market-basket-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Install environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from mlxtend.frequent_patterns import association_rules, apriori # for mining frequent itemsets and association rule\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Item</th>\n",
       "      <th>date_time</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>30-10-2016 09:58</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>30-10-2016 10:05</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>30-10-2016 10:05</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>30-10-2016 10:07</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>30-10-2016 10:07</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item         date_time period_day weekday_weekend\n",
       "0            1          Bread  30-10-2016 09:58    morning         weekend\n",
       "1            2   Scandinavian  30-10-2016 10:05    morning         weekend\n",
       "2            2   Scandinavian  30-10-2016 10:05    morning         weekend\n",
       "3            3  Hot chocolate  30-10-2016 10:07    morning         weekend\n",
       "4            3            Jam  30-10-2016 10:07    morning         weekend"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data and get overview\n",
    "df = pd.read_csv(\"bread basket.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20507 entries, 0 to 20506\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Transaction      20507 non-null  int64 \n",
      " 1   Item             20507 non-null  object\n",
      " 2   date_time        20507 non-null  object\n",
      " 3   period_day       20507 non-null  object\n",
      " 4   weekday_weekend  20507 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 801.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of unique transactions is  9465\n"
     ]
    }
   ],
   "source": [
    "# counting the number of unique transactions\n",
    "print('The total number of unique transactions is ', df['Transaction'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of selling items is  94\n"
     ]
    }
   ],
   "source": [
    "# counting the number of selling items\n",
    "print('The total number of selling items is ', df['Item'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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</th>\n",
       "      <th>Item</th>\n",
       "      <th>date_time</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-10-30 09:58:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30 10:05:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>2016-10-30 10:05:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>2016-10-30 10:07:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>2016-10-30 10:07:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Transaction           Item           date_time period_day weekday_weekend\n",
       "0            1          Bread 2016-10-30 09:58:00    morning         weekend\n",
       "1            2   Scandinavian 2016-10-30 10:05:00    morning         weekend\n",
       "2            2   Scandinavian 2016-10-30 10:05:00    morning         weekend\n",
       "3            3  Hot chocolate 2016-10-30 10:07:00    morning         weekend\n",
       "4            3            Jam 2016-10-30 10:07:00    morning         weekend"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert date_time column into the right format for easier extracting\n",
    "df['date_time'] = pd.to_datetime(df['date_time'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bread</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Scandinavian</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hot chocolate</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Jam</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item period_day weekday_weekend        date  hour  \\\n",
       "0            1          Bread    morning         weekend  2016-10-30     9   \n",
       "1            2   Scandinavian    morning         weekend  2016-10-30    10   \n",
       "2            2   Scandinavian    morning         weekend  2016-10-30    10   \n",
       "3            3  Hot chocolate    morning         weekend  2016-10-30    10   \n",
       "4            3            Jam    morning         weekend  2016-10-30    10   \n",
       "\n",
       "     month weekday  \n",
       "0  2016-10  Sunday  \n",
       "1  2016-10  Sunday  \n",
       "2  2016-10  Sunday  \n",
       "3  2016-10  Sunday  \n",
       "4  2016-10  Sunday  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting date\n",
    "df['date'] = df['date_time'].dt.date\n",
    "\n",
    "# extracting hour\n",
    "df['hour'] = df['date_time'].dt.hour\n",
    "\n",
    "# extracting month and display full name of the month\n",
    "df['month'] = df['date_time'].dt.strftime('%Y-%m')\n",
    "\n",
    "# extracting weekday and display full name of the weekday\n",
    "df['weekday'] = df['date_time'].dt.strftime('%A')\n",
    "\n",
    "# dropping date_time column as it's not necessary anymore\n",
    "df.drop('date_time', axis = 1, inplace = True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bread</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>9</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>scandinavian</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>scandinavian</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>hot chocolate</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>jam</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item period_day weekday_weekend        date  hour  \\\n",
       "0            1          bread    morning         weekend  2016-10-30     9   \n",
       "1            2   scandinavian    morning         weekend  2016-10-30    10   \n",
       "2            2   scandinavian    morning         weekend  2016-10-30    10   \n",
       "3            3  hot chocolate    morning         weekend  2016-10-30    10   \n",
       "4            3            jam    morning         weekend  2016-10-30    10   \n",
       "\n",
       "     month weekday  \n",
       "0  2016-10  Sunday  \n",
       "1  2016-10  Sunday  \n",
       "2  2016-10  Sunday  \n",
       "3  2016-10  Sunday  \n",
       "4  2016-10  Sunday  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the item name to lowercase and remove any spaces\n",
    "df['Item'] = df['Item'].str.strip()\n",
    "df['Item'] = df['Item'].str.lower()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration and Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best selling items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>item</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>coffee</td>\n",
       "      <td>5471</td>\n",
       "      <td>0.266787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bread</td>\n",
       "      <td>3325</td>\n",
       "      <td>0.162140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tea</td>\n",
       "      <td>1435</td>\n",
       "      <td>0.069976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cake</td>\n",
       "      <td>1025</td>\n",
       "      <td>0.049983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pastry</td>\n",
       "      <td>856</td>\n",
       "      <td>0.041742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item  count  percentage\n",
       "0  coffee   5471    0.266787\n",
       "1   bread   3325    0.162140\n",
       "2     tea   1435    0.069976\n",
       "3    cake   1025    0.049983\n",
       "4  pastry    856    0.041742"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of items sold in descending order, take Top 20 best selling items\n",
    "top_items = pd.DataFrame(df['Item'].value_counts(dropna=True, sort=True)).reset_index()\n",
    "top_items.columns = ['item', 'count']\n",
    "top_items['percentage'] = top_items['count'].apply(lambda x: x/top_items['count'].sum())\n",
    "top_items = top_items.head(20)\n",
    "top_items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create bar plot showing Top 20 best selling items\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x = 'item', y = 'count', data = top_items, palette = 'viridis')\n",
    "plt.xlabel('Items', size = 12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Count of Items', size = 12)\n",
    "plt.title('Top 20 best selling items', size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of items per transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3631</td>\n",
       "      <td>0.383624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2908</td>\n",
       "      <td>0.307237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1528</td>\n",
       "      <td>0.161437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>0.089805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>341</td>\n",
       "      <td>0.036027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>135</td>\n",
       "      <td>0.014263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>38</td>\n",
       "      <td>0.004015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21</td>\n",
       "      <td>0.002219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>0.000740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Transaction  percentage\n",
       "Item                         \n",
       "1            3631    0.383624\n",
       "2            2908    0.307237\n",
       "3            1528    0.161437\n",
       "4             850    0.089805\n",
       "5             341    0.036027\n",
       "6             135    0.014263\n",
       "7              38    0.004015\n",
       "8              21    0.002219\n",
       "9               7    0.000740\n",
       "10              2    0.000211\n",
       "11              4    0.000423"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create summary table of number of transactions by their number of items\n",
    "# count the number of items of each transaction\n",
    "items_num = df.groupby('Transaction', as_index=False)['Item'].count()\n",
    "# pivot table to display the distribution of transactions by the number of items\n",
    "items_num = items_num.pivot_table(index='Item', values='Transaction', aggfunc='count')\n",
    "# calculate percentage\n",
    "items_num['percentage'] = items_num['Transaction'].apply(lambda x: x/items_num['Transaction'].sum())\n",
    "items_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create histogram plot showing the distribution of transactions by the number of items per transaction\n",
    "ax = sns.histplot(data= items_num, x = 'Item', discrete=True)\n",
    "plt.xlabel('Number of Items', size = 12)\n",
    "ax.set(xticks=items_num['Item'].values)\n",
    "plt.ylabel('Count of Transactions', size = 12)\n",
    "plt.title('Number of Items per Transaction', size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantity Sold by Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>month</th>\n",
       "      <th>Transaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  Transaction\n",
       "0  2016-01          233\n",
       "1  2016-02          268\n",
       "2  2016-03          380\n",
       "3  2016-04          308\n",
       "4  2016-05          392"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the quantity of items sold by month and year\n",
    "qty_month = df.groupby('month', as_index=False)['Transaction'].count()\n",
    "qty_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create bar plot showing the number of items sold by month\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(x = 'month', y = 'Transaction', data = qty_month, palette = 'viridis')\n",
    "plt.xlabel('Month', size = 12)\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Count of Items', size = 12)\n",
    "plt.title('Quantity Sold by Month', size = 15)\n",
    "plt.grid(axis = 'y', ls='--', lw='0.5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that data is recorded from 2016-01-11 to 2017-12-03, so that the value of 2016-01 bar contains items sold of 20 days, 2017-12 bar contains items sold for 3 days only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantity Sold by Days of Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>weekday</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>wdkey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>3035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2645</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2288</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>2601</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>3266</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>3554</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>3118</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weekday  Transaction  wdkey\n",
       "1     Monday         3035      0\n",
       "5    Tuesday         2645      1\n",
       "6  Wednesday         2288      2\n",
       "4   Thursday         2601      3\n",
       "0     Friday         3266      4\n",
       "2   Saturday         3554      5\n",
       "3     Sunday         3118      6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of transactions by days of week, reorder by names of the days\n",
    "qty_wd = df.groupby('weekday', as_index=False)['Transaction'].count()\n",
    "qty_wd['wdkey'] = [4,0,5,6,3,1,2]\n",
    "qty_wd.sort_values(\"wdkey\",inplace=True)\n",
    "qty_wd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create bar plot showing the number of items sold by days of week\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.barplot(x = 'weekday', y = 'Transaction', data = qty_wd, palette = 'viridis')\n",
    "plt.xlabel('Day of Week', size = 12)\n",
    "plt.ylabel('Count of Items', size = 12)\n",
    "plt.title('Quantity Sold by Days of Week', size = 15)\n",
    "plt.grid(axis = 'y', ls='--', lw='0.5')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantity Sold by Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>hour</th>\n",
       "      <th>Transaction</th>\n",
       "      <th>hour_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 1]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>(6, 7]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>645</td>\n",
       "      <td>(7, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1966</td>\n",
       "      <td>(8, 9]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>2666</td>\n",
       "      <td>(9, 10]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  Transaction hour_bins\n",
       "0     1            1    (0, 1]\n",
       "1     7           24    (6, 7]\n",
       "2     8          645    (7, 8]\n",
       "3     9         1966    (8, 9]\n",
       "4    10         2666   (9, 10]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of items sold in different hours of a day\n",
    "# create hour bins to show time period\n",
    "qty_hr = df.groupby('hour', as_index=False)['Transaction'].count()\n",
    "qty_hr['hour_bins'] = pd.cut(x=trans_hr.hour, bins = range(0,24,1))\n",
    "qty_hr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the distribution of items sold quantity by hours\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.barplot(x = 'Transaction', y = 'hour_bins', data = qty_hr, palette = 'viridis')\n",
    "plt.xlabel('Count of Items', size = 12)\n",
    "plt.ylabel('Hours', size = 12)\n",
    "plt.title('Quantity Sold by Hours', size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply Apriori Algorithm to implement Market Basket Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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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       "        text-align: right;\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>Transaction</th>\n",
       "      <th>Item</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>bread</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>scandinavian</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>cookies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>hot chocolate</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>jam</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item  Count\n",
       "0            1          bread      1\n",
       "1            2   scandinavian      2\n",
       "2            3        cookies      1\n",
       "3            3  hot chocolate      1\n",
       "4            3            jam      1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only required variables for modelling\n",
    "transactions = df.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `apriori` function expects input data in a one-hot encoded pandas DataFrame, therefore, we need to transform the dataframe above into the representation of categorical variables as binary vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Item</th>\n",
       "      <th>adjustment</th>\n",
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       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Item        adjustment afternoon with the baker alfajores argentina night  \\\n",
       "Transaction                                                                 \n",
       "1                    0                        0         0               0   \n",
       "2                    0                        0         0               0   \n",
       "3                    0                        0         0               0   \n",
       "4                    0                        0         0               0   \n",
       "5                    0                        0         0               0   \n",
       "\n",
       "Item        art tray bacon baguette bakewell bare popcorn basket  ...  \\\n",
       "Transaction                                                       ...   \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",
       "5                  0     0        0        0            0      0  ...   \n",
       "\n",
       "Item        the bart the nomad tiffin toast truffles tshirt valentine's card  \\\n",
       "Transaction                                                                    \n",
       "1                  0         0      0     0        0      0                0   \n",
       "2                  0         0      0     0        0      0                0   \n",
       "3                  0         0      0     0        0      0                0   \n",
       "4                  0         0      0     0        0      0                0   \n",
       "5                  0         0      0     0        0      0                0   \n",
       "\n",
       "Item        vegan feast vegan mincepie victorian sponge  \n",
       "Transaction                                              \n",
       "1                     0              0                0  \n",
       "2                     0              0                0  \n",
       "3                     0              0                0  \n",
       "4                     0              0                0  \n",
       "5                     0              0                0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first create a mxn matrice where m=transaction and n=items\n",
    "# each row represents whether the items was in a specific transaction or not (>=1 returns True (1), 0 returns 0)\n",
    "my_basket = transactions.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='any').fillna(0)\n",
    "\n",
    "my_basket.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimum support is the minimum frequency or occurrence threshold that an itemset must meet to be considered frequent. \n",
    "\n",
    "Let's say an itemset is considered frequent if it appears at least 1% in total number of transactions (1% of 9465). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.036344</td>\n",
       "      <td>(alfajores)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016059</td>\n",
       "      <td>(baguette)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.327205</td>\n",
       "      <td>(bread)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.040042</td>\n",
       "      <td>(brownie)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.103856</td>\n",
       "      <td>(cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.023666</td>\n",
       "      <td>(coffee, toast)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.014369</td>\n",
       "      <td>(sandwich, tea)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(coffee, bread, cake)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.011199</td>\n",
       "      <td>(coffee, bread, pastry)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(coffee, cake, tea)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     support                 itemsets\n",
       "0   0.036344              (alfajores)\n",
       "1   0.016059               (baguette)\n",
       "2   0.327205                  (bread)\n",
       "3   0.040042                (brownie)\n",
       "4   0.103856                   (cake)\n",
       "..       ...                      ...\n",
       "56  0.023666          (coffee, toast)\n",
       "57  0.014369          (sandwich, tea)\n",
       "58  0.010037    (coffee, bread, cake)\n",
       "59  0.011199  (coffee, bread, pastry)\n",
       "60  0.010037      (coffee, cake, tea)\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create frequent items df with itemsets and support columns by using `apriori` function\n",
    "frequent_items = apriori(my_basket, min_support = 0.01, use_colnames = True)\n",
    "frequent_items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just because an item or itemset is common does not mean that there is a strong association between them. For example, the item \"coffee\" may have a high support value, but that does not mean there is a strong association between coffee and any other particular item. Therefore, we should consider lift metric when assessing the associations between items.\n",
    "\n",
    "Lift indicates how many more times A and B actually appear in the same order, compared to if there was no relationship between them. In other words, it takes into account the support of each item to measure the strength of the association between them. A lift value of 1 means that there is no association between the two items. A lift value greater than 1 means that the two items are positively associated, meaning that the presence of one item increases the likelihood of the presence of the other item.\n",
    "\n",
    "This project will set the minimum threshold for lift metric to 1.2 to return the association rules that occur at least 20% more often than we would expect if they were random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(toast)</td>\n",
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       "      <td>0.478394</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.704403</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.764582</td>\n",
       "      <td>0.332006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(spanish brunch)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.598837</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.300235</td>\n",
       "      <td>0.204851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.228891</td>\n",
       "      <td>1.604781</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>1.111865</td>\n",
       "      <td>0.420538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(coffee, tea)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.201271</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>1.121962</td>\n",
       "      <td>0.509401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>(tea)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.402222</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>1.071712</td>\n",
       "      <td>0.309050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.195652</td>\n",
       "      <td>1.883874</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>1.114125</td>\n",
       "      <td>0.498236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(coffee, cake)</td>\n",
       "      <td>(tea)</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.183398</td>\n",
       "      <td>1.285822</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>1.049923</td>\n",
       "      <td>0.235157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(tea)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.023772</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.604781</td>\n",
       "      <td>0.008959</td>\n",
       "      <td>1.075372</td>\n",
       "      <td>0.439556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(coffee, bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.130061</td>\n",
       "      <td>1.444872</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>1.046033</td>\n",
       "      <td>0.336907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(coffee, bread)</td>\n",
       "      <td>(pastry)</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.011199</td>\n",
       "      <td>0.124413</td>\n",
       "      <td>1.444872</td>\n",
       "      <td>0.003448</td>\n",
       "      <td>1.043749</td>\n",
       "      <td>0.338354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.011410</td>\n",
       "      <td>0.109868</td>\n",
       "      <td>1.883874</td>\n",
       "      <td>0.005354</td>\n",
       "      <td>1.057910</td>\n",
       "      <td>0.523553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(tea)</td>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.100741</td>\n",
       "      <td>1.402222</td>\n",
       "      <td>0.004122</td>\n",
       "      <td>1.032134</td>\n",
       "      <td>0.334566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(coffee, tea)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.937977</td>\n",
       "      <td>0.004858</td>\n",
       "      <td>1.051779</td>\n",
       "      <td>0.540090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(tea)</td>\n",
       "      <td>(coffee, cake)</td>\n",
       "      <td>0.142631</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.070370</td>\n",
       "      <td>1.285822</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>1.016827</td>\n",
       "      <td>0.259266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(toast)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.033597</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.049470</td>\n",
       "      <td>1.472431</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>1.016699</td>\n",
       "      <td>0.615122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(spanish brunch)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018172</td>\n",
       "      <td>0.010882</td>\n",
       "      <td>0.022747</td>\n",
       "      <td>1.251766</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>1.004682</td>\n",
       "      <td>0.385594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "0            (toast)          (coffee)            0.033597   \n",
       "1   (spanish brunch)          (coffee)            0.018172   \n",
       "2             (cake)             (tea)            0.103856   \n",
       "3      (coffee, tea)            (cake)            0.049868   \n",
       "4         (sandwich)             (tea)            0.071844   \n",
       "5    (hot chocolate)            (cake)            0.058320   \n",
       "6     (coffee, cake)             (tea)            0.054728   \n",
       "7              (tea)            (cake)            0.142631   \n",
       "8           (pastry)   (coffee, bread)            0.086107   \n",
       "9    (coffee, bread)          (pastry)            0.090016   \n",
       "10            (cake)   (hot chocolate)            0.103856   \n",
       "11             (tea)        (sandwich)            0.142631   \n",
       "12            (cake)     (coffee, tea)            0.103856   \n",
       "13             (tea)    (coffee, cake)            0.142631   \n",
       "14          (coffee)           (toast)            0.478394   \n",
       "15          (coffee)  (spanish brunch)            0.478394   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \\\n",
       "0             0.478394  0.023666    0.704403  1.472431  0.007593    1.764582   \n",
       "1             0.478394  0.010882    0.598837  1.251766  0.002189    1.300235   \n",
       "2             0.142631  0.023772    0.228891  1.604781  0.008959    1.111865   \n",
       "3             0.103856  0.010037    0.201271  1.937977  0.004858    1.121962   \n",
       "4             0.142631  0.014369    0.200000  1.402222  0.004122    1.071712   \n",
       "5             0.103856  0.011410    0.195652  1.883874  0.005354    1.114125   \n",
       "6             0.142631  0.010037    0.183398  1.285822  0.002231    1.049923   \n",
       "7             0.103856  0.023772    0.166667  1.604781  0.008959    1.075372   \n",
       "8             0.090016  0.011199    0.130061  1.444872  0.003448    1.046033   \n",
       "9             0.086107  0.011199    0.124413  1.444872  0.003448    1.043749   \n",
       "10            0.058320  0.011410    0.109868  1.883874  0.005354    1.057910   \n",
       "11            0.071844  0.014369    0.100741  1.402222  0.004122    1.032134   \n",
       "12            0.049868  0.010037    0.096643  1.937977  0.004858    1.051779   \n",
       "13            0.054728  0.010037    0.070370  1.285822  0.002231    1.016827   \n",
       "14            0.033597  0.023666    0.049470  1.472431  0.007593    1.016699   \n",
       "15            0.018172  0.010882    0.022747  1.251766  0.002189    1.004682   \n",
       "\n",
       "    zhangs_metric  \n",
       "0        0.332006  \n",
       "1        0.204851  \n",
       "2        0.420538  \n",
       "3        0.509401  \n",
       "4        0.309050  \n",
       "5        0.498236  \n",
       "6        0.235157  \n",
       "7        0.439556  \n",
       "8        0.336907  \n",
       "9        0.338354  \n",
       "10       0.523553  \n",
       "11       0.334566  \n",
       "12       0.540090  \n",
       "13       0.259266  \n",
       "14       0.615122  \n",
       "15       0.385594  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the rules from frequent itemset generated above with min lift = 1.2\n",
    "rules = association_rules(frequent_items, metric = \"lift\", min_threshold = 1.2)\n",
    "rules.sort_values('confidence', ascending = False, inplace = True)\n",
    "rules.reset_index(drop=True, inplace = True)\n",
    "rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel coordinates plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to convert rules to coordinates.\n",
    "def rules_to_coordinates(rules):\n",
    "    rules['antecedent'] = rules['antecedents'].apply(lambda antecedent: list(antecedent)[0])\n",
    "    rules['consequent'] = rules['consequents'].apply(lambda consequent: list(consequent)[0])\n",
    "    rules['rule'] = rules.index\n",
    "    return rules[['antecedent','consequent','rule']]\n",
    "\n",
    "# import sub lib to plot parallel coordinates\n",
    "from pandas.plotting import parallel_coordinates\n",
    "\n",
    "# Convert rules into coordinates suitable for use in a parallel coordinates plot\n",
    "coords = rules_to_coordinates(rules)\n",
    "\n",
    "# Generate parallel coordinates plot\n",
    "plt.figure(figsize=(5,5))\n",
    "parallel_coordinates(coords, 'rule')\n",
    "plt.legend([])\n",
    "plt.grid(ls='--', lw='0.5')\n",
    "plt.title('Parallel coordinates', size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Itemsets Lift Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert antecedents and consequents into strings\n",
    "rules['antecedents'] = rules['antecedents'].apply(lambda a: ','.join(list(a)))\n",
    "rules['consequents'] = rules['consequents'].apply(lambda a: ','.join(list(a)))\n",
    "\n",
    "# transform antecedent, consequent, and support columns into matrix\n",
    "support_table_lift = rules.pivot(index='consequents', columns='antecedents', values='lift')\n",
    "\n",
    "# generate a heatmap with annotations \n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(support_table_lift, annot = True, cbar = True, cmap=\"RdPu\")\n",
    "plt.suptitle('Itemsets Lift', size = 15)\n",
    "plt.title('How many times the antecedents and the consequents occur together more often than random?\\n', size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Itemsets Confidence Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# transform antecedent, consequent, and support columns into matrix\n",
    "rules_confidence = rules[rules['confidence']>=0.2] # select min lift=1.2\n",
    "support_table_conf = rules_confidence.pivot(index='consequents', columns='antecedents', values='confidence')\n",
    "\n",
    "# generate a heatmap with annotations\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(support_table_conf, annot = True, cbar = True, cmap=\"BuPu\")\n",
    "plt.suptitle('Itemsets Confidence', size = 15)\n",
    "plt.title('How often the consequents is purchased given that the antecedents was purchased?\\n', size = 10)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}