n",
       "      <td>avocado</td>\n",
       "      <td>vegetables mix</td>\n",
       "      <td>green grapes</td>\n",
       "      <td>whole weat flour</td>\n",
       "      <td>yams</td>\n",
       "      <td>cottage cheese</td>\n",
       "      <td>energy drink</td>\n",
       "      <td>tomato juice</td>\n",
       "      <td>low fat yogurt</td>\n",
       "      <td>green tea</td>\n",
       "      <td>honey</td>\n",
       "      <td>salad</td>\n",
       "      <td>mineral water</td>\n",
       "      <td>salmon</td>\n",
       "      <td>antioxydant juice</td>\n",
       "      <td>frozen smoothie</td>\n",
       "      <td>spinach</td>\n",
       "      <td>olive oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>burgers</td>\n",
       "      <td>meatballs</td>\n",
       "      <td>eggs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chutney</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>turkey</td>\n",
       "      <td>avocado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mineral water</td>\n",
       "      <td>milk</td>\n",
       "      <td>energy bar</td>\n",
       "      <td>whole wheat rice</td>\n",
       "      <td>green tea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0          1           2                 3             4   \\\n",
       "0         shrimp    almonds     avocado    vegetables mix  green grapes   \n",
       "1        burgers  meatballs        eggs               NaN           NaN   \n",
       "2        chutney        NaN         NaN               NaN           NaN   \n",
       "3         turkey    avocado         NaN               NaN           NaN   \n",
       "4  mineral water       milk  energy bar  whole wheat rice     green tea   \n",
       "\n",
       "                 5     6               7             8             9   \\\n",
       "0  whole weat flour  yams  cottage cheese  energy drink  tomato juice   \n",
       "1               NaN   NaN             NaN           NaN           NaN   \n",
       "2               NaN   NaN             NaN           NaN           NaN   \n",
       "3               NaN   NaN             NaN           NaN           NaN   \n",
       "4               NaN   NaN             NaN           NaN           NaN   \n",
       "\n",
       "               10         11     12     13             14      15  \\\n",
       "0  low fat yogurt  green tea  honey  salad  mineral water  salmon   \n",
       "1             NaN        NaN    NaN    NaN            NaN     NaN   \n",
       "2             NaN        NaN    NaN    NaN            NaN     NaN   \n",
       "3             NaN        NaN    NaN    NaN            NaN     NaN   \n",
       "4             NaN        NaN    NaN    NaN            NaN     NaN   \n",
       "\n",
       "                  16               17       18         19  \n",
       "0  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
       "1                NaN              NaN      NaN        NaN  \n",
       "2                NaN              NaN      NaN        NaN  \n",
       "3                NaN              NaN      NaN        NaN  \n",
       "4                NaN              NaN      NaN        NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"store_data.csv\", header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7501, 20)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "# 7501 Orders with maximun 20 products bought together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.values[1,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<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>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shrimp</td>\n",
       "      <td>almonds</td>\n",
       "      <td>avocado</td>\n",
       "      <td>vegetables mix</td>\n",
       "      <td>green grapes</td>\n",
       "      <td>whole weat flour</td>\n",
       "      <td>yams</td>\n",
       "      <td>cottage cheese</td>\n",
       "      <td>energy drink</td>\n",
       "      <td>tomato juice</td>\n",
       "      <td>low fat yogurt</td>\n",
       "      <td>green tea</td>\n",
       "      <td>honey</td>\n",
       "      <td>salad</td>\n",
       "      <td>mineral water</td>\n",
       "      <td>salmon</td>\n",
       "      <td>antioxydant juice</td>\n",
       "      <td>frozen smoothie</td>\n",
       "      <td>spinach</td>\n",
       "      <td>olive oil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>burgers</td>\n",
       "      <td>meatballs</td>\n",
       "      <td>eggs</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",
       "      <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>2</th>\n",
       "      <td>chutney</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",
       "      <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>3</th>\n",
       "      <td>turkey</td>\n",
       "      <td>avocado</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",
       "      <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>4</th>\n",
       "      <td>mineral water</td>\n",
       "      <td>milk</td>\n",
       "      <td>energy bar</td>\n",
       "      <td>whole wheat rice</td>\n",
       "      <td>green tea</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              0          1           2                 3             4   \\\n",
       "0         shrimp    almonds     avocado    vegetables mix  green grapes   \n",
       "1        burgers  meatballs        eggs                 0             0   \n",
       "2        chutney          0           0                 0             0   \n",
       "3         turkey    avocado           0                 0             0   \n",
       "4  mineral water       milk  energy bar  whole wheat rice     green tea   \n",
       "\n",
       "                 5     6               7             8             9   \\\n",
       "0  whole weat flour  yams  cottage cheese  energy drink  tomato juice   \n",
       "1                 0     0               0             0             0   \n",
       "2                 0     0               0             0             0   \n",
       "3                 0     0               0             0             0   \n",
       "4                 0     0               0             0             0   \n",
       "\n",
       "               10         11     12     13             14      15  \\\n",
       "0  low fat yogurt  green tea  honey  salad  mineral water  salmon   \n",
       "1               0          0      0      0              0       0   \n",
       "2               0          0      0      0              0       0   \n",
       "3               0          0      0      0              0       0   \n",
       "4               0          0      0      0              0       0   \n",
       "\n",
       "                  16               17       18         19  \n",
       "0  antioxydant juice  frozen smoothie  spinach  olive oil  \n",
       "1                  0                0        0          0  \n",
       "2                  0                0        0          0  \n",
       "3                  0                0        0          0  \n",
       "4                  0                0        0          0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= df.fillna(0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "records = []\n",
    "for i in range(0, 7501):\n",
    "    records.append([str(df.values[i, j]) for j in range(0, 20) if df.values[i, j] != 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "products = []\n",
    "for i in range(1, 7501):\n",
    "    for j in range(20):\n",
    "        if str(df.values[i,j]) not in products:\n",
    "            products.append(str(df.values[i,j]))\n",
    "            \n",
    "products.remove(\"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total products:  120\n"
     ]
    }
   ],
   "source": [
    "print(\"Total products: \", len(products))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "te = TransactionEncoder()\n",
    "te_array = te.fit(records).transform(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asparagus</th>\n",
       "      <th>almonds</th>\n",
       "      <th>antioxydant juice</th>\n",
       "      <th>asparagus</th>\n",
       "      <th>avocado</th>\n",
       "      <th>babies food</th>\n",
       "      <th>bacon</th>\n",
       "      <th>barbecue sauce</th>\n",
       "      <th>black tea</th>\n",
       "      <th>blueberries</th>\n",
       "      <th>...</th>\n",
       "      <th>turkey</th>\n",
       "      <th>vegetables mix</th>\n",
       "      <th>water spray</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole weat flour</th>\n",
       "      <th>whole wheat pasta</th>\n",
       "      <th>whole wheat rice</th>\n",
       "      <th>yams</th>\n",
       "      <th>yogurt cake</th>\n",
       "      <th>zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    asparagus  almonds  antioxydant juice  asparagus  avocado  babies food  \\\n",
       "0       False     True               True      False     True        False   \n",
       "1       False    False              False      False    False        False   \n",
       "2       False    False              False      False    False        False   \n",
       "3       False    False              False      False     True        False   \n",
       "4       False    False              False      False    False        False   \n",
       "\n",
       "   bacon  barbecue sauce  black tea  blueberries  ...  turkey  vegetables mix  \\\n",
       "0  False           False      False        False  ...   False            True   \n",
       "1  False           False      False        False  ...   False           False   \n",
       "2  False           False      False        False  ...   False           False   \n",
       "3  False           False      False        False  ...    True           False   \n",
       "4  False           False      False        False  ...   False           False   \n",
       "\n",
       "   water spray  white wine  whole weat flour  whole wheat pasta  \\\n",
       "0        False       False              True              False   \n",
       "1        False       False             False              False   \n",
       "2        False       False             False              False   \n",
       "3        False       False             False              False   \n",
       "4        False       False             False              False   \n",
       "\n",
       "   whole wheat rice   yams  yogurt cake  zucchini  \n",
       "0             False   True        False     False  \n",
       "1             False  False        False     False  \n",
       "2             False  False        False     False  \n",
       "3             False  False        False     False  \n",
       "4              True  False        False     False  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trans = pd.DataFrame(te_array, columns=te.columns_)\n",
    "df_trans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful insights from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mineral water        1788\n",
       "eggs                 1348\n",
       "spaghetti            1306\n",
       "french fries         1282\n",
       "chocolate            1229\n",
       "green tea             991\n",
       "milk                  972\n",
       "ground beef           737\n",
       "frozen vegetables     715\n",
       "pancakes              713\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of top 10 products sold\n",
    "df_trans.sum().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Apriori Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.020397</td>\n",
       "      <td>(almonds)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.033329</td>\n",
       "      <td>(avocado)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.033729</td>\n",
       "      <td>(brownies)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.087188</td>\n",
       "      <td>(burgers)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.030129</td>\n",
       "      <td>(butter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.020931</td>\n",
       "      <td>(spaghetti, tomatoes)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.016531</td>\n",
       "      <td>(spaghetti, turkey)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.015865</td>\n",
       "      <td>(spaghetti, mineral water, chocolate)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.017064</td>\n",
       "      <td>(spaghetti, mineral water, ground beef)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.015731</td>\n",
       "      <td>(spaghetti, mineral water, milk)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      support                                 itemsets\n",
       "0    0.020397                                (almonds)\n",
       "1    0.033329                                (avocado)\n",
       "2    0.033729                               (brownies)\n",
       "3    0.087188                                (burgers)\n",
       "4    0.030129                                 (butter)\n",
       "..        ...                                      ...\n",
       "148  0.020931                    (spaghetti, tomatoes)\n",
       "149  0.016531                      (spaghetti, turkey)\n",
       "150  0.015865    (spaghetti, mineral water, chocolate)\n",
       "151  0.017064  (spaghetti, mineral water, ground beef)\n",
       "152  0.015731         (spaghetti, mineral water, milk)\n",
       "\n",
       "[153 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_itemsets = frequent_patterns.apriori(df_trans,min_support=0.015,use_colnames=True)\n",
    "freq_itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>(soup)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.050527</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.023064</td>\n",
       "      <td>0.456464</td>\n",
       "      <td>1.914955</td>\n",
       "      <td>0.011020</td>\n",
       "      <td>1.401255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>(spaghetti, milk)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.035462</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.015731</td>\n",
       "      <td>0.443609</td>\n",
       "      <td>1.861024</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>1.368879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>(spaghetti, ground beef)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.039195</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.435374</td>\n",
       "      <td>1.826477</td>\n",
       "      <td>0.007722</td>\n",
       "      <td>1.348914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>(olive oil)</td>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>0.065858</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.027596</td>\n",
       "      <td>0.419028</td>\n",
       "      <td>1.757904</td>\n",
       "      <td>0.011898</td>\n",
       "      <td>1.310962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>(mineral water, ground beef)</td>\n",
       "      <td>(spaghetti)</td>\n",
       "      <td>0.040928</td>\n",
       "      <td>0.174110</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.416938</td>\n",
       "      <td>2.394681</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>1.416470</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>(salmon)</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.042528</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.071588</td>\n",
       "      <td>1.683336</td>\n",
       "      <td>0.006927</td>\n",
       "      <td>1.031302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>(herb &amp; pepper)</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.049460</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.071588</td>\n",
       "      <td>1.447397</td>\n",
       "      <td>0.005275</td>\n",
       "      <td>1.023835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>(spaghetti, chocolate)</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.039195</td>\n",
       "      <td>0.015865</td>\n",
       "      <td>0.066555</td>\n",
       "      <td>1.698053</td>\n",
       "      <td>0.006522</td>\n",
       "      <td>1.029311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>(spaghetti, milk)</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.035462</td>\n",
       "      <td>0.015731</td>\n",
       "      <td>0.065996</td>\n",
       "      <td>1.861024</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>1.032691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>(mineral water)</td>\n",
       "      <td>(honey)</td>\n",
       "      <td>0.238368</td>\n",
       "      <td>0.047460</td>\n",
       "      <td>0.015065</td>\n",
       "      <td>0.063199</td>\n",
       "      <td>1.331619</td>\n",
       "      <td>0.003752</td>\n",
       "      <td>1.016801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      antecedents             consequents  antecedent support  \\\n",
       "148                        (soup)         (mineral water)            0.050527   \n",
       "181             (spaghetti, milk)         (mineral water)            0.035462   \n",
       "175      (spaghetti, ground beef)         (mineral water)            0.039195   \n",
       "141                   (olive oil)         (mineral water)            0.065858   \n",
       "176  (mineral water, ground beef)             (spaghetti)            0.040928   \n",
       "..                            ...                     ...                 ...   \n",
       "145               (mineral water)                (salmon)            0.238368   \n",
       "119               (mineral water)         (herb & pepper)            0.238368   \n",
       "172               (mineral water)  (spaghetti, chocolate)            0.238368   \n",
       "184               (mineral water)       (spaghetti, milk)            0.238368   \n",
       "122               (mineral water)                 (honey)            0.238368   \n",
       "\n",
       "     consequent support   support  confidence      lift  leverage  conviction  \n",
       "148            0.238368  0.023064    0.456464  1.914955  0.011020    1.401255  \n",
       "181            0.238368  0.015731    0.443609  1.861024  0.007278    1.368879  \n",
       "175            0.238368  0.017064    0.435374  1.826477  0.007722    1.348914  \n",
       "141            0.238368  0.027596    0.419028  1.757904  0.011898    1.310962  \n",
       "176            0.174110  0.017064    0.416938  2.394681  0.009938    1.416470  \n",
       "..                  ...       ...         ...       ...       ...         ...  \n",
       "145            0.042528  0.017064    0.071588  1.683336  0.006927    1.031302  \n",
       "119            0.049460  0.017064    0.071588  1.447397  0.005275    1.023835  \n",
       "172            0.039195  0.015865    0.066555  1.698053  0.006522    1.029311  \n",
       "184            0.035462  0.015731    0.065996  1.861024  0.007278    1.032691  \n",
       "122            0.047460  0.015065    0.063199  1.331619  0.003752    1.016801  \n",
       "\n",
       "[186 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules = frequent_patterns.association_rules(freq_itemsets,metric='lift',min_threshold=1)\n",
    "rules.sort_values(by='confidence',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>(shrimp)</td>\n",
       "      <td>0.095321</td>\n",
       "      <td>0.071457</td>\n",
       "      <td>0.016664</td>\n",
       "      <td>0.174825</td>\n",
       "      <td>2.446574</td>\n",
       "      <td>0.009853</td>\n",
       "      <td>1.125268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>(shrimp)</td>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>0.071457</td>\n",
       "      <td>0.095321</td>\n",
       "      <td>0.016664</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>2.446574</td>\n",
       "      <td>0.009853</td>\n",
       "      <td>1.179825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>(tomatoes)</td>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>0.068391</td>\n",
       "      <td>0.095321</td>\n",
       "      <td>0.016131</td>\n",
       "      <td>0.235867</td>\n",
       "      <td>2.474464</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>1.183930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>(tomatoes)</td>\n",
       "      <td>0.095321</td>\n",
       "      <td>0.068391</td>\n",
       "      <td>0.016131</td>\n",
       "      <td>0.169231</td>\n",
       "      <td>2.474464</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>1.121381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>(herb &amp; pepper)</td>\n",
       "      <td>(ground beef)</td>\n",
       "      <td>0.049460</td>\n",
       "      <td>0.098254</td>\n",
       "      <td>0.015998</td>\n",
       "      <td>0.323450</td>\n",
       "      <td>3.291994</td>\n",
       "      <td>0.011138</td>\n",
       "      <td>1.332860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>(ground beef)</td>\n",
       "      <td>(herb &amp; pepper)</td>\n",
       "      <td>0.098254</td>\n",
       "      <td>0.049460</td>\n",
       "      <td>0.015998</td>\n",
       "      <td>0.162822</td>\n",
       "      <td>3.291994</td>\n",
       "      <td>0.011138</td>\n",
       "      <td>1.135410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>(spaghetti)</td>\n",
       "      <td>(ground beef)</td>\n",
       "      <td>0.174110</td>\n",
       "      <td>0.098254</td>\n",
       "      <td>0.039195</td>\n",
       "      <td>0.225115</td>\n",
       "      <td>2.291162</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>1.163716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>(ground beef)</td>\n",
       "      <td>(spaghetti)</td>\n",
       "      <td>0.098254</td>\n",
       "      <td>0.174110</td>\n",
       "      <td>0.039195</td>\n",
       "      <td>0.398915</td>\n",
       "      <td>2.291162</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>1.373997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>(soup)</td>\n",
       "      <td>(milk)</td>\n",
       "      <td>0.050527</td>\n",
       "      <td>0.129583</td>\n",
       "      <td>0.015198</td>\n",
       "      <td>0.300792</td>\n",
       "      <td>2.321232</td>\n",
       "      <td>0.008651</td>\n",
       "      <td>1.244861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>(spaghetti, mineral water)</td>\n",
       "      <td>(ground beef)</td>\n",
       "      <td>0.059725</td>\n",
       "      <td>0.098254</td>\n",
       "      <td>0.017064</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.907928</td>\n",
       "      <td>0.011196</td>\n",
       "      <td>1.262445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    antecedents          consequents  antecedent support  \\\n",
       "94          (frozen vegetables)             (shrimp)            0.095321   \n",
       "95                     (shrimp)  (frozen vegetables)            0.071457   \n",
       "98                   (tomatoes)  (frozen vegetables)            0.068391   \n",
       "99          (frozen vegetables)           (tomatoes)            0.095321   \n",
       "110             (herb & pepper)        (ground beef)            0.049460   \n",
       "111               (ground beef)      (herb & pepper)            0.098254   \n",
       "116                 (spaghetti)        (ground beef)            0.174110   \n",
       "117               (ground beef)          (spaghetti)            0.098254   \n",
       "136                      (soup)               (milk)            0.050527   \n",
       "174  (spaghetti, mineral water)        (ground beef)            0.059725   \n",
       "\n",
       "     consequent support   support  confidence      lift  leverage  conviction  \n",
       "94             0.071457  0.016664    0.174825  2.446574  0.009853    1.125268  \n",
       "95             0.095321  0.016664    0.233209  2.446574  0.009853    1.179825  \n",
       "98             0.095321  0.016131    0.235867  2.474464  0.009612    1.183930  \n",
       "99             0.068391  0.016131    0.169231  2.474464  0.009612    1.121381  \n",
       "110            0.098254  0.015998    0.323450  3.291994  0.011138    1.332860  \n",
       "111            0.049460  0.015998    0.162822  3.291994  0.011138    1.135410  \n",
       "116            0.098254  0.039195    0.225115  2.291162  0.022088    1.163716  \n",
       "117            0.174110  0.039195    0.398915  2.291162  0.022088    1.373997  \n",
       "136            0.129583  0.015198    0.300792  2.321232  0.008651    1.244861  \n",
       "174            0.098254  0.017064    0.285714  2.907928  0.011196    1.262445  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[(rules['confidence']>0.16) & (rules['lift']>2)].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([frozenset({'chocolate'}), frozenset({'burgers'}),\n",
       "       frozenset({'eggs'}), frozenset({'burgers'}),\n",
       "       frozenset({'burgers'}), frozenset({'french fries'}),\n",
       "       frozenset({'green tea'}), frozenset({'burgers'}),\n",
       "       frozenset({'burgers'}), frozenset({'milk'}),\n",
       "       frozenset({'mineral water'}), frozenset({'burgers'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'burgers'}),\n",
       "       frozenset({'cake'}), frozenset({'eggs'}), frozenset({'cake'}),\n",
       "       frozenset({'french fries'}), frozenset({'cake'}),\n",
       "       frozenset({'mineral water'}), frozenset({'cake'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'mineral water'}),\n",
       "       frozenset({'chicken'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'chicken'}), frozenset({'eggs'}),\n",
       "       frozenset({'chocolate'}), frozenset({'chocolate'}),\n",
       "       frozenset({'escalope'}), frozenset({'chocolate'}),\n",
       "       frozenset({'french fries'}), frozenset({'frozen vegetables'}),\n",
       "       frozenset({'chocolate'}), frozenset({'chocolate'}),\n",
       "       frozenset({'green tea'}), frozenset({'ground beef'}),\n",
       "       frozenset({'chocolate'}), frozenset({'chocolate'}),\n",
       "       frozenset({'milk'}), frozenset({'mineral water'}),\n",
       "       frozenset({'chocolate'}), frozenset({'olive oil'}),\n",
       "       frozenset({'chocolate'}), frozenset({'pancakes'}),\n",
       "       frozenset({'chocolate'}), frozenset({'chocolate'}),\n",
       "       frozenset({'shrimp'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'chocolate'}), frozenset({'cooking oil'}),\n",
       "       frozenset({'mineral water'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'cooking oil'}), frozenset({'eggs'}),\n",
       "       frozenset({'french fries'}), frozenset({'eggs'}),\n",
       "       frozenset({'frozen vegetables'}), frozenset({'eggs'}),\n",
       "       frozenset({'green tea'}), frozenset({'eggs'}),\n",
       "       frozenset({'ground beef'}), frozenset({'eggs'}),\n",
       "       frozenset({'low fat yogurt'}), frozenset({'eggs'}),\n",
       "       frozenset({'milk'}), frozenset({'eggs'}),\n",
       "       frozenset({'mineral water'}), frozenset({'eggs'}),\n",
       "       frozenset({'pancakes'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'eggs'}), frozenset({'eggs'}), frozenset({'turkey'}),\n",
       "       frozenset({'french fries'}), frozenset({'escalope'}),\n",
       "       frozenset({'frozen vegetables'}), frozenset({'french fries'}),\n",
       "       frozenset({'green tea'}), frozenset({'french fries'}),\n",
       "       frozenset({'french fries'}), frozenset({'milk'}),\n",
       "       frozenset({'pancakes'}), frozenset({'french fries'}),\n",
       "       frozenset({'frozen smoothie'}), frozenset({'mineral water'}),\n",
       "       frozenset({'frozen smoothie'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'ground beef'}), frozenset({'frozen vegetables'}),\n",
       "       frozenset({'frozen vegetables'}), frozenset({'milk'}),\n",
       "       frozenset({'mineral water'}), frozenset({'frozen vegetables'}),\n",
       "       frozenset({'frozen vegetables'}), frozenset({'shrimp'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'frozen vegetables'}),\n",
       "       frozenset({'tomatoes'}), frozenset({'frozen vegetables'}),\n",
       "       frozenset({'mineral water'}), frozenset({'grated cheese'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'grated cheese'}),\n",
       "       frozenset({'green tea'}), frozenset({'milk'}),\n",
       "       frozenset({'pancakes'}), frozenset({'green tea'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'green tea'}),\n",
       "       frozenset({'herb & pepper'}), frozenset({'ground beef'}),\n",
       "       frozenset({'ground beef'}), frozenset({'milk'}),\n",
       "       frozenset({'mineral water'}), frozenset({'ground beef'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'ground beef'}),\n",
       "       frozenset({'herb & pepper'}), frozenset({'mineral water'}),\n",
       "       frozenset({'herb & pepper'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'mineral water'}), frozenset({'honey'}),\n",
       "       frozenset({'mineral water'}), frozenset({'low fat yogurt'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'low fat yogurt'}),\n",
       "       frozenset({'mineral water'}), frozenset({'milk'}),\n",
       "       frozenset({'olive oil'}), frozenset({'milk'}),\n",
       "       frozenset({'pancakes'}), frozenset({'milk'}),\n",
       "       frozenset({'shrimp'}), frozenset({'milk'}), frozenset({'soup'}),\n",
       "       frozenset({'milk'}), frozenset({'spaghetti'}), frozenset({'milk'}),\n",
       "       frozenset({'mineral water'}), frozenset({'olive oil'}),\n",
       "       frozenset({'mineral water'}), frozenset({'pancakes'}),\n",
       "       frozenset({'salmon'}), frozenset({'mineral water'}),\n",
       "       frozenset({'mineral water'}), frozenset({'shrimp'}),\n",
       "       frozenset({'soup'}), frozenset({'mineral water'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'mineral water'}),\n",
       "       frozenset({'tomatoes'}), frozenset({'mineral water'}),\n",
       "       frozenset({'mineral water'}), frozenset({'turkey'}),\n",
       "       frozenset({'mineral water'}), frozenset({'whole wheat rice'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'olive oil'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'pancakes'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'shrimp'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'tomatoes'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'turkey'}),\n",
       "       frozenset({'spaghetti', 'mineral water'}),\n",
       "       frozenset({'spaghetti', 'chocolate'}),\n",
       "       frozenset({'mineral water', 'chocolate'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'mineral water'}),\n",
       "       frozenset({'chocolate'}),\n",
       "       frozenset({'spaghetti', 'mineral water'}),\n",
       "       frozenset({'spaghetti', 'ground beef'}),\n",
       "       frozenset({'mineral water', 'ground beef'}),\n",
       "       frozenset({'spaghetti'}), frozenset({'mineral water'}),\n",
       "       frozenset({'ground beef'}),\n",
       "       frozenset({'spaghetti', 'mineral water'}),\n",
       "       frozenset({'spaghetti', 'milk'}),\n",
       "       frozenset({'mineral water', 'milk'}), frozenset({'spaghetti'}),\n",
       "       frozenset({'mineral water'}), frozenset({'milk'})], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules.antecedents.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rules[\"antecedents\"] = rules[\"antecedents\"].apply(lambda x: list(x)[0]).astype(\"unicode\")\n",
    "rules[\"consequents\"] = rules[\"consequents\"].apply(lambda x: list(x)[0]).astype(\"unicode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mineral water', 'spaghetti', 'chocolate', 'milk', 'frozen vegetables']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules[rules[\"antecedents\"] == 'shrimp'].sort_values(\"confidence\", ascending=False)[\"consequents\"].head(5).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "rules.to_csv(\"recommedations.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend(product, items=3):\n",
    "    top_recommended_products = rules[rules[\"antecedents\"] == product].sort_values(\n",
    "        \"confidence\", ascending=False)[\"consequents\"].head(items).tolist()\n",
    "    return top_recommended_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mineral water', 'spaghetti', 'chocolate', 'milk', 'frozen vegetables']\n"
     ]
    }
   ],
   "source": [
    "print(recommend(\"shrimp\",5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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