MRA - Grocery Product Data_Case B_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5726df1",
   "metadata": {},
   "source": [
    "### Import the required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17352539",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d58a3e",
   "metadata": {},
   "source": [
    "### Load the required Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20148611",
   "metadata": {},
   "outputs": [],
   "source": [
    "grocery = pd.read_csv('dataset_group.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bcc50f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Order_id</th>\n",
       "      <th>Product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>pork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>sandwich bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>lunch meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-01-2018</td>\n",
       "      <td>1</td>\n",
       "      <td>all- purpose</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Order_id        Product\n",
       "0  01-01-2018         1         yogurt\n",
       "1  01-01-2018         1           pork\n",
       "2  01-01-2018         1  sandwich bags\n",
       "3  01-01-2018         1     lunch meat\n",
       "4  01-01-2018         1   all- purpose"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grocery.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5d68306",
   "metadata": {},
   "source": [
    "### Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8bb4456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows are: 20641 and the number of columns are: 3\n"
     ]
    }
   ],
   "source": [
    "grocery.shape\n",
    "print(\"The number of rows are:\", grocery.shape[0],\"and the number of columns are:\", grocery.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97f35489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20641 entries, 0 to 20640\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Date      20641 non-null  object\n",
      " 1   Order_id  20641 non-null  int64 \n",
      " 2   Product   20641 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 483.9+ KB\n"
     ]
    }
   ],
   "source": [
    "grocery.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bfe561",
   "metadata": {},
   "source": [
    "### Check for Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4b36b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Order_id    0\n",
       "Product     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grocery.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd32480c",
   "metadata": {},
   "source": [
    "### Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa8b7947",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order_id</th>\n",
       "      <td>20641.0</td>\n",
       "      <td>575.986289</td>\n",
       "      <td>328.557078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>862.0</td>\n",
       "      <td>1139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean         std  min    25%    50%    75%     max\n",
       "Order_id  20641.0  575.986289  328.557078  1.0  292.0  581.0  862.0  1139.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grocery.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7932c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grocery['Product'].value_counts().sort_values(ascending=False).head(37).plot(kind='bar',figsize=(12,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bb8275",
   "metadata": {},
   "source": [
    "### Market Basket Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5542142",
   "metadata": {},
   "outputs": [],
   "source": [
    "basket = grocery.groupby(['Order_id', 'Product'])['Product'].count().unstack().reset_index().fillna(0).set_index('Order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4107af7",
   "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>Product</th>\n",
       "      <th>all- purpose</th>\n",
       "      <th>aluminum foil</th>\n",
       "      <th>bagels</th>\n",
       "      <th>beef</th>\n",
       "      <th>butter</th>\n",
       "      <th>cereals</th>\n",
       "      <th>cheeses</th>\n",
       "      <th>coffee/tea</th>\n",
       "      <th>dinner rolls</th>\n",
       "      <th>dishwashing liquid/detergent</th>\n",
       "      <th>...</th>\n",
       "      <th>sandwich loaves</th>\n",
       "      <th>shampoo</th>\n",
       "      <th>soap</th>\n",
       "      <th>soda</th>\n",
       "      <th>spaghetti sauce</th>\n",
       "      <th>sugar</th>\n",
       "      <th>toilet paper</th>\n",
       "      <th>tortillas</th>\n",
       "      <th>waffles</th>\n",
       "      <th>yogurt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Product   all- purpose  aluminum foil  bagels  beef  butter  cereals  cheeses  \\\n",
       "Order_id                                                                        \n",
       "1                  3.0            1.0     0.0   1.0     1.0      0.0      0.0   \n",
       "2                  0.0            1.0     0.0   0.0     0.0      1.0      1.0   \n",
       "3                  0.0            0.0     1.0   0.0     0.0      1.0      1.0   \n",
       "4                  1.0            0.0     0.0   0.0     0.0      1.0      0.0   \n",
       "5                  1.0            0.0     0.0   0.0     0.0      0.0      0.0   \n",
       "\n",
       "Product   coffee/tea  dinner rolls  dishwashing liquid/detergent  ...  \\\n",
       "Order_id                                                          ...   \n",
       "1                0.0           2.0                           0.0  ...   \n",
       "2                0.0           0.0                           1.0  ...   \n",
       "3                0.0           1.0                           0.0  ...   \n",
       "4                0.0           0.0                           0.0  ...   \n",
       "5                0.0           1.0                           0.0  ...   \n",
       "\n",
       "Product   sandwich loaves  shampoo  soap  soda  spaghetti sauce  sugar  \\\n",
       "Order_id                                                                 \n",
       "1                     0.0      1.0   1.0   1.0              0.0    0.0   \n",
       "2                     0.0      2.0   0.0   0.0              0.0    0.0   \n",
       "3                     1.0      1.0   2.0   1.0              3.0    0.0   \n",
       "4                     0.0      0.0   0.0   1.0              0.0    0.0   \n",
       "5                     1.0      0.0   0.0   1.0              1.0    0.0   \n",
       "\n",
       "Product   toilet paper  tortillas  waffles  yogurt  \n",
       "Order_id                                            \n",
       "1                  0.0        0.0      0.0     1.0  \n",
       "2                  1.0        1.0      2.0     1.0  \n",
       "3                  2.0        0.0      0.0     0.0  \n",
       "4                  1.0        0.0      0.0     0.0  \n",
       "5                  1.0        1.0      1.0     1.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0917d03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_zero_one(x):\n",
    "    if x <= 0:\n",
    "        return 0\n",
    "    if x >= 1:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b999d2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "basket = basket.applymap(encode_zero_one)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bcd9a55",
   "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>0.374890</td>\n",
       "      <td>(all- purpose)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.384548</td>\n",
       "      <td>(aluminum foil)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.385426</td>\n",
       "      <td>(bagels)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.374890</td>\n",
       "      <td>(beef)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.367867</td>\n",
       "      <td>(butter)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8449</th>\n",
       "      <td>0.051800</td>\n",
       "      <td>(spaghetti sauce, dinner rolls, ice cream, pou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8450</th>\n",
       "      <td>0.051800</td>\n",
       "      <td>(spaghetti sauce, dinner rolls, juice, poultry)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8451</th>\n",
       "      <td>0.053556</td>\n",
       "      <td>(spaghetti sauce, laundry detergent, dinner ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8452</th>\n",
       "      <td>0.050044</td>\n",
       "      <td>(dishwashing liquid/detergent, laundry deterge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8453</th>\n",
       "      <td>0.055312</td>\n",
       "      <td>(eggs, ice cream, pasta, paper towels)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8454 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       support                                           itemsets\n",
       "0     0.374890                                     (all- purpose)\n",
       "1     0.384548                                    (aluminum foil)\n",
       "2     0.385426                                           (bagels)\n",
       "3     0.374890                                             (beef)\n",
       "4     0.367867                                           (butter)\n",
       "...        ...                                                ...\n",
       "8449  0.051800  (spaghetti sauce, dinner rolls, ice cream, pou...\n",
       "8450  0.051800    (spaghetti sauce, dinner rolls, juice, poultry)\n",
       "8451  0.053556  (spaghetti sauce, laundry detergent, dinner ro...\n",
       "8452  0.050044  (dishwashing liquid/detergent, laundry deterge...\n",
       "8453  0.055312             (eggs, ice cream, pasta, paper towels)\n",
       "\n",
       "[8454 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "itemsets = apriori(basket, min_support = 0.05, use_colnames = True, low_memory=True)\n",
    "itemsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa236013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.barplot(itemsets.sort_values('support',ascending=False).iloc[0:20,1],\n",
    "            itemsets.sort_values('support',ascending=False).iloc[0:20,0])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0bf770",
   "metadata": {},
   "source": [
    "Check the rules using association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0ec9ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "basket = association_rules(itemsets, metric =\"lift\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de1cd3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "basket = basket.sort_values(['lift','confidence'], ascending =[False, False]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df284926",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47953</th>\n",
       "      <td>(pasta, paper towels)</td>\n",
       "      <td>(eggs, ice cream)</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.166813</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.336898</td>\n",
       "      <td>2.019617</td>\n",
       "      <td>0.027924</td>\n",
       "      <td>1.256500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47948</th>\n",
       "      <td>(eggs, ice cream)</td>\n",
       "      <td>(pasta, paper towels)</td>\n",
       "      <td>0.166813</td>\n",
       "      <td>0.164179</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.331579</td>\n",
       "      <td>2.019617</td>\n",
       "      <td>0.027924</td>\n",
       "      <td>1.250441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47951</th>\n",
       "      <td>(ice cream, pasta)</td>\n",
       "      <td>(eggs, paper towels)</td>\n",
       "      <td>0.167691</td>\n",
       "      <td>0.165935</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.329843</td>\n",
       "      <td>1.987784</td>\n",
       "      <td>0.027486</td>\n",
       "      <td>1.244581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47950</th>\n",
       "      <td>(eggs, paper towels)</td>\n",
       "      <td>(ice cream, pasta)</td>\n",
       "      <td>0.165935</td>\n",
       "      <td>0.167691</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.987784</td>\n",
       "      <td>0.027486</td>\n",
       "      <td>1.248464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47854</th>\n",
       "      <td>(dinner rolls, paper towels)</td>\n",
       "      <td>(eggs, pasta)</td>\n",
       "      <td>0.155399</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.967185</td>\n",
       "      <td>0.025468</td>\n",
       "      <td>1.245830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47851</th>\n",
       "      <td>(eggs, pasta)</td>\n",
       "      <td>(dinner rolls, paper towels)</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.155399</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.305699</td>\n",
       "      <td>1.967185</td>\n",
       "      <td>0.025468</td>\n",
       "      <td>1.216477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47908</th>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>(dinner rolls, juice)</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.157155</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.302564</td>\n",
       "      <td>1.925254</td>\n",
       "      <td>0.024894</td>\n",
       "      <td>1.208490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47909</th>\n",
       "      <td>(dinner rolls, juice)</td>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>0.157155</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.329609</td>\n",
       "      <td>1.925254</td>\n",
       "      <td>0.024894</td>\n",
       "      <td>1.236289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47796</th>\n",
       "      <td>(sandwich bags, cereals)</td>\n",
       "      <td>(bagels, cheeses)</td>\n",
       "      <td>0.158033</td>\n",
       "      <td>0.167691</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.322222</td>\n",
       "      <td>1.921524</td>\n",
       "      <td>0.024421</td>\n",
       "      <td>1.227997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47797</th>\n",
       "      <td>(bagels, cheeses)</td>\n",
       "      <td>(sandwich bags, cereals)</td>\n",
       "      <td>0.167691</td>\n",
       "      <td>0.158033</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.303665</td>\n",
       "      <td>1.921524</td>\n",
       "      <td>0.024421</td>\n",
       "      <td>1.209140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47949</th>\n",
       "      <td>(eggs, pasta)</td>\n",
       "      <td>(ice cream, paper towels)</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.170325</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.326425</td>\n",
       "      <td>1.916484</td>\n",
       "      <td>0.026451</td>\n",
       "      <td>1.231748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47952</th>\n",
       "      <td>(ice cream, paper towels)</td>\n",
       "      <td>(eggs, pasta)</td>\n",
       "      <td>0.170325</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.055312</td>\n",
       "      <td>0.324742</td>\n",
       "      <td>1.916484</td>\n",
       "      <td>0.026451</td>\n",
       "      <td>1.229979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47923</th>\n",
       "      <td>(laundry detergent, dinner rolls)</td>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>0.165057</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.053556</td>\n",
       "      <td>0.324468</td>\n",
       "      <td>1.895226</td>\n",
       "      <td>0.025298</td>\n",
       "      <td>1.226881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47922</th>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>(laundry detergent, dinner rolls)</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.165057</td>\n",
       "      <td>0.053556</td>\n",
       "      <td>0.312821</td>\n",
       "      <td>1.895226</td>\n",
       "      <td>0.025298</td>\n",
       "      <td>1.215029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47794</th>\n",
       "      <td>(sandwich bags, bagels)</td>\n",
       "      <td>(cheeses, cereals)</td>\n",
       "      <td>0.154522</td>\n",
       "      <td>0.174715</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.329545</td>\n",
       "      <td>1.886192</td>\n",
       "      <td>0.023925</td>\n",
       "      <td>1.230934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47799</th>\n",
       "      <td>(cheeses, cereals)</td>\n",
       "      <td>(sandwich bags, bagels)</td>\n",
       "      <td>0.174715</td>\n",
       "      <td>0.154522</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.291457</td>\n",
       "      <td>1.886192</td>\n",
       "      <td>0.023925</td>\n",
       "      <td>1.193264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47895</th>\n",
       "      <td>(dinner rolls, ice cream)</td>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>0.161545</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.320652</td>\n",
       "      <td>1.872938</td>\n",
       "      <td>0.024143</td>\n",
       "      <td>1.219989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47894</th>\n",
       "      <td>(spaghetti sauce, poultry)</td>\n",
       "      <td>(dinner rolls, ice cream)</td>\n",
       "      <td>0.171203</td>\n",
       "      <td>0.161545</td>\n",
       "      <td>0.051800</td>\n",
       "      <td>0.302564</td>\n",
       "      <td>1.872938</td>\n",
       "      <td>0.024143</td>\n",
       "      <td>1.202196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47798</th>\n",
       "      <td>(bagels, cereals)</td>\n",
       "      <td>(sandwich bags, cheeses)</td>\n",
       "      <td>0.162423</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.313514</td>\n",
       "      <td>1.850217</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>1.209861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47795</th>\n",
       "      <td>(sandwich bags, cheeses)</td>\n",
       "      <td>(bagels, cereals)</td>\n",
       "      <td>0.169447</td>\n",
       "      <td>0.162423</td>\n",
       "      <td>0.050922</td>\n",
       "      <td>0.300518</td>\n",
       "      <td>1.850217</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>1.197425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             antecedents                        consequents  \\\n",
       "47953              (pasta, paper towels)                  (eggs, ice cream)   \n",
       "47948                  (eggs, ice cream)              (pasta, paper towels)   \n",
       "47951                 (ice cream, pasta)               (eggs, paper towels)   \n",
       "47950               (eggs, paper towels)                 (ice cream, pasta)   \n",
       "47854       (dinner rolls, paper towels)                      (eggs, pasta)   \n",
       "47851                      (eggs, pasta)       (dinner rolls, paper towels)   \n",
       "47908         (spaghetti sauce, poultry)              (dinner rolls, juice)   \n",
       "47909              (dinner rolls, juice)         (spaghetti sauce, poultry)   \n",
       "47796           (sandwich bags, cereals)                  (bagels, cheeses)   \n",
       "47797                  (bagels, cheeses)           (sandwich bags, cereals)   \n",
       "47949                      (eggs, pasta)          (ice cream, paper towels)   \n",
       "47952          (ice cream, paper towels)                      (eggs, pasta)   \n",
       "47923  (laundry detergent, dinner rolls)         (spaghetti sauce, poultry)   \n",
       "47922         (spaghetti sauce, poultry)  (laundry detergent, dinner rolls)   \n",
       "47794            (sandwich bags, bagels)                 (cheeses, cereals)   \n",
       "47799                 (cheeses, cereals)            (sandwich bags, bagels)   \n",
       "47895          (dinner rolls, ice cream)         (spaghetti sauce, poultry)   \n",
       "47894         (spaghetti sauce, poultry)          (dinner rolls, ice cream)   \n",
       "47798                  (bagels, cereals)           (sandwich bags, cheeses)   \n",
       "47795           (sandwich bags, cheeses)                  (bagels, cereals)   \n",
       "\n",
       "       antecedent support  consequent support   support  confidence      lift  \\\n",
       "47953            0.164179            0.166813  0.055312    0.336898  2.019617   \n",
       "47948            0.166813            0.164179  0.055312    0.331579  2.019617   \n",
       "47951            0.167691            0.165935  0.055312    0.329843  1.987784   \n",
       "47950            0.165935            0.167691  0.055312    0.333333  1.987784   \n",
       "47854            0.155399            0.169447  0.051800    0.333333  1.967185   \n",
       "47851            0.169447            0.155399  0.051800    0.305699  1.967185   \n",
       "47908            0.171203            0.157155  0.051800    0.302564  1.925254   \n",
       "47909            0.157155            0.171203  0.051800    0.329609  1.925254   \n",
       "47796            0.158033            0.167691  0.050922    0.322222  1.921524   \n",
       "47797            0.167691            0.158033  0.050922    0.303665  1.921524   \n",
       "47949            0.169447            0.170325  0.055312    0.326425  1.916484   \n",
       "47952            0.170325            0.169447  0.055312    0.324742  1.916484   \n",
       "47923            0.165057            0.171203  0.053556    0.324468  1.895226   \n",
       "47922            0.171203            0.165057  0.053556    0.312821  1.895226   \n",
       "47794            0.154522            0.174715  0.050922    0.329545  1.886192   \n",
       "47799            0.174715            0.154522  0.050922    0.291457  1.886192   \n",
       "47895            0.161545            0.171203  0.051800    0.320652  1.872938   \n",
       "47894            0.171203            0.161545  0.051800    0.302564  1.872938   \n",
       "47798            0.162423            0.169447  0.050922    0.313514  1.850217   \n",
       "47795            0.169447            0.162423  0.050922    0.300518  1.850217   \n",
       "\n",
       "       leverage  conviction  \n",
       "47953  0.027924    1.256500  \n",
       "47948  0.027924    1.250441  \n",
       "47951  0.027486    1.244581  \n",
       "47950  0.027486    1.248464  \n",
       "47854  0.025468    1.245830  \n",
       "47851  0.025468    1.216477  \n",
       "47908  0.024894    1.208490  \n",
       "47909  0.024894    1.236289  \n",
       "47796  0.024421    1.227997  \n",
       "47797  0.024421    1.209140  \n",
       "47949  0.026451    1.231748  \n",
       "47952  0.026451    1.229979  \n",
       "47923  0.025298    1.226881  \n",
       "47922  0.025298    1.215029  \n",
       "47794  0.023925    1.230934  \n",
       "47799  0.023925    1.193264  \n",
       "47895  0.024143    1.219989  \n",
       "47894  0.024143    1.202196  \n",
       "47798  0.023400    1.209861  \n",
       "47795  0.023400    1.197425  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket.head(20)"
   ]
  }
 ],
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