apriori-algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:48.483746Z",
     "iopub.status.busy": "2020-11-25T19:29:48.482753Z",
     "iopub.status.idle": "2020-11-25T19:29:48.490377Z",
     "shell.execute_reply": "2020-11-25T19:29:48.489580Z"
    },
    "papermill": {
     "duration": 0.052489,
     "end_time": "2020-11-25T19:29:48.490537",
     "exception": false,
     "start_time": "2020-11-25T19:29:48.438048",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/the-bread-basket/bread basket.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028642,
     "end_time": "2020-11-25T19:29:48.550508",
     "exception": false,
     "start_time": "2020-11-25T19:29:48.521866",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reading and Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:48.613381Z",
     "iopub.status.busy": "2020-11-25T19:29:48.612514Z",
     "iopub.status.idle": "2020-11-25T19:29:49.824871Z",
     "shell.execute_reply": "2020-11-25T19:29:49.823856Z"
    },
    "papermill": {
     "duration": 1.247339,
     "end_time": "2020-11-25T19:29:49.825060",
     "exception": false,
     "start_time": "2020-11-25T19:29:48.577721",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:49.894470Z",
     "iopub.status.busy": "2020-11-25T19:29:49.893646Z",
     "iopub.status.idle": "2020-11-25T19:29:49.941617Z",
     "shell.execute_reply": "2020-11-25T19:29:49.940762Z"
    },
    "papermill": {
     "duration": 0.085153,
     "end_time": "2020-11-25T19:29:49.941759",
     "exception": false,
     "start_time": "2020-11-25T19:29:49.856606",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"/kaggle/input/the-bread-basket/bread basket.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:50.027335Z",
     "iopub.status.busy": "2020-11-25T19:29:50.026035Z",
     "iopub.status.idle": "2020-11-25T19:29:50.038323Z",
     "shell.execute_reply": "2020-11-25T19:29:50.038977Z"
    },
    "papermill": {
     "duration": 0.067132,
     "end_time": "2020-11-25T19:29:50.039161",
     "exception": false,
     "start_time": "2020-11-25T19:29:49.972029",
     "status": "completed"
    },
    "tags": []
   },
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:50.112875Z",
     "iopub.status.busy": "2020-11-25T19:29:50.111561Z",
     "iopub.status.idle": "2020-11-25T19:29:50.129220Z",
     "shell.execute_reply": "2020-11-25T19:29:50.128396Z"
    },
    "papermill": {
     "duration": 0.057995,
     "end_time": "2020-11-25T19:29:50.129349",
     "exception": false,
     "start_time": "2020-11-25T19:29:50.071354",
     "status": "completed"
    },
    "tags": []
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20507.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4976.202370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2796.203001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2552.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7357.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9684.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Transaction\n",
       "count  20507.000000\n",
       "mean    4976.202370\n",
       "std     2796.203001\n",
       "min        1.000000\n",
       "25%     2552.000000\n",
       "50%     5137.000000\n",
       "75%     7357.000000\n",
       "max     9684.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:50.203460Z",
     "iopub.status.busy": "2020-11-25T19:29:50.202240Z",
     "iopub.status.idle": "2020-11-25T19:29:50.215892Z",
     "shell.execute_reply": "2020-11-25T19:29:50.216879Z"
    },
    "papermill": {
     "duration": 0.058721,
     "end_time": "2020-11-25T19:29:50.217082",
     "exception": false,
     "start_time": "2020-11-25T19:29:50.158361",
     "status": "completed"
    },
    "tags": []
   },
   "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": {
    "papermill": {
     "duration": 0.028917,
     "end_time": "2020-11-25T19:29:50.275825",
     "exception": false,
     "start_time": "2020-11-25T19:29:50.246908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There is no missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:50.342080Z",
     "iopub.status.busy": "2020-11-25T19:29:50.341311Z",
     "iopub.status.idle": "2020-11-25T19:29:51.524702Z",
     "shell.execute_reply": "2020-11-25T19:29:51.523871Z"
    },
    "papermill": {
     "duration": 1.219802,
     "end_time": "2020-11-25T19:29:51.524831",
     "exception": false,
     "start_time": "2020-11-25T19:29:50.305029",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Converting the 'date_time' column into the right format\n",
    "df['date_time'] = pd.to_datetime(df['date_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:51.599270Z",
     "iopub.status.busy": "2020-11-25T19:29:51.598173Z",
     "iopub.status.idle": "2020-11-25T19:29:51.603316Z",
     "shell.execute_reply": "2020-11-25T19:29:51.602751Z"
    },
    "papermill": {
     "duration": 0.048798,
     "end_time": "2020-11-25T19:29:51.603458",
     "exception": false,
     "start_time": "2020-11-25T19:29:51.554660",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Cookies</td>\n",
       "      <td>2016-10-30 10:07:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>Muffin</td>\n",
       "      <td>2016-10-30 10:08:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>Coffee</td>\n",
       "      <td>2016-10-30 10:13:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Pastry</td>\n",
       "      <td>2016-10-30 10:13:00</td>\n",
       "      <td>morning</td>\n",
       "      <td>weekend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>Bread</td>\n",
       "      <td>2016-10-30 10:13:00</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 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\n",
       "5            3        Cookies 2016-10-30 10:07:00    morning         weekend\n",
       "6            4         Muffin 2016-10-30 10:08:00    morning         weekend\n",
       "7            5         Coffee 2016-10-30 10:13:00    morning         weekend\n",
       "8            5         Pastry 2016-10-30 10:13:00    morning         weekend\n",
       "9            5          Bread 2016-10-30 10:13:00    morning         weekend"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:51.670370Z",
     "iopub.status.busy": "2020-11-25T19:29:51.669387Z",
     "iopub.status.idle": "2020-11-25T19:29:51.674991Z",
     "shell.execute_reply": "2020-11-25T19:29:51.674350Z"
    },
    "papermill": {
     "duration": 0.0408,
     "end_time": "2020-11-25T19:29:51.675116",
     "exception": false,
     "start_time": "2020-11-25T19:29:51.634316",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9465"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of unique customers\n",
    "df['Transaction'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:51.783244Z",
     "iopub.status.busy": "2020-11-25T19:29:51.750907Z",
     "iopub.status.idle": "2020-11-25T19:29:51.827031Z",
     "shell.execute_reply": "2020-11-25T19:29:51.826355Z"
    },
    "papermill": {
     "duration": 0.121296,
     "end_time": "2020-11-25T19:29:51.827155",
     "exception": false,
     "start_time": "2020-11-25T19:29:51.705859",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Extracting date\n",
    "df['date'] = df['date_time'].dt.date\n",
    "\n",
    "#Extracting time\n",
    "df['time'] = df['date_time'].dt.time\n",
    "\n",
    "# Extracting month and replacing it with text\n",
    "df['month'] = df['date_time'].dt.month\n",
    "df['month'] = df['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), \n",
    "                                          ('January','February','March','April','May','June','July','August',\n",
    "                                          'September','October','November','December'))\n",
    "\n",
    "# Extracting hour\n",
    "df['hour'] = df['date_time'].dt.hour\n",
    "# Replacing hours with text\n",
    "hour_in_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)\n",
    "hour_in_obj = ('1-2','7-8','8-9','9-10','10-11','11-12','12-13','13-14','14-15',\n",
    "               '15-16','16-17','17-18','18-19','19-20','20-21','21-22','22-23','23-24')\n",
    "df['hour'] = df['hour'].replace(hour_in_num, hour_in_obj)\n",
    "\n",
    "# Extracting weekday and replacing it with text\n",
    "df['weekday'] = df['date_time'].dt.weekday\n",
    "df['weekday'] = df['weekday'].replace((0,1,2,3,4,5,6), \n",
    "                                          ('Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday'))\n",
    "\n",
    "# dropping date_time column\n",
    "df.drop('date_time', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:51.906790Z",
     "iopub.status.busy": "2020-11-25T19:29:51.905906Z",
     "iopub.status.idle": "2020-11-25T19:29:51.911172Z",
     "shell.execute_reply": "2020-11-25T19:29:51.910377Z"
    },
    "papermill": {
     "duration": 0.053019,
     "end_time": "2020-11-25T19:29:51.911299",
     "exception": false,
     "start_time": "2020-11-25T19:29:51.858280",
     "status": "completed"
    },
    "tags": []
   },
   "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>Item</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</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>09:58:00</td>\n",
       "      <td>October</td>\n",
       "      <td>9-10</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>10:05:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:05:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:07:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:07:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item period_day weekday_weekend        date  \\\n",
       "0            1          Bread    morning         weekend  2016-10-30   \n",
       "1            2   Scandinavian    morning         weekend  2016-10-30   \n",
       "2            2   Scandinavian    morning         weekend  2016-10-30   \n",
       "3            3  Hot chocolate    morning         weekend  2016-10-30   \n",
       "4            3            Jam    morning         weekend  2016-10-30   \n",
       "\n",
       "       time    month   hour weekday  \n",
       "0  09:58:00  October   9-10  Sunday  \n",
       "1  10:05:00  October  10-11  Sunday  \n",
       "2  10:05:00  October  10-11  Sunday  \n",
       "3  10:07:00  October  10-11  Sunday  \n",
       "4  10:07:00  October  10-11  Sunday  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:52.000853Z",
     "iopub.status.busy": "2020-11-25T19:29:51.994622Z",
     "iopub.status.idle": "2020-11-25T19:29:52.015207Z",
     "shell.execute_reply": "2020-11-25T19:29:52.014520Z"
    },
    "papermill": {
     "duration": 0.071964,
     "end_time": "2020-11-25T19:29:52.015328",
     "exception": false,
     "start_time": "2020-11-25T19:29:51.943364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# cleaning the item column\n",
    "df['Item'] = df['Item'].str.strip()\n",
    "df['Item'] = df['Item'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:52.098364Z",
     "iopub.status.busy": "2020-11-25T19:29:52.097306Z",
     "iopub.status.idle": "2020-11-25T19:29:52.102586Z",
     "shell.execute_reply": "2020-11-25T19:29:52.101840Z"
    },
    "papermill": {
     "duration": 0.055057,
     "end_time": "2020-11-25T19:29:52.102711",
     "exception": false,
     "start_time": "2020-11-25T19:29:52.047654",
     "status": "completed"
    },
    "tags": []
   },
   "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>Item</th>\n",
       "      <th>period_day</th>\n",
       "      <th>weekday_weekend</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</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>09:58:00</td>\n",
       "      <td>October</td>\n",
       "      <td>9-10</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>10:05:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:05:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:07:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</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>10:07:00</td>\n",
       "      <td>October</td>\n",
       "      <td>10-11</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Transaction           Item period_day weekday_weekend        date  \\\n",
       "0            1          bread    morning         weekend  2016-10-30   \n",
       "1            2   scandinavian    morning         weekend  2016-10-30   \n",
       "2            2   scandinavian    morning         weekend  2016-10-30   \n",
       "3            3  hot chocolate    morning         weekend  2016-10-30   \n",
       "4            3            jam    morning         weekend  2016-10-30   \n",
       "\n",
       "       time    month   hour weekday  \n",
       "0  09:58:00  October   9-10  Sunday  \n",
       "1  10:05:00  October  10-11  Sunday  \n",
       "2  10:05:00  October  10-11  Sunday  \n",
       "3  10:07:00  October  10-11  Sunday  \n",
       "4  10:07:00  October  10-11  Sunday  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032523,
     "end_time": "2020-11-25T19:29:52.168230",
     "exception": false,
     "start_time": "2020-11-25T19:29:52.135707",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:52.243377Z",
     "iopub.status.busy": "2020-11-25T19:29:52.242531Z",
     "iopub.status.idle": "2020-11-25T19:29:52.701020Z",
     "shell.execute_reply": "2020-11-25T19:29:52.701691Z"
    },
    "papermill": {
     "duration": 0.500718,
     "end_time": "2020-11-25T19:29:52.701866",
     "exception": false,
     "start_time": "2020-11-25T19:29:52.201148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x = df.Item.value_counts().head(20).index, y = df.Item.value_counts().head(20).values, palette = 'gnuplot')\n",
    "plt.xlabel('Items', size = 15)\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Count of Items', size = 15)\n",
    "plt.title('Top 20 Items purchased by customers', color = 'green', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038037,
     "end_time": "2020-11-25T19:29:52.778473",
     "exception": false,
     "start_time": "2020-11-25T19:29:52.740436",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Coffee has the highest transactions.\n",
    "\n",
    "Coke is the 20th most buyed product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:52.868746Z",
     "iopub.status.busy": "2020-11-25T19:29:52.867950Z",
     "iopub.status.idle": "2020-11-25T19:29:53.118667Z",
     "shell.execute_reply": "2020-11-25T19:29:53.119212Z"
    },
    "papermill": {
     "duration": 0.302715,
     "end_time": "2020-11-25T19:29:53.119368",
     "exception": false,
     "start_time": "2020-11-25T19:29:52.816653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthTran = df.groupby('month')['Transaction'].count().reset_index()\n",
    "monthTran.loc[:,\"monthorder\"] = [4,8,12,2,1,7,6,3,5,11,10,9]\n",
    "monthTran.sort_values(\"monthorder\",inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(data = monthTran, x = \"month\", y = \"Transaction\")\n",
    "plt.xlabel('Months', size = 15)\n",
    "plt.ylabel('Orders per month', size = 15)\n",
    "plt.title('Number of orders received each month', color = 'green', size = 20)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.035,
     "end_time": "2020-11-25T19:29:53.189870",
     "exception": false,
     "start_time": "2020-11-25T19:29:53.154870",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Most transactions were in **March, January, February, November, December**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:53.273321Z",
     "iopub.status.busy": "2020-11-25T19:29:53.272222Z",
     "iopub.status.idle": "2020-11-25T19:29:53.490137Z",
     "shell.execute_reply": "2020-11-25T19:29:53.491000Z"
    },
    "papermill": {
     "duration": 0.266146,
     "end_time": "2020-11-25T19:29:53.491176",
     "exception": false,
     "start_time": "2020-11-25T19:29:53.225030",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAFXCAYAAAB6ASLsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzdeZgcVbn48e/LIovsEBATEK6CsngNEhFZBNzAFRUVXFhEiSKK/hSV5epk9OKOeNGLioCAoogLgooiBtnX4EV2FCVAWAOIgEiE8P7+OKdN0emZ9CQzPZPJ9/M8/XT1qVNVp09Vd791+tSpyEwkSZIkjbylRrsAkiRJ0pLC4FuSJEnqEYNvSZIkqUcMviVJkqQeMfiWJEmSesTgW5IkSeoRg29JAER/nBv9Ma7GHo3+2Cj647Toj7ujPzL648HRLlNT9McJtVwbjHZZxptar+eOdjmaxmKZuhX9sU8t/z492NZiW09SN5YZ7QJI40kjeL0NeG725WMd8swEngUsm335RA+Lt0SJ/lga+DnwHOB7wCxgvv0hSVIvGXxLI2N94CPAF0a7IEuwDYFNge9kX04d7cKo5zYBHh3tQkhSO7udSMPvb8ADwCHRH2uNdmGWYM+sz3eOaik0KrIvb8y+vG20yyFJ7Wz5lobfo8BXgCOBPuBDC1og+mNH4PdAf/bltA7zZwJkX27QSNsH+C7wbuAO4NPAFsA/gV8C/y/78sHojy2AzwLbAssC5wAHZl/OHKAsywGfAt5JCWBnAScBn8++/FeH/M8DDgZeDqwNPAhMr+/lpra8JwB7A88GXgvsB2wEXJZ9ueOAFTRv+S2BQ4HtgVWBu4FfAZ/Nvryrka/Zd70v+qOvTnes3w7beRvwQeAFwNOAm4EfAF/NvpzTlndmnfxPYBrwZmAicHhrW9Efr6AcCy8E5gDnU+pssDK8GPg4sB2wBnAPcGZ9D3e25T0X2AFYrq73ncAGwA+zL/eJ/nga8H5gH8o/AssB9wJ/BL6effm7LurkBBaw76I/1qhlfmPd/r+AGcAXsy9/O8B6dwemUo7dFSn79BLgiOzLGW15317zTgZWAG4BTga+3GG/JHBeo2zfrsvumn15RodybF23+5Psy7c20lcEPgzsXt9vAtcAR2Vf/rDDep4GfJJS15MoJ38nUz6DQzbE42BLYC9gR2A9Sn3eDpwB/Hf25d8G2EbX+6Dm34lyPG9JqY8LgIOyL28YwvsaUj1FfzwTeC+wM+UYXAO4DziX8vm/oZH3ecANwO+zL182wPquAZ4HrJd9eXe35ZaGgy3f0sj4X+AvwPuiPzYe4W29gRKAzga+BfyZ8oP28xpQXEg50T4OuAh4PfCr6I+BPv+nAvsCvwC+QflxnQb8NPojmhmjP3YB/kAJ9q4A/ocSeL8ZuDz644UDbON/KD+y19Tpixb0JqM/XgdcXMv/O+CrwE3A/sCMtosW+4ET6/R59XU/5Yd6Qdv5HPAjSreFH1DqIIDPAWdFfyzbYbGnUU5q3gj8tr6nW+r63gKcBUwBfgx8G1iTEtxsOEAZ3k2pk1dTTsq+Rgli31vf6/oDFP+nwAco9fQ1Sv0CnFDLtCzlROooygnA84FdBq2Q+XXcd9EfzwKupAT/rWOxVY+/if7Yr+09Rg3oT6GcuPyMcsJ6AeXk6nVt+Y+j7I/n1Lz/S/mH6bN1/QtqTDqhPu89wPy96nPruCH6YzXK5+dzwFzg+Dp/AvCD6I//bn9PlM/PZyifm29QToT3relDshDHwX7AHpTPxXcp++Au4KPARdEfK7eXdyj7oHod5Rh/qK7/AuA1wHnd/tO3kPX0Usqx9SDlOD8SuBR4C3BF9McLWhmzL2+k1NdOnb5/oz+2ATYHTjfw1miw5VsaAdmXj0d/HEwJtr5ACUZHyhuAl2dfngdQg+qzgFdQWsimZl+e3Mpcg5h9KUHs6R3WtwmwWauVLPrjMMoP2euAd1EuXiT6Y3Xgh5SW/pdmX17f2MZmwGXAsZTW3nYvBLbIvrylmzcY/bESJXhaBtgx+/KCxrxPUur4GOBVANmX0+q/CXsD53bT2l3X9RLgEEpr4VatH+boj0OA02odfJwSjDWtC1wP7JB9+Y+2cn8beBLYvtmKGP1xJOW6gPYybFyXmVnXd0dj3suAsylB75s6vIVnAZtnX97XWGZVSkB2JfDi7Mu5bdtbc+Aa6WigfXdi3f7bsy9Paax/NcpJz1HRH2dkX95TZ+1H2T9XAK/Mvvx7Y5mlKf+itF7vQzlmTwPemX35z8a8aZRW2AMo9dJR9uUl0R9/Al4X/bFm9uX9jXUsR6mje4HfNBb7GqU1+JPZl19q5F+ecjHvodEfP8m+vKrOejuwKyUo3Kl1wXX95+WKgcrWyUIeB58HDuiwj99D+Sx+APhiY1bX+6DhjcDO2ZfTG3k/TwmM9wW+1GGZdgtTT+cA62RfPtz23l5AOUH5AuUkpeVoYCdKi/5BbetqXQPy7S7KKg07W76lEZJ9+RNK6+aboj+2G8FN/bAVeNftPkkNkIFrm4F3dVJ9njzA+j7b/Hu6/jAeUl/u28i3F7Aa0NcMvOsy1wHfAbaI/ti0wza+1G3gXe1KaS3+UTPwro6gBCivHKRFuFut9/ffzRaxOirNxyhB9HsHWPZjzcC7Ue41gB90+Pt+GvB35rc/pYX6w82Aq5bjHEoXgte3t2JWn2oG3q3FKC33c2r5nzqzEYR2ab59VwOgHYCfNgPvuv4HKcHx8sBujVmt7ljvawZ9dZm5zW5ElG4fTwD7NgPv6rPA/ZR/XxbkRMq/FHu0pb8eWB04uTUCUT0peRcwoxl41/I9RukyEcA7GrPeXZ8PbY50lH3ZaqEfiiEfB9mXt7YH3tXxlJbqndvSh7IPWk5pBt7VMfV5q8HeUMOQ6yn78t72wLum/5ESmO/U9q/UzyldWfapJ1fAv08G30b5Z3KB3a2kkWDLtzSyPkbpAnBE9MfW2ZcjMY72fH0ymXeR4ZUd5rV+yCcNsL7zOqRdQAl+tmikvaQ+v6C2PrZr/d27CaVVuOnyAbY9kFbr+TntM7Ivn4j+OJ/Sx3gLyjCPC2uw7fwp+mMWsGH0x2o1qGx5DLh6kPXNV6fZl3+P/riKErQ2tep1h+iPF3VY59rA0pT6bd+/89Vr9uVD0R+/oASYV0V//JSyPy/LvlyY0UA67btWmVcd4FiYUJ83AYj+eDrlb/97si//b7CN1T7XL6D07/1I9EenbHNa616AkyjB3d6Ubistra4oJzbSXkSp5xzgPbUCveZ2X0g5wbmwQ/5zuyhf05CPgxp8vo9ycrEp5bqIZiPbxNbEUPZBm07fN7fX59W7XMdC1VP0x2sp1y5MAdZi/hhmLUo3m9b3wrGUa2F2o3RZAtiTcr3AMSP0fSwtkMG3NILqX90/ofRLfBulD+xw69R6+kQX8zr1XYZyQddTZF/Ojf64n6f+Dd3qrrBfe/42K3VIG2o/y1Xrc6eWuGb6akNc78JsZ/2arxl83zvAD3lrffPVadWpHlr1+vFByglDq9fdKS2176D0fQd4rB6bBzW6gnRjsDK/sj4G0ipzaz/dMVDGhtUpLcwTKC3oCy37clb0x3TKvySbZF/eEP2xNqXf+1W1FbWl9Z5eVB8Dae6HVYEHsi8f75BvqMf8whwHP6J0Q/krpUvZ3ZQTEyhdnJZr5B3KPmia70ZVNdCFcjLQjSHXU/THgZRuNn+jdLm5jdLlLSldYV7AU98flBb5QyknJK3geyrlQuDvdllWadgZfEsj72BK94PPR3+cNkCeVneAgT6Tq9I5kB4J69DWelz7f65J+eu6pVWeF2Rfdmr1HcxQW5xa23rGAPPXbcu3sJrb+csQtjPQ+2nlW2eA+Z3eT2uZVbMvH+owf0ADteTVrhrTgGnRH+tRLl7bh9KtYgPKxXVdb6ZDWqvMH86+PKqLdbQCuImD5nrquv8v+3KgC3iH4kTKCcLezBsZZhme2urd3O6R2Zcf7XLdfwfWiP5YtkNgOdCxO9i6oMvjIPpjCiXw/h3wmub263Ugn2hbZCj7YLgNqZ7qxbT9lMD8he3dYeq1GvPJvryj/uvzpuiPTSgncptTuq/NHob3IS0U+3xLIyz78i+Ui382ZOBhB1t9rNdrnxH98RwWvUV3KNq7QUAJzpYBmn9PX9qYN9Ja292xfUb9YW71qf/DCG7nOZSuOre0dTkZTKs889VpvRCyU7/7Ea3X7Mvb63UAO1NGxtluIS66bDekMte+8dcC60QZCnOwvI8A1wGbRRnKcFH9jHIS+a4alO5N+TfoB235LqdeKDuEdf+B8rva6RqPHYdYzqEeB8+pz2d0CGi3onS1+Leh7IMRMNR6WovyHXhxh8B7JTpf1N1ydH2eihdaaoww+JZ64zOUlqbD6Nxd4EZKQLBr/RscgOiPFSjDwvXSp+pIJq0yLE8ZRQGe+lftdynvqS/6Y74LraI/lqojjgyHn1OGlXt7lOETmz4C/Afwu1z0m6ocX5//K/qj1U+51fL/Fcp35nFDWN/plBOrd9SWyaZpzOuW0vQN4HHgyAGGSXta9EfXAWH0x4QoY0W3ezqwMiXwnG/89qGoF5NeALw5+mPfTnmiP57fPLaZd1x/u56INPMuFf2xbiPpq5QLJY+vF8y1r3v1GHhYy/ay/pMynN1E4P9RuiucmX15b1u+eynjTk+J/vhUdBjKMPrj2dEfzeEiW5+Pw+vnppVvDeC/uilfw1CPg5n1ece2fGvz1P7tTUPZB8NpqPV0L6WLyZY12G7lX5bSFWWwIQ6nA3+inGS9DfhT9uXvF6340qKx24nUA9mXD0QZP7rjMFx1aML/odzc5v9q95RlKH+P30lv79J4A3Bd7Q/8OKXLzLMpY4m3RlEh+/L+KGNYnwZcWvvSXkdpLVyfcsHYmpRRLhZJ9uUjNaj7MWU84R9TusZsSRle8G5Kv85F3c7F0R9fovxFf22tg39QhjDbnHKB2JeHWO6plL64F0R//IjSb3y7ur7zKV1AmsvcWN/r8ZT98BtK8LAspV63p4yj/bwuizGRsn9uoLQ43g6sQhk28RmUm8XMN4rEQngH5ULV42r/3MsoJ2eTKGNIb045JlpB7rGUetgL+HP0x+n1fT0TeBnl/U8DyL48PsoNZD4A/CX64yzK/l+D8o/SSykB3fu7LOuJlFFrPt943ckHKTfW+QywZ/THhZT++8+kXGj5Isqwea3RX35I6V//Bsrxczplv72FMoTes7ss38IcB1dQhtx7c/THxZRjdR3KsXsTnb9Dut4Hw2xI9ZR9+WT0x1GUbkLX1PxPowwluAZ1TO9OG8q+zOiPb1FO4MBWb40BtnxLvXMU81qnOumjDOn3GOXv0ddQbiaxMyUI7pW3UX50X08JPpai/ADv1t6nuA459p+Uv3Y3oAQ/76UEWucw/5BuCy378nTKXTrPpNTJQZQA6FvAltmXfx2m7XySElD9mRKUHEipg/+ijIU8pFbiOuTkLpQRKd5GqaMHKIFox+EWsy+/TzmxOJlSvx+k9M9+DvATShDarZmUY+tuSoDyUcq487dQAub5xhpfGNmXs2qZD6PckOadlLrbhhIov495N/0h+zKzL/emvK8bKHXzUUoXnQsoQ+k1138A5Zi8hDKG/UcpwduqlBOirw2hrBdS7lq6LGVf/HKAfA/V8nyIMtrKbnW7OwEPU1rOz26+J+CtlPpeirLf3kA5MXhbt+VrrK/r46AOMfgG4JuU4PlASmB9LAN8hwx1HwyXhaynT1FGj/on5Vh6M2Xkla1Y8AhHJ1AaBeYw8ImW1DORjrQjSZLGqdr97ffA97Mv9xzl4ki2fEuSpHGtNdLLN0a1FFJln29JkjSuRH88n3Jdw5aUfu+/zL68bHRLJRUG35IkabzZEvgcZRSpHzO06ySkEWWfb0mSJKlH7PMtSZIk9cgS1e1krbXWyg022GC0iyFJkqRx7Morr7wvMyd0mrdEBd8bbLABM2bMGO1iSJIkaRyLiFsHmme3E0mSJKlHDL4lSZKkHjH4liRJknrE4FuSJEnqEYNvSZIkqUcMviVJkqQeMfiWJEmSesTgW5IkSeoRg29JkiSpRwy+JUmSpB4x+JYkSZJ6ZJlebiwilgfOB5ar2/5JZvZFxDRgP2B2zXpoZp5ZlzkEeA8wFzgwM8+q6VsCJwArAGcCH87M7N27kSRJw+Hwd71ltIuwWDns+z8Z7SJoEfQ0+AbmAC/LzEciYlngwoj4dZ13ZGZ+pZk5IjYF9gA2A54J/C4iNs7MucA3ganApZTgexfg10iSJEljVE+7nWTxSH25bH0M1lq9K3BKZs7JzFuAm4GtImJdYJXMvKS2dp8EvHEkyy5JkiQtqp73+Y6IpSPiKuBe4OzMvKzO+mBEXB0Rx0fE6jVtInB7Y/FZNW1inW5PlyRJksasngffmTk3MycDkyit2JtTupA8G5gM3AUcUbNHp1UMkj6fiJgaETMiYsbs2bM7ZZEkSZJ6YtRGO8nMB4FzgV0y854alD8JfAfYqmabBazXWGwScGdNn9QhvdN2jsnMKZk5ZcKECcP8LiRJkqTu9TT4jogJEbFanV4BeAVwY+3D3fIm4No6fQawR0QsFxEbAhsBl2fmXcDDEbF1RASwF3B6z96IJEmStBB6PdrJusCJEbE0JfA/NTN/GRHfi4jJlK4jM4H3AWTmdRFxKnA98ARwQB3pBGB/5g01+Gsc6USSJEljXE+D78y8GtiiQ/qegyxzOHB4h/QZwObDWkBJkiRpBHmHS0mSJKlHDL4lSZKkHjH4liRJknrE4FuSJEnqEYNvSZIkqUcMviVJkqQeMfiWJEmSesTgW5IkSeoRg29JkiSpRwy+JUmSpB4x+JYkSZJ6xOBbkiRJ6pFlRrsAkiSNtm987BejXYTFygePeP1oF0FabNnyLUmSJPWIwbckSZLUIwbfkiRJUo8YfEuSJEk9YvAtSZIk9YjBtyRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1CPeXl6SJGkJdcPh54x2ERY7mxz2skVa3pZvSZIkqUcMviVJkqQeMfiWJEmSeqSnwXdELB8Rl0fEHyPiuojor+lrRMTZEfHn+rx6Y5lDIuLmiLgpInZupG8ZEdfUeUdFRPTyvUiSJElD1euW7znAyzLzBcBkYJeI2Bo4GJiemRsB0+trImJTYA9gM2AX4OiIWLqu65vAVGCj+till29EkiRJGqqejnaSmQk8Ul8uWx8J7ArsWNNPBM4FPlnTT8nMOcAtEXEzsFVEzARWycxLACLiJOCNwK8XtYxbfvykRV3FEufKL+812kWQJElaLPS8z3dELB0RVwH3Amdn5mXAOpl5F0B9Xrtmnwjc3lh8Vk2bWKfb0yVJkqQxq+fBd2bOzczJwCRKK/bmg2Tv1I87B0mffwURUyNiRkTMmD179tALLEmSJA2TURvtJDMfpHQv2QW4JyLWBajP99Zss4D1GotNAu6s6ZM6pHfazjGZOSUzp0yYMGFY34MkSZI0FL0e7WRCRKxWp1cAXgHcCJwB7F2z7Q2cXqfPAPaIiOUiYkPKhZWX164pD0fE1nWUk70ay0iSJEljUq9vL78ucGIdsWQp4NTM/GVEXAKcGhHvAW4D3gqQmddFxKnA9cATwAGZObeua3/gBGAFyoWWi3yxpSRJkjSSej3aydXAFh3S7wdePsAyhwOHd0ifAQzWX1ySJEkaU7zDpSRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1CMG35IkSVKPGHxLkiRJPWLwLUmSJPWIwbckSZLUIwbfkiRJUo8YfEuSJEk9YvAtSZIk9YjBtyRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1CMG35IkSVKPGHxLkiRJPWLwLUmSJPWIwbckSZLUI8uMdgGkpts+8/zRLsJiZf1PXzPaRZAkSUNgy7ckSZLUI10F3xHxk4h4TUQYrEuSJEkLqdtuJxOAXwD3RMRJwAmZeePIFUuSliznvXSH0S7CYmeH888b7SJI0pB11ZKdmTsAGwHHArsD10XExRHx3ohYeSQLKEmSJI0XXXcjycy/ZuanM3ND4FXAzcCRwF0RcWJE7DhCZZQkSZLGhYXtw30p8HvgJmBF4GXAORFxVURsMVyFkyRJksaTIQXfEbFDRHwXuBs4ArgceFFmrgdsDtwPnDTI8utFxO8j4oaIuC4iPlzTp0XEHTV4vyoiXtNY5pCIuDkiboqInRvpW0bENXXeURERQ3rnkiRJUo91dcFlRHwK2AfYALgA+ADw48x8rJUnM6+v+S4YZFVPAB/LzD/UvuJXRsTZdd6RmfmVtu1uCuwBbAY8E/hdRGycmXOBbwJTKa3wZwK7AL/u5v1IkiRJo6Hb0U7eD5wIHJ+ZNw+S70Zg34FmZuZdwF11+uGIuAGYOMj6dgVOycw5wC0RcTOwVUTMBFbJzEsA6ggsb8TgW5IkSWNYt91O1s/MQxcQeJOZD2Tmid2sMCI2ALYALqtJH4yIqyPi+IhYvaZNBG5vLDarpk2s0+3pkiRJ0pjV7VCDc1vTEbFURKzY/hjKRiNiJeCnwEcy8yFKF5JnA5MpLeNHtLJ2Ks4g6Z22NTUiZkTEjNmzZw+lmJIkSdKw6vYOlxERn6zdPh4HHu7w6EpELEsJvE/OzJ8BZOY9mTk3M58EvgNsVbPPAtZrLD4JuLOmT+qQPp/MPCYzp2TmlAkTJnRbTEmSJGnYddvt5EDgYOA4Sqvz4cBngD8BMykXPi5QHZHkOOCGzPxqI33dRrY3AdfW6TOAPSJiuYjYkHKjn8tr3/GHI2Lrus69gNO7fC+SJEnSqOj2gsv9gD7gfymB98/riCWfpdx2fqMu17MtsCdwTURcVdMOBd4eEZMpXUdmAu8DyMzrIuJU4HrKSCkHNLrA7A+cAKxAudDSiy0lSZI0pnUbfG8IXJWZcyPicWA1gMx8MiKOptx2/uAFrSQzL6Rzf+0zB1nmcErA354+gzK2uCRJkrRY6Lbbyf3ASnX6NsooJS2rU1qfJUmSJA2i25bvi4AXUVqofwBMi4g1gH8BBwDTR6Z4kiRJ0vjRbfA9jXnjaH+O0u1kH0qL99nAh4a7YJIkSdJ401XwnZk3ATfV6TnAh+tDkiRJUpe67fMtSZIkaREN2PIdEecMZUWZ+bJFL44kSZI0fg3W8n1/22NjYHtgReCR+rwdZYzv+0a2mJIkSdLib8CW78x8a2s6It4DPBfYJjNva6SvD/ySctGlJEmSpEF02+f7MODTzcAboL7uo9ylUpIkSdIgug2+nwEsN8C85YC1h6c4kiRJ0vjV7Tjf5wJfjIi/1Nu6AxARLwK+CJw3AmWT1GPbfn3b0S7CYuWiD1002kWQJC1mum35ngo8AFwWEXdGxFURcSdwaU2fOlIFlCRJksaLbm+yMwt4YUS8hnKb+WcAdwNXZOaZI1g+SZIkadzottsJADXQNtiWJEmSFoJ3uJQkSZJ6xOBbkiRJ6hGDb0mSJKlHDL4lSZKkHllg8B0Ry0fEbyNixx6UR5IkSRq3Fhh8Z+ZjlOEFlx754kiSJEnjV7fdTs4A3jiSBZEkSZLGu27H+T4L+HJErEsZ5/seIJsZvNmOJEmSNLhug+/v1+c310e7xG4pkiRJ0qC6Db43HNFSSJIkSUuAroLvzLx1pAsiSZIkjXddj/MdEctFxP4RcVwdenCjmr57RGwyckWUJEmSxoeuWr4jYmPgbGBV4EpgR2DlOnt74LXAXiNQPkmSJGnc6Lbl+yjgNmADYGcgGvPOA7brZiURsV5E/D4iboiI6yLiwzV9jYg4OyL+XJ9XbyxzSETcHBE3RcTOjfQtI+KaOu+oiIhO25QkSZLGim6D7+2Bz2fmg7QNMUgZdnDdLtfzBPCxzNwE2Bo4ICI2BQ4GpmfmRsD0+po6bw9gM2AX4OiIaI2q8k1gKrBRfezSZRkkSZKkUdFt8P0YsMIA8yYCD3azksy8KzP/UKcfBm6oy+8KnFiznci8G/rsCpySmXMy8xbgZmCrOt74Kpl5SWYmcBLeBEiSJEljXLfB99nAoRGxaiMtI2I54EOUG+8MSURsAGwBXAask5l3QQnQgbVrtonA7Y3FZtW0iXW6PV2SJEkas7od5/vjwEWUluezKV1PPk3pDvI0Ot94Z0ARsRLwU+AjmfnQIN21O83IQdI7bWsqpXsK66+//lCKKUmSJA2rrlq+M/N24AXAtygXXf6F0s/7x8CWmXl3txuMiGUpgffJmfmzmnxP7UpCfb63ps8C1mssPgm4s6ZP6pDeqezHZOaUzJwyYcKEbospSZIkDbuux/nOzL9l5qcyc5vM3Dgzt87MwzLz/m7XUUckOQ64ITO/2ph1BrB3nd4bOL2RvkcdY3xDyoWVl9euKQ9HxNZ1nXs1lpEkSZLGpG67nQAQEasBm1Nave8ErqsjoHRrW2BP4JqIuKqmHQp8ATg1It5DGdLwrQCZeV1EnApcTxkp5YDMnFuX2x84gXIh6K/rQ5IkSRqzur3JzjLA4cABwIqNWY9GxNHAYZn5+ILWk5kX0rm/NsDLB1jm8Lrt9vQZlBMBSZIkabHQbcv3VykXLX4G+BmlT/bawG7Ap4DlgQNHooCSJEnSeNFt8L0ncGhbP+0HgMMj4jHgvzD4liRJkgbV7QWXTwLXDTDvWgYY5k+SJEnSPN0G398D3jvAvP2A7w9PcSRJkqTxq9tuJ7cCu0XEdZTh/1p9vncFVgaOiIgP1LyZmd8c9pJKkiRJi7lug+8j6vNEYJMO85t9wRMw+JYkSZLadBV8Z2bXN+ORJEmS1JlBtSRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1COLFHxHxGrDVRBJkiRpvOsq+I6I/SPiE43XkyNiFnB/RFwZEZNGrISSJEnSONFty/eHgIcar48C7gTeWdfxhWEulyRJkjTudHuHy/WBmwAiYgKwLfDyzDw3Iv4FfGOEyidJkiSNG922fM8BnlandwIeBS6orx8A7PstSZIkLUC3Ld+XAwfUft4HAr/JzLl13n9QuqBIkiRJGkS3Ld8HAZsB1wDrAdJB12EAABtdSURBVIc15u0OXDTM5ZIkSZLGna5avjPzOuDZEbEm8EBmZmP2QcDdI1E4SZIkaTxZYMt3RCwfEXMiYtfMvL8t8CYzr8nM2SNXREmSJGl8WGDwnZmPAfcCcxeUV5IkSdLAuu3z/W3gwIhYdiQLI0mSJI1n3Y52shqwOTAzIqYD9wDN7ieZmZ8c7sJJkiRJ40m3wfdulLG+AbbvMD8Bg29JkiRpEN2OdrLhSBdEkiRJGu+67fMtSZIkaRF1HXxHxH9GxI8i4i916MEX1vTDI+LVI1dESZIkaXzoKviuwfWVwDOAk4DmqCdzgA91uZ7jI+LeiLi2kTYtIu6IiKvq4zWNeYdExM0RcVNE7NxI3zIirqnzjoqI6Gb7kiRJ0mjqtuX788AJmbkDcHjbvKuAyV2u5wRglw7pR2bm5Po4EyAiNgX2oNzWfhfg6IhYuub/JjAV2Kg+Oq1TkiRJGlO6Db6fB/yoTmfbvIeANbpZSWaeDzzQ5TZ3BU7JzDmZeQtwM7BVRKwLrJKZl9S7bZ4EvLHLdUqSJEmjptvg+17gPwaYtxlw2yKW44MRcXXtlrJ6TZsI3N7IM6umTazT7emSJEnSmNZt8H0K8JmI2K6RlhGxMWV875MXoQzfBJ5N6bpyF3BETe/UjzsHSe8oIqZGxIyImDF79uxFKKYkSZK0aLoNvj8FzADOY14r9+nAtcDVwOcWtgCZeU9mzs3MJ4HvAFvVWbOA9RpZJwF31vRJHdIHWv8xmTklM6dMmDBhYYspSZIkLbJub7IzB3hdRLwceDmwFqXv9vTMPHtRChAR62bmXfXlmygBPcAZwA8i4qvAMykXVl6emXMj4uGI2Bq4DNgL+PqilEGSJEnqhW5vLw9AZk4Hpi/sxiLih8COwFoRMQvoA3aMiMmUriMzgffVbV0XEacC1wNPAAdk5ty6qv0pI6esAPy6PiRJkqQxbcDgOyLWH8qKMnOBF11m5ts7JB83SP7DmX9oQzJzBrD5UMonSZIkjbbBWr5nMsiFjB0sveAskiRJ0pJrsOD79Y3pVYAvATcAP6MMPbg2sBtlDPCPj1QBJUmSpPFiwOA7M3/Vmo6IE4BfZub+bdm+FRHfAl5LGY5QkiRJ0gC6HWrwzZQW705+CrxheIojSZIkjV/dBt//BLYbYN72wGPDUxxJkiRp/Op2qMFvAp+KiDUp42+3+nzvShkacL4RSSRJkiQ9Vbc32ZkWEX8DPgF8gHm3eb8bOCgzvzZyRZQkSZLGhwUG3xGxFLAuZTzur1Nu+f4MSuB9e70tvCRJkqQF6KbP91KUMb+3y8wnM/PWzLysPht4S5IkSV1aYPCdmU8AtwIrjnxxJEmSpPGr29FOvggcFhETRrIwkiRJ0njW7Wgnr6L0+54ZEVcC9/DUW89nZu4+3IWTJEmSxpNug++1gJvaXkuSJEkagm6HGtxppAsiSZIkjXfd9vmWJEmStIgWGHxHxOSIODYi/hQR/6iPP0XEdyJici8KKUmSJI0HgwbfEfFxYAawG3AtcEx9XFvTrqh5JEmSJC3AgH2+I+L1lCEGvwR8LjMfapu/MnAI8IWIuD4zfzWiJZUkSZIWc4NdcPkx4MTMPLjTzMx8GDg0ItYFDgIMviVJkqRBDNbtZAvglC7WcQrwwuEpjiRJkjR+DRZ8Lw080cU6nljAeiRJkiQxeNB8LfC6LtbxuppXkiRJ0iAGC76/BRwQEftFRHTKEBHvBT4AfHMkCidJkiSNJwNecJmZJ0TEi4FvAwdFxC+AW+vsZwGvBTYGvp2ZJ414SSVJkqTF3KC3l8/M/SPiLODDwAHAcnXWHOAS4ODMPH1kiyhJkiSND4MG3wCZ+XPg5xGxNLBWTb4vM+eOaMkkSZKkcWaBwXdLDbbvGcGySJIkSeNaT4cIjIjjI+LeiLi2kbZGRJwdEX+uz6s35h0SETdHxE0RsXMjfcuIuKbOO2qgC0IlSZKksaTX43OfAOzSlnYwMD0zNwKm19dExKbAHsBmdZmja9cXKKOrTAU2qo/2dUqSJEljTk+D78w8H3igLXlX4MQ6fSLwxkb6KZk5JzNvAW4Gtqq3s18lMy/JzAROaiwjSZIkjVlj4c6U62TmXQD1ee2aPhG4vZFvVk2bWKfb0yVJkqQxbSwE3wPp1I87B0nvvJKIqRExIyJmzJ49e9gKJ0mSJA3VWAi+76ldSajP99b0WcB6jXyTgDtr+qQO6R1l5jGZOSUzp0yYMGFYCy5JkiQNxVgIvs8A9q7TewOnN9L3iIjlImJDyoWVl9euKQ9HxNZ1lJO9GstIkiRJY1bX43wPh4j4IbAjsFZEzAL6gC8Ap0bEe4DbgLcCZOZ1EXEqcD3wBHBA48Y++1NGTlkB+HV9SJIkSWNaT4PvzHz7ALNePkD+w4HDO6TPADYfxqJJkiRJI24sdDuRJEmSlggG35IkSVKPGHxLkiRJPWLwLUmSJPWIwbckSZLUIwbfkiRJUo8YfEuSJEk9YvAtSZIk9YjBtyRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1CMG35IkSVKPGHxLkiRJPWLwLUmSJPWIwbckSZLUIwbfkiRJUo8YfEuSJEk9YvAtSZIk9YjBtyRJktQjBt+SJElSjxh8S5IkST1i8C1JkiT1iMG3JEmS1CMG35IkSVKPGHxLkiRJPWLwLUmSJPXImAm+I2JmRFwTEVdFxIyatkZEnB0Rf67PqzfyHxIRN0fETRGx8+iVXJIkSerOmAm+q50yc3JmTqmvDwamZ+ZGwPT6mojYFNgD2AzYBTg6IpYejQJLkiRJ3RprwXe7XYET6/SJwBsb6adk5pzMvAW4GdhqFMonSZIkdW0sBd8J/DYiroyIqTVtncy8C6A+r13TJwK3N5adVdPmExFTI2JGRMyYPXv2CBVdkiRJWrBlRrsADdtm5p0RsTZwdkTcOEje6JCWnTJm5jHAMQBTpkzpmEeSJEnqhTHT8p2Zd9bne4HTKN1I7omIdQHq8701+yxgvcbik4A7e1daSZIkaejGRPAdEU+PiJVb08CrgGuBM4C9a7a9gdPr9BnAHhGxXERsCGwEXN7bUkuSJElDM1a6nawDnBYRUMr0g8z8TURcAZwaEe8BbgPeCpCZ10XEqcD1wBPAAZk5d3SKLkmSJHVnTATfmflX4AUd0u8HXj7AMocDh49w0SRJkqRhMya6nUiSJElLAoNvSZIkqUcMviVJkqQeMfiWJEmSesTgW5IkSeoRg29JkiSpRwy+JUmSpB4x+JYkSZJ6xOBbkiRJ6hGDb0mSJKlHDL4lSZKkHjH4liRJknrE4FuSJEnqEYNvSZIkqUcMviVJkqQeMfiWJEmSesTgW5IkSeoRg29JkiSpRwy+JUmSpB4x+JYkSZJ6xOBbkiRJ6hGDb0mSJKlHDL4lSZKkHjH4liRJknrE4FuSJEnqEYNvSZIkqUcMviVJkqQeWayD74jYJSJuioibI+Lg0S6PJEmSNJjFNviOiKWB/wVeDWwKvD0iNh3dUkmSJEkDW2yDb2Ar4ObM/Gtm/gs4Bdh1lMskSZIkDWhxDr4nArc3Xs+qaZIkSdKYFJk52mVYKBHxVmDnzHxvfb0nsFVmfqgt31Rgan35XOCmnhZ0eK0F3DfahVhCWfejy/ofXdb/6LHuR5f1P3oW97p/VmZO6DRjmV6XZBjNAtZrvJ4E3NmeKTOPAY7pVaFGUkTMyMwpo12OJZF1P7qs/9Fl/Y8e6350Wf+jZzzX/eLc7eQKYKOI2DAingbsAZwxymWSJEmSBrTYtnxn5hMR8UHgLGBp4PjMvG6UiyVJkiQNaLENvgEy80zgzNEuRw+Ni+4ziynrfnRZ/6PL+h891v3osv5Hz7it+8X2gktJkiRpcbM49/mWJEmSFisG3yMsIjIivtd4vUxEzI6IXw7T+qdFxEHDsa7xJCLWjIir6uPuiLij8fppw7idHYdrXy4uIuLIiPhI4/VZEXFs4/UREfHRLtazQURcO1LlrNt4ZCTXP1YMcrw/GBHX92D7+0TEN0Z6O4uziJjb2EdXRcQGHfKcGRGrdUj3e74LEXFYRFwXEVfXOn7xIHn3iYhnDsM2Z0bEWou6nvFmKPtiCOscN5+DxbrP92LiH8DmEbFCZv4TeCVwxyiXadzLzPuByVA+sMAjmfmVUS3U+HEx8FbgaxGxFGUs1lUa87cBPtJpQY2MgY73GuAt9MlhRCyTmU8MRxnFPzNzcqcZERGUbqCv6XGZxo2IeAnwOuCFmTmnBsSDNbTsA1xLhyGKB9mGn4cuLMS+WOLY8t0bvwZeW6ffDvywNSMi1oiIn9ezw0sj4j9r+rSIOD4izo2Iv0bEgY1lDouImyLid5QbB7XS94uIKyLijxHx04hYMSJWjohbImLZmmeVeqa+bC/e+FgSESdExFsarx9pTH+81t3VEdFf054eEb+q9XltROxe03eJiBsj4kLgzY11bBURF0fE/9Xn59b0CyJiciPfRa39vJi6iBJgA2xG+QF7OCJWj4jlgE0AIuK8iLiytoyvW9O2rPV5CXBAa4W1FepnEfGbiPhzRHypMe9VEXFJRPwhIn4cESvV9C9ExPV1n32lpm1Y814REZ9trGOliJhe13FNROxa0z8bER9u5Du8+VkbJ5aOiO/UVqjfRsQKAPW7ZUqdXisiZtbpfWo9/wL4bUSsGxHn19arayNi+5rv3RHxp4g4D9i2tbGIeH1EXFY/B7+LiHUiYqm6XyfUPEtFxM2xBLcYRvnn54aIOBr4A7BeNFpR/Z4fsnWB+zJzDkBm3peZd0bEp2t9XRsRx0TxFmAKcHI9rldoq/spEXFunZ5Wl/stcFKUf5l+W4/vbwPRKkCU3/Ir62dtak17T0Qc2cizX0R8tVeVMkoG2heD1fGSFe9kpo8RfACPAP8J/ARYHrgK2BH4ZZ3/daCvTr8MuKpOT6O0MC5HaVm8H1gW2BK4BliR0tp4M3BQXWbNxnb/G/hQnf4u8MY6PRU4YrTrpcf7YBpwEHAC8JbmvqnPr6JcVR2UE9JfAi8FdgO+08i/at2HtwMb1fynNvblKsAydfoVwE/r9N7A1+r0xsCM0a6TYajTmcD6wPuA9wOfBV5DCcIuqcfuhJp3d8pQoABXAzvU6S8D19bpfYC/Nur4VspNtNYCzgeeXvN9Evg0sAblbrWti8ZXq89nAHvV6QMa+3gZYJU6vVb93ASwAfCHmr4U8Jfm52hxfLSO9zq9AfAEMLm+PhV4V50+F5jSqJOZjX0xC1ijvv4YcFidXhpYmfLjehswgdKidRHwjZpn9cZ+eS/1+wboAz7S+Mz9dLTrqsf7ZS7l+/8q4LS6b54Etm7kmVn3hd/zQ6/flWrd/gk4uvE9s0Yjz/eA19fpfx//zbqv01OAc+v0NOBKYIX6+ijg03X6tUA2lmt9ZlagNEqsCTy9fq8sW+ddDDx/tOtrlPbFYHW8RMU7tnz3QGZeTfmifTvzD424HeULgcw8B1gzIlat836VmXMy8z7gXmAdYHvgtMx8NDMf4qk3Fto8SivrNcA7Ka2SAMcC767T76YcnJrnVfXxf5QWqOdRgutrgFdExBcjYvvM/Hudd0tm/jnLp/v7jfWsCvw4Sj/mI5lX/z8GXlfPvvelnAQs7lqt39tQgu1LGq/vADYHzo6Iq4D/AibV43q1zDyvruN7beucnpl/z8zHgOuBZwFbA5sCF9V17V3THwIeA46NiDcDj9Z1bMu8f5aa6w/gcxFxNfA7YCKwTmbOBO6PiC2ox0CWLhzjyS2ZeVWdvpLyXbQgZ2fmA3X6CuDdUbqzPD8zHwZeTPnhnJ2Z/wJ+1Fh2EnBW/R76OPM+B8cDe9XpfVnyvof+mZmT6+NNNe3WzLy0Q16/54coMx+hBGtTgdnAjyJiH2Cn+k/MNZQGrs0GXsuAzsjSbRRKw8z36zZ/Bfytke/AiPgjcCml8WCjzPwHcA7lN+B5lCD8moUow2JjkH0xmCUq3rHPd++cAXyF0uq9ZiM9OuRtjf84p5E2l3n7a6DxIU+gnPH9sR7oOwJk5kX1L84dgKUzc0QvchvDnqB2tYqIYF4ftAA+n5nfbl8gIraktOh+vv7teAYD1/9ngd9n5pui9LU9FyAzH42Is4FdgbdRzvgXdxdTAu3nU1p4bqe0kD5E+aGZmJkvaS4Q5UKywcY27XS8ByUQfHt75ojYCng55e62H6T8sDLANt5JaaXdMjMfj9LFYvk671hKa+8zKAHieNNeryvU6X9/HphXFy3/aE1k5vkR8VJKK9/3IuLLlP080L78OvDVzDwjInaktGqRmbdHxD0R8TJK8P7OhX5H48c/Bpnn9/wQZeZcyvfuuTUoex/ln+cp9fibxvzHektXn4fWptoXrsf6K4CX1O/8c3nqd8yhwI2M0WBwuHXYF3szeB0vUfGOLd+9czzwmQ5nvOdTf4Tqh/e+eoY3kPOBN9U+aisDr2/MWxm4q7awtv+wnURpEVwiPvgDmEk5G4cSCLf6gZ0F7Bvz+hJPjIi1o1wJ/2hmfp9y4vRCypfnhhHx7LpsMyhclXkX0+7Ttu1jKX9XXtFoUVycXUS5oOaBzJxb39NqwEsoraATolx0Q0QsGxGbZeaDwN8jYru6jm6Cr0uBbSPiOXVdK0bExnVfrZrlRlsfoV5sWMu1R4f1rwrcWwPvnSit5y2nAbsAL6IcC0uKmcz7PLxloEwR8SxK3X0HOI7yObgM2LH2f12WcgFuS/NzsHfb6o6ltBqeWn+c1Znf80MUEc+NiI0aSZMpXdMA7qvfGc3j/GFKXbbMZN7nYbdBNtX8zX41pZsVlOP+bzXwfh7lXzsAMvMySkv4O2hc8zVeDbAvbqX7Om4Zt58DW757JDNnAf/TYdY04Lv17/BHmf/Hqn09f4iIH1H6U90KXNCY/SnKj+KtlC4TzS+Wkyn9osb9B38Q3wFOj4jLgenU1ozM/G1EbAJcUhrEeQR4F/Ac4MsR8STwOLB/Zj5WL6T5VUTcB1xI6WIB8CXgxCjD7J3T3HBmXhkRDzGGvwyG6BpK37wftKWtlJn3Rrmg6aja1WQZ4GvAdZS/AY+PiEfpItDNzNm1VeOHUS7mhNKN5WHKvlye0jr+/+q8DwM/iHIR5U8bqzoZ+EVEzKB8dm5sbONfEfF74MElLCD8CnBqROxJ2/HaZkfg4xHxOOWzsVdm3lVbES8B7qJ011q65p9G6X51B+XkacPGus6gfAbGy+dgRPg9v1BWAr5e/2F7gtI/eCrwIKWeZlK6ULWcAHwrIv5JaTToB46LiEMp9TuQfsr30R+A8yjXPgD8Bnh//S2/iXLsN51Kufbib4x/A+2LTeiujoHx/TnwDpdLiBoM7ZqZe452WZZEtRX9XOB5mfnkKBdHDVGGS/wD8NbM/PNol2c8izK6ypGZuf1ol2U88nt+7IpyP4gjM3P6aJdlvFscPge2fC8BIuLrwKspfZfVYxGxF3A48FED77ElIjaljG5zmoH3yIqIg4H9sa/3iPB7fmyqrb+XA3808B55i8vnwJZvSZIkqUe84FKSJEnqEYNvSZIkqUcMviVJkqQeMfiWpDEiIt4TERkRk9rSv1jT39WW/sqavs0wlmGDus7XDXG5aXW5jIgnI+JvEXFFRBweEc8YrvJJ0uLO4FuSxo6L63N7ML0N5T4AndLnUG4bPxb8nTJm8jaUmx39DNgTuKbeLVaSlngG35I0dtwIPEAjyK53cNuScte2TsH3jMycw9jwRGZeWh9nZebnKbf3vgv4UUQsvYDlJWncM/iWpDEiy9ivl/DUIHsLyl08jwaeX2+z3Lo50IuZ11pORGwXEedFxKMRcX9EfKeVv5Fn/Yg4JSIeqPnOiojnDlauiNgxIh6OiM8txHt6EPgE8GzglY11fiEiromIRyJiVkSc3OyeEhFfjoi/Rr3tbCP93RHxr4hYa6hlkaSxwOBbksaWi4HJEbFCff0SSreSaym3yn5xTd8MWBW4CCAitgWmA3cDbwE+QrnRxL9v5R4RawAXAs8F3g+8DXg68LvG9p4iInYGzgS+nJmHLuR7+j3lNtNbN9LWBj4HvLaW9T+Acxqt48dSbk2/Q9u69gF+kZn3LWRZJGlUeYdLSRpbLgaWBV4EnE9pBb8kMzMiLq2vf8e81vFWy/cXgIszc/fWiiLiDmB6RGyemdcC/48SbE/OzAdqnouAmcC+wP82CxIRbwBOBf4rM7+ysG8oM+dExH3AOo20fRvbWZrS4j8L2BY4PzNvqmV7N3BuzfcfwPbAGxa2LJI02mz5lqSx5XJKK3EruN6GEpgCXNqW/ufMnB0RK1JayE+NiGVaD0or9+OUPuMArwDOBh5q5HmY0rI+pa0cuwE/Bj62KIF3Q3v3kVdHxMUR8ff6fmfVWRs3sh0H7BYRK9XX+wD3AL8ZhvJI0qgw+JakMSQzHwWuArapQw5OYl7wfQmwde3vvQ21ywmwOrA0pV/4443HHEor+no131rA7m15Hgd2auRpeQPl4s/TFvU9RcTywJqUwJmIeBFwBiXg3pNy4tDqkrJ8Y9FTgSeBt9W+33sBJ2XmE4taJkkaLXY7kaSx52LgHZQAe2Zm3lXTLwNWpvSDfg7wxZr+IJDANEr/7HZ31ucHKEHvZzvkebjt9YeAjwJnR8RLM/P+hXonxU6U35vWScSbgNnA7vUiUyLiWe0LZeY/IuIUSov3rcCzgBMWoRySNOoMviVp7LkIOBDYm3kBK5n5cERcBxxUky6u6f+o/cGfm5mfGWS90ykXWV6Xmf9cQBkeAnYGzgPOioiXZeZDQ30jEbEa5SThZkpfdYAVgMdbgXf1zgFWcRylu8004NLMvGGoZZCkscTgW5LGnlZ3klcDH26bdwmwH/A3oBmIfoJyceWTwE8oLdnrU0YTOSwz/wR8FXgXZVSRrwN3UC6C3AG4MDN/2NxQZt4fEa8ELgB+GRG71G4xA1kmIlrdR1am9DXfH1gR2CUz59Z5ZwMfiYivAb+gtPC/q31ltQyX1ROO7YD3DbJtSVos2OdbksaYzLwDuI1ykeIlbbMvaaU3W44z80LgpcAE4HuUoPYTwO3UvtZ1eL6tKTfzORL4LfAlypCFVw9QlruAlwMbAD+LiKcNUvRVa/kuplys+Rbg+8DzM/Pfd+HMzDOBT1Iu6jyDEvwPdjv7nwP/BE4ZJI8kLRbiqf/6SZI0tkT8/3bumApAGAagYOoJBfhAAQqwSBWgJgyVAC/tcKcg4x+StDsinsw8Zs8C8JW1EwCW1FrbImKP8fP8nDwOwC/ENwCr6jE+uVyZ2WcPA/AHaycAAFDEwSUAABQR3wAAUER8AwBAEfENAABFxDcAABQR3wAAUOQFZ/Usrs/o6yIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekTran = df.groupby('weekday')['Transaction'].count().reset_index()\n",
    "weekTran.loc[:,\"weekorder\"] = [4,0,5,6,3,1,2]\n",
    "weekTran.sort_values(\"weekorder\",inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(data = weekTran, x = \"weekday\", y = \"Transaction\")\n",
    "plt.xlabel('Week Day', size = 15)\n",
    "plt.ylabel('Orders per day', size = 15)\n",
    "plt.title('Number of orders received each day', color = 'green', size = 20)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.036978,
     "end_time": "2020-11-25T19:29:53.569945",
     "exception": false,
     "start_time": "2020-11-25T19:29:53.532967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "People order more on weekends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:53.655769Z",
     "iopub.status.busy": "2020-11-25T19:29:53.654990Z",
     "iopub.status.idle": "2020-11-25T19:29:53.955255Z",
     "shell.execute_reply": "2020-11-25T19:29:53.954495Z"
    },
    "papermill": {
     "duration": 0.349039,
     "end_time": "2020-11-25T19:29:53.955428",
     "exception": false,
     "start_time": "2020-11-25T19:29:53.606389",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAAFXCAYAAAAIzkBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nOzde5hkVXnv8e9PuSmI3IkCOihIRCSoLYGoiBqUIBHBECVeEE0mKCbiwQTx1nSMuahBj2JiSLhpkOgRJhpBgcNRiQbQEQeYcVA0oowQRi4KiIoj7/lj745FWTXT1dNdVUN/P8/TT1Wtvdbe7+7VNfPWqrXXTlUhSZIkafgeNOoAJEmSpIXKZFySJEkaEZNxSZIkaURMxiVJkqQRMRmXJEmSRsRkXJIkSRqRjUYdgCTNVKbyXOBk4PHAVsAna7JeONKgOmQqNwDUZC0abSQPLJnKgcDngKmarJNHG01jHGMaRKZyFnA0sGtN1g3rua+TgUngWTVZn1/f2KSFxmRcGpFM5deB44BnAbsADwFuBb4GnA+cU5P109FFuG6ZyiuBM4FjarLOmudjLQI+CfywPeadwHXzeUxJkuabybg0ApnK22lGkh4EXAGcDdwN7AgcCPwz8BpgYkQhjqPfBjYDTqjJ+uiog9FQfZnm25BbRx2IJM01k3FpyDKVNwNTwI3AkTVZV/aocyhwwrBjG3OPbB9vGmkUGrqarHvwWxBJD1Am49IQtVMtTgZ+DhxSk7W8V72arE9nKpf0aP/7wOuA3wA2Ab4FfBQ4pSbrZ111C/hCTdaBPfZzFl3zRdvYvkMzSn8y8Dc0o9FbAMuBk2uyPt2xj88Dz2xfnpmpnNlxiBnNQ53J+XTMzZ32uUxl+vk656hmKpsCbwD+ANgNWANcDXygJuvjXXUX8cvfwV8B76CZRrQd8OyarM9nKqGZXvQa4LHAbcAS4C3riOMoYDGwD82UpO8A5wDv7td3wEuAvwR+B/g14NU1WWdlKjsCfwb8LrAzzd/TLcDlwF/UZP3X2mJpj3FD+3Rvmv4+AtgJeOf0HOh2KtWbgOcAO9BMEbqUZp70N3rs86HAnwBHAnsAofnQeUm731u66r4eeDGwO1DAtcD7a7LO7drvgXTNz85UrgMeAzyyJutXRswzlTcBfw28ribrgx3lO7fndEh7vncDXwLeUZP1lR772ZHmb+FQYEvgG8B7ge92152JAf8OXgj8HrBvG2u1xz8bOLUm674e+59xH3S0+WOa9+HuwI9opoP9WU3Wj2Zxfr8H/DmwF/BT4GKab7O+36Pu7sDbaP6+tqf55uP/0vTF9V11z6LPHPd+8/c7/o3alKbPXwosAs6tyXrloOcmzRdXU5GG6xhgY+C8fon4tB7/Mf8V8DGar+s/CpxK8x/tXwEXZSobz1GMj6aZFrAI+Eh7zL2AT2Yqz+qodxbNf9q0j1MdPz9c10EGOJ8b2n1+oX19dsdxbljHMTYBLqJJyjYGPtie0+OAj7Ux9PJY4Eqa38E5wGk0c9QB3gd8ANi6Lf9X4GCaJGKTPnGc3p7jbjTXA3wQuJ0m2f9sptJrYGQbmilM+7VtTgVuaZOtL9F8c/Jd4B+A02kS2cOAPdf2O+myCfD/gBfSJE3/myY5JFM5GLiKJoH5SrvtUpqk/cuZypO7znFr4D9pPsRtAZzRxrYSeBVNP0/X3Qr4Ik1f/6KtezZNQvbRTOUvZxD72TR9elSf7a8A7qXpn+njPhlYBryWJqn9APDvwAHAFzOVQ7rOadv2nF4FfJOm75cBH6L5gDeQWfwd/A3wZJq/xQ/Q/O1uQdMXZ/fY/4z7oMO72p+r23i+D/wRzQfMQb0W+Bea9+UHaT7Evxj4v+2H4s5YnwosBV5G8/f1Hpq/95cCSzOVuZyid14b23/S9OG1c7hvab05Mi4N19Pbx0sHaZSp7A+cRDPCtW9N1n+35SfR/Kd5KM1Iab/kchAH0oyCT3Uc/6PAZ9tjfA6gHaGFJgH8t0Eu4BzkfNpRsJPbFRueCZw1wIoNJ7RtPgO8oCZrTXucKZoPHCdlKp+uyfrPrnZPB/66JuvNXXH/FvCnwLfbuG9vy99C83t5BF0jpu1Frq9qz+ulNVk/6dh2Ms21A8fRJFidnkiTfL1qOu62ze/SfFh4X03W/RLC9sPH/ZKedXgE8HXgmTVZP+7Yz9bAucA9wAE1WV/v2PYEmuTwn2kSxWkfpPmG40PAcZ2jtpnKw7j/4M/7gCcBJ9Zkvauj3mbAvwFvzlQ+UZO1bC2xf5jmW4OjaRLV/9Emeo8Hzq/Juq0t2wj4OE2S+qyarC901H8kTUJ4eqayqOOD8F/TjL7f73edqZxK8y3EjM3y7+D5NVnf7trPg2guYH5FpnJq1zS3Qfpg2n7AE2uyvtfW24jmA9qzMpV9a7K+PMBpHgw8tSbrf5Ld9t+Oo2j+nfh4Wxaa/tsSeFlN1jkd9V9M8wHqXzKVPXuN/s/Co4G9en2DIo0DR8al4XpE+7hqwHavah//cjpxBWiTtBOA+4A/XP/wgCaZvN/IZE3WRcD3aL4unwvDOp9X0Xy1/786E9qarNU0o5H0Oc4tNCPv3Y5pH985nYi3+/spzYeLXl5PMzXmVZ0JWOsdNNNcXtqj3b3AGzvj7tK9L2qy7q3JuqtP/X5O6EzEW6+gWTpysjMRb4+xAvgn4EmZyp4AmcoONCOgN7cx39fV5q7pKQ/taPPLgKWdiXhb76fAiTTfkPzB2oJupz1cCjyl/YDQ6ej2sXP0+Pk0H2I+0JmIt/u6iWZ0+NdopkzQfjPzUuAummk8nfWX0nxjMoiB/w66E/G27D5+mbA/b7p8kD7o8hfTiXhbbw1Nsg+Dv9/f35mIt/6px75+C/h14PLORLw9/sdovjXZg18OXqyvt5mIa5w5Mi4N1/Rk5xqw3fQI5P/r3lCT9c1MZRWwa6ayVU3WOqeIrMOymqxf9Ci/Edh/Pfc9bd7Ppx0J3A34fk1Wr4v/po/9pB7bru6eJtQV9xd6bPsPmmSrM4aH0oxU3goc3zHXvdPP6D194Ib2Q0O3L9BMJXhTO+3iQpppK/36bW1+ClzTo3y6n3+jHbXt9rj28fE0I+tPpRncuaxHYt/tqcCDgeqz7+npSb1+J93OAg6iSb7/HP7n24GXAD+g+d1Mmz6nR/c57u4dx72QJll8KPAffZLYz/PLpH+tZvt30H5w+TOa+e2PATbvarNTx/NB+qDT0h5lN7aPWw+wn0H21ff931H+dJr35mUDxtDLIKP70tCZjEvDdRPNf/I7D9ju4e3jzX223ww8qq23vsl4v/ZrmLtv04ZxPjM5BjQjwN3+u0dZ5z5/5SK4mqxfZCq3dRVvTfMBbHuaaQiD6BlDTdadmcp+NCP3L+CXo6O3Zip/T/Ntw89neIzVNVm9Phhu2z7+0Trab9E+Tv8Of+UivbXs+6ntz7r2vTZLaObyvyxTOan9MHJoe4z3dX2rMH3cI9exz+nj9u3rVr+/kV4G/jto59V/BdiVJpn8MM388jU0v+/Xc/8pSYP0Qade76/p39uD52lf6/PenI1B+koaOpNxabi+CDyb5qvw0wdoNz0y92s085W7PaKrHjSj7/3e43P1n9xszeZ81ucYvaztGP2+uZiuuyNwvxVLMpUH0yR83+9R/2s1Wfe74HEG+n57UpO1Cnh1O/d2T5q/qeOAt9N8YHrbeh5jOu7fqMnqNXLebToJ22mtte6/7/fWZP2vGdTvqybrJ5nKx2mmGh1Ec11Drykqncc9rCbrUwPEuWOf7f3+rta2r0H+Dv6QJhH/lTt8ttdcvL6r/iB9MGqzeW9OT7vp9W/aWv896/OBUxobzhmXhutMmmXoXjQ937afrtUHvtY+Htij3m40I+3f6ZrScQfNnT276z+YZlm1uTA9LWLQEbTZnM9A2rnT3wZ2apdQ6za9MsxVA+x2uu4ze2x7Bl2JQk3W3cAK4AmZyjYDHGdGarKqJmtFTdYHaJJRaFZGWV9XtI/PmGH9L9MkSwdkKt1TKfrVnem+1+Ws9vHoTGU7mmUgr+lx8eeg53QdzQWs+2QqD++x/cCZBjjLv4Pd2sfzemzr9fc3SB+MWt/3f1d553vzjvbxV/5Nw5ujaQNnMi4N0fTKIDRLyl3Qb/mudlm5z3QUndE+vjVT2b6j3oNplgR7EL860v5l4FGZynO7yt9Ks7rAXJielvGoAdvN5nxm4wya6QHvbvc9fZzt+OXo8Rm9GvZxVvv4ls6kql0F5K/7tDmFpr/PaKce3E+msnX3MoFrk6nslWY99G7TI7j3zHRfa3EmzUjrZKbyKxfxZSoPatd2BqAm6wc0K2A8AnhPu+JHZ/0tphPadh78OcBEpvK2Xss6ZiqPzVR2nUmgNVlfAq6nWa3jNTRzzs/qUfWTNB/OjutewrDjuPu387tpp/qcAzyMrgs42/dtr4tu12bQv4Mb2scDu+o9iR4XCw/SB2PgSzRLSz49zbrk/6N9fQDNUpJf7Ng0Pe/7j7rqP5Ff/ZZA2qA4TUUaspqsv2oTkEngK5nKf9Jc+HQ3TUJ1AM3FZEs72vxnpvIumovUlmcqnwB+TDMKuBfNf1rv7jrUe2jmE38yU/kYzXzT36L56vvzDDCytxaX0yR/x7fJ6fT82g+s7YYhszyf2XhPu8/DgKszlQtpLso7kuYmNu+qyfriWtp3x/2lTOUDNDdVmY775+3+76DHHNiarDMylafQrHP87UxlemWabWj64gCa5PfYGYbx28Ap7d/NdcBqmm8SDqMZGV3v31tN1m1tUrQEuCJTuZRmZPc+mg9e+9NMydmso9nraPruWODA9jzvpTnH59HMb/98R93dgb8AXp6pfJHmb+eRNBcxPpVmObzvzDDkD9OsSPI2mjnKH+1xTj/PVI6gWXf+gvb3t4zm73eX9piPoUlmpz/QvJlmStnxbQL+xXb7i2ku8nzBDOObzd/Bh2ku3nxfmvX9r6f5nR1Ks0b5i3scZpA+GJmarMpUjqa5EdHHMpVP0vwt70Hzzc5dwCu6VoT5JM3v4Kg0N266kuZv8bB22+8P8RSkOeXIuDQCNVl/QfOf5qk0FzMdQ/Mf7/NpRu/+kK5lvWqyTqRJUK6nWXruT2new28FDqrJurer/qU0/7GtoFld4mia0bZ9meXdA3ucxx3Ai2hW1DiGJiF6BzNYhWHQ85llfPfSTN+Yvjvmn9D8Hq4H/qCNYVCvb/fzI+CPac7hIpokuWfMNVnH0dwt8/K23v+iSYweTpM8v2+A41/U1t+MJhE5gSaRuwR4Rk3WJwY+o94xX0pzd86/p7n50bE0f5d70ax28ZKu+nfQfNh7K80HlMU0I9VPoPn24esdde+kmWrxJzQrjLyI5nfyLJpE7A3t+czUh2k+KGwMfLZ63GWyPe41NKua/C2/fN+9BngKzdSJl7fxTNe/FXgaTZL868DxNFO8XkNzF86BDPJ30C63+AzgApp/C15H843Wa2nuJtlr/zPug1Fr10d/Ks0Hp/1p/v37LZr17Z/atX769LKXz6FZq3wvmt/HY2iWwPyH4UUuzb2U1zVIkiRJI+HIuCRJkjQiJuOSJEnSiJiMS5IkSSNiMi5JkiSNiMm4JEmSNCILdp3x7bbbrhYtWjTqMCRJkvQA99WvfvXWqtq+17YFm4wvWrSIpUuXrruiJEmStB6S9L2/x4KdprLmB7ePOgRJkiQtcAs2GZckSZJGbeyT8SRnJFmdZHmf7bsk+VySlUlWJHn9sGOUJEmSZmPsk3HgLODgtWxfA5xQVY8H9gOOS7LnMAKTJEmS1sfYJ+NVdRnQd4J3Vd1cVVe1z+8CVgI7DSk8SZIkadbGPhkfRJJFwJOAK0cbiSRJkrRuD5hkPMkWwHnA8VV1Z586i5MsTbL0trt7VpEkSZKGZoNLxtsLNpe1P8e2ZRvTJOLnVNX5/dpW1WlVNVFVE9tuseWwQpYkSZJ62uBu+lNVNwL7TL9OEuB0YGVVnTKywCRJkqQBjf3IeJJzgcuBPZKsSvLqripPA14OPLtjxPyQoQcqSZIkDWjsR8ar6qh1bP8ikCGFI0mSJM2ZsR8ZlyRJkh6oFmwyvtH224w6BEmSJC1wCzYZlyRJkkbNZFySJEkakbG/gHO+/PwHN/Hffz95v7Jfe+3UiKKRJEnSQuTIuCRJkjQiY5+MJ9mjY/3wZUnuTHJ8j3pvSLIiyfIk5ybZbBTxSpIkSTM19sl4VX2jqvapqn2ApwD3AEs66yTZCfhTYKKq9gIeDLxk6MFKkiRJAxj7ZLzLc4BvV9V3e2zbCHhIko2AhwI3DTUySZIkaUAbWjL+EuDc7sKq+j7wHuB7wM3Aj6rq4iHHJkmSJA1kg0nGk2wCvAD4Pz22bQ0cBuwKPBLYPMnLetRbnGRpkqW33X3PfIcsSZIkrdUGk4wDvwNcVVW3JNml44LOY4HfBr5TVT+oqp8D5wO/1b2DqjqtqiaqamLbLR465PAlSZKk+9uQ1hk/inaKSlXdCOwzvSHJbwL7JXko8BOaueVLRxGkJEmSNFMbxMh4m2QfRDPi/Suq6krgE8BVwLU053Xa0AKUJEmSZmGDGBmvqnuAbddRZxKYXFsdSZIkaZxsECPjkiRJ0gPRBjEyPh823v6R/Nprp0YdhiRJkhYwR8YlSZKkETEZlyRJkkZkwU5T+ckPvsU1//CCUYehMbL3az416hAkSdIC48i4JEmSNCIbRDKe5A1JViRZnuTcJJv1qPP6dvuKJMePIk5JkiRpEGOfjCfZCfhTYKKq9gIeDLykq85ewB8B+wK/ARyaZPdhxypJkiQNYuyT8dZGwEOSbAQ8FLipa/vjgSuq6p6qWgN8ATh8yDFKkiRJAxn7ZLyqvg+8B/gecDPwo6q6uKvacuCAJNsmeShwCLDLcCOVJEmSBjP2yXiSrYHDgF2BRwKbJ3lZZ52qWgn8LXAJ8FngamBNj30tTrI0ydI77r533mOXJEmS1mbsk3Hgt4HvVNUPqurnwPnAc5Isa3+OBaiq06vqyVV1AHA7cH33jqrqtKqaqKqJrbfYZKgnIUmSJHXbENYZ/x6wXzv95CfAc4ClVXVMZ6UkO1TV6iSPAo4A9h9+qJIkSdLMjX0yXlVXJvkEcBXN1JOvAaf1qHpekm2BnwPHVdUdQwxTkiRJGtjYJ+MAVTUJTK6jzjOGFI4kSZI0JzaEOeOSJEnSA9IGMTI+Hx6y/W7s/ZpPjToMSZIkLWCOjEuSJEkjYjIuSZIkjciCnaZy563Xc9Hph4w6DEnSBu55r75w1CFI2oA5Mi5JkiSNyNgk40len2R5khVJju9T54wkq5Ms7yrfJsklSa5vH7ceTtSSJEnS7I1FMp5kL+CPgH2B3wAOTbJ7j6pnAQf3KH8TcGlV7Q5c2r6WJEmSxtpYJOPA44ErquqeqloDfAE4vLtSVV0G3N6j/WHA2e3zs4EXzlegkiRJ0lwZl2R8OXBAkm2TPBQ4BNhlgPY7VtXNAO3jDvMQoyRJkjSnxmI1lapameRvgUuAu4GrgTVzfZwki4HFADtss9lc716SJEkayLiMjFNVp1fVk6vqAJqpKD9Msqz9OXYdzW9J8giA9nF1n2OcVlUTVTXx8IdtMrcnIEmSJA1obJLxJDu0j48CjgDeX1X7tD8fWkfzTwFHt8+PBj45f5FKkiRJc2NsknHgvCRfB/4dOK6q7uiukORc4HJgjySrkry63fQ3wEFJrgcOal9LkiRJY20s5owDVNUzZlDnqD7ltwHPmfOgJEmSpHk0TiPjkiRJ0oIyNiPjw7bldrvzvFdfOOowJEmStIA5Mi5JkiSNiMm4JEmSNCILdprKbbddz0fOet6ow5AkSQvYy1950ahD0Ig5Mi5JkiSNyFCT8SRnJFmdZHlH2TZJLklyffu4dZ+2RyZZkeS+JBMd5dsm+VySu5OcOozzkCRJkubCsEfGzwIO7ip7E3BpVe0OXNq+7mU5zZ05L+sq/ynwNuCNcxemJEmSNP+GmoxX1WXA7V3FhwFnt8/PBl7Yp+3KqvpGj/IfV9UXaZJySZIkaYMxDnPGd6yqmwHaxx1GHI8kSZI0FOOQjA9NksVJliZZetdd9446HEmSJC1w45CM35LkEQDt4+r2+ZlJliWZs9tkVtVpVTVRVRMPe9gmc7VbSZIkaVbGYZ3xTwFHA3/TPn4SoKqOGWVQkiRJ0nwb9tKG5wKXA3skWZXk1TRJ+EFJrgcOal/3ant4klXA/sAFSS7q2HYDcArwyna/e87zqUiSJEnrbagj41V1VJ9Nz5lB2yXAkj7bFq1HWJIkSdJIjMOccUmSJGlBGoc54yOx7ba78/JXXrTuipIkSdI8cWRckiRJGhGTcUmSJGlEFuw0lVtuv56/O/d5ow5DkiRJ8+yEo8Z3arIj45IkSdKIDHud8TOSrE6yvKPsyCQrktyXZGItbXvWS3JQkq8mubZ9fPZ8n4ckSZI0F4Y9Mn4WcHBX2XLgCOCydbTtV+9W4Her6ok0d/D8yPqHKUmSJM2/Yd/057Iki7rKVgIkWVfbnvWq6msdL1cAmyXZtKp+tv4RS5IkSfPngTZn/EXA10zEJUmStCF4wKymkuQJwN8Cz11LncXAYoCtt9tsSJFJkiRJvY3tyHiSM5MsS3LhDOruDCwBXlFV3+5Xr6pOq6qJqprY/GGbzGW4kiRJ0sDGdmS8qo6ZSb0kWwEXACdV1ZfmNypJkiRp7gx7acNzgcuBPZKsSvLqJIcnWQXsD1yQpOeq7Gup9zpgN+Bt7Uj6siQ7DOF0JEmSpPUy7NVUjuqzackM2i7pVa+q/hL4y/UMTZIkSRq6sZ0zLkmSJD3Qje2c8fm24za7c8JRPWfESJIkSUPhyLgkSZI0IibjkiRJ0ogs2GkqN/zweo5ZcvCow5AkSRpLZx7+2VGHsCA4Mi5JkiSNyLDXGT8jyeokyzvK3p3kuiTXJFnS3sSnV9sjk6xIcl+SiY7yfTvWF786yeHDOBdJkiRpfQ17ZPwsoHtuyCXAXlW1N/BN4KQ+bZcDRwCX9SifqKp92n3/Y5IFO/1GkiRJG46hJuNVdRlwe1fZxVW1pn15BbBzn7Yrq+obPcrv6Wi/GVBzGLIkSZI0b8ZtzvirgM8M2ijJbyZZAVwLHNuRnEuSJElja2yS8SRvAdYA5wzatqqurKonAE8FTkqyWZ9jLE6yNMnSn9557/oFLEmSJK2nsUjGkxwNHAq8tKqqLTuzvSjzwpnup6pWAj8G9uqz/bSqmqiqic223GQuQpckSZJmbeQXOiY5GDgReGZV3TNdXlXHzLD9rsCNVbUmyaOBPYAb5iNWSZIkaS4Ne2nDc4HLgT2SrEryauBU4GHAJe1I+If6tD08ySpgf+CCJBe1m54OXJ1kGbAEeG1V3TrvJyNJkiStp6GOjFfVUT2KT59h2yU0yXZ3+UeAj6xnaJIkSdLQjcWccUmSJGkhGvmc8VFZtNXunHn4Z0cdhiRJkhYwR8YlSZKkETEZlyRJkkZkwU5Tuf6HN3HIv71t1GFIkqQHoAtf+I5Rh6ANhCPjkiRJ0ogMe53xM5KsTrK8o+wdSa5p1xi/OMkj+7Q9MsmKJPclmeix/VFJ7k7yxvk8B0mSJGmuDHtk/Czg4K6yd1fV3lW1D/Bp4O192i4HjgAu67P9vcBn5iJISZIkaRiGfdOfy5Is6iq7s+Pl5kD1absSIMmvbEvyQuC/gB/PUaiSJEnSvBuLCziTvBN4BfAj4FkDtt0cOBE4CHCKiiRJkjYYY3EBZ1W9pap2Ac4BXjdg8yngvVV197oqJlmcZGmSpffe6SC6JEmSRmsskvEOHwVeBJDkzPaizgvX0eY3gXcluQE4Hnhzkp4JfVWdVlUTVTWxyZabz2XckiRJ0sBGPk0lye5VdX378gXAdQBVdcxM2lfVMzr2dTJwd1WdOtdxSpIkSXNtqMl4knOBA4HtkqwCJoFDkuwB3Ad8Fzi2T9vDgQ8A2wMXJFlWVc8bSuCSJEnSPFjvZDzJrwO/Dny5qm5aW92qOqpH8ekzOU5VLQGWrKPOyTPZlyRJkjQOBpoznuQfk3yo4/WLgWuB84HrkvzWHMcnSZIkPWClquey3r0rJ98FTqqqj7avvwlcAfw5zRSSbarqOfMR6FybmJiopUuXjjoMSZIkPcAl+WpV/cod5GHw1VR2AG5sd7o7sBvwrqr6b+A04EnrE6gkSZK0kAyajN8O7Ng+/23gv6tqefs6wIPnKjBJkiTpgW7QCzg/A/xFkh1ppqZ8vGPbXsANcxTXvLv+hz/g+ef//ajDkCRJC8QFR7x21CFoDA06Mn4CzRzxY4HLgLd3bDsc+OwcxSVJkiQ94M14ZDzJxjSj32+rqu93b++8+Y4kSZKkdRtkZPwXwP+jWVN8VpKckWR1kuU9tr0xSSXZrk/bI5OsSHJfkomO8kVJfpJkWfvzoV7tJUmSpHEz42S8qu4DrueXF3DOxlnAwd2FSXYBDgK+t5a2y4EjaKbHdPt2Ve3T/vS8g6ckSZI0bgadM/4W4O1Jnjibg1XVZTQrsnR7L80FoX0XPa+qlVX1jdkcV5IkSRpHg66m8lZgW2BZku8Dt9CVQFfVvoPsMMkLgO9X1dVJBgznf+ya5GvAncBbq+o/+hxrMbAYYLPttpntsSRJkqQ5MWgyvrz9mRNJHkoz2v7c9djNzcCjquq2JE8B/i3JE6rqzu6KVXUazc2JePhuj575rUclSZKkeTBQMl5Vx8zx8R8L7ApMj4rvDFyVZF/gr2nu6HlTVR2ylph+Bvysff7VJN8GHgd4r3tJkiSNtUFHxudUVV0L7DD9OskNwERV3QrMKPFPsj1we1X9IsljgN2B/5qHcCVJkqQ5NVAynuTj66pTVb+/lvbnAgcC2yVZBUxW1ekzPPbhwAeA7YELkiyrqucBB9DcFXQNzfKLx1ZVr4tEJUmSpLEy6Mj49j3KtgH2AG4D1rraSVUdtY7ti9aybQmwpEf5ecB5a9uvJEmSNI4GnTP+rF7l7TrhS2iWKNwg7L7V9lxwxGtHHYYkSZIWsEHXGe+pqm6kueDyXXOxP0mSJGkhmJNkvPULmtVQJEmSJM3AoBdw7tmjeBPg8cA7gK/MRVDD8K07bvQAKdUAAB3NSURBVOfQT5wz6jAkSZq1T//eS0cdgqT1NJub/vS6WU5oEvE/XO+IJEmSpAVi0GS81wWcPwVWVdX35yAeSZIkacEYaM54VX2hx8+VM03Ek5yRZHWS5R1lJyf5fpJl7U/Pu20mOTLJiiT3JZno2rZ3ksvb7dcm2WyQ85IkSZJGYeA7cCbZCHgR8HSaNcZvB/4DOL+q1qyj+VnAqcCHu8rfW1XvWUfb5cARwD/2iOdfgJdX1dVJtgV+PoNTkSRJkkZq0As4dwAuBvYGbgBuAfYHjgOuTvLcqvpBv/ZVdVmSRbMJtKpWtjF0b3oucE1VXd3Wu202+5ckSZKGbdClDU8BtgV+s6oeU1X7V9VjgN9sy0+ZZRyvS3JNO41l6wHbPg6oJBcluSrJn/ermGRxkqVJlt57552zDFWSJEmaG4Mm44cAJ1bV/ZYwbF+fBDx/FjH8A/BYYB/gZuDvBmy/Ec2UmZe2j4cneU6vilV1WlVNVNXEJltuOYtQJUmSpLkzaDK+KXBXn2130aw5PpCquqWqflFV9wH/BOwLkOTM9oLOC9exi1XAF6rq1qq6B7gQePKgcUiSJEnDNmgyfgVwYpLNOwvb1ye22weS5BEdLw+nuVCTqjqmqvapqp6rq3S4CNg7yUPbizmfCXx90DgkSZKkYRt0NZUTgM8BNya5mOYCzh2A59Hc+OfAtTVOcm5bZ7skq4BJ4MAk+9DcTOgG4I/7tD0c+ACwPXBBkmVV9byquiPJKTQ3HSrgwqq6YMDzkiRJkoZuoGS8qpYleRxNUv5UmlVVbgY+BJxSVbeuo/1RPYpPn+GxlwBL+mz7F5rlDSVJkqQNRqp63d3+gW9iYqKWLl066jAkSZL0AJfkq1U10WvbOkfGk0xP/5iRqtp3gNgkSZKkBWsm01RWcP9kPMArgE8D3mBHkiRJmqV1JuNV9crO1+2KJa8ATq6qq+YpLkmSJOkBb9DVVGCAKSvj7Ft3/IgXfOLTow5D0gPAp37v0FGHIEnaQA26zrgkSZKkOTLUZDzJGUlWJ1neVf4nSb6RZEWSd/Vpe2S7/b4kEx3lL23v1Dn9c1+7brkkSZI01tYnGZ/NdJWzgIM7C5I8CzgM2LuqngC8p0/b5cARwGX3C6LqnPZOnfsALwduqKpls4hNkiRJGqqZLG34A3on3pcmWdNdWFU79NtXVV2WZFFX8WuAv6mqn7V1Vvdpu7KNZ23hHgWcu7YKkiRJ0riYyQWcH2R+L9p8HPCMJO8Efgq8saq+Mst9vZhmlF2SJEkaezNZ2vDkIcSwNbAf8FTg40keUwPeGjTJbwL3VNXytdRZDCwGeMh2288+YkmSJGkOjMNqKquA86vxZeA+YLskZ7YXZF44w/28hHVMUamq06pqoqomNtny4esZtiRJkrR+ZrPO+Fz7N+DZwOeTPA7YBLi1qo6Z6Q6SPAg4EjhgfkKUJEmS5t6wlzY8F7gc2CPJqiSvBs4AHtMud/ivwNG9pqgkOTzJKmB/4IIkF3VsPgBYVVX/Nf9nIUmSJM2NoY6MV9VRfTa9bAZtlwBL+mz7PM2cc0mSJGmDMQ5zxiVJkqQFaRzmjI/Ebls/nE/93qGjDkOSJEkLmCPjkiRJ0oiYjEuSJEkjsmCnqXz7jh9zxHlXjDqMDcb5L/L6WEmSpLnmyLgkSZI0IsNeZ/yMJKvbNcWnyz7W3mlzWZIbkizr0/bIJCuS3JdkoqN84yRnJ7k2ycokJw3jXCRJkqT1NeyR8bOAgzsLqurFVbVPVe0DnAec36ftcuAI4LKu8iOBTavqicBTgD9OsmgOY5YkSZLmxbBv+nNZv0Q5SYDfB57dp+3Ktt6vbAI2T7IR8BDgXuDOuYlYkiRJmj/jNGf8GcAtVXX9gO0+AfwYuBn4HvCeqrp9roOTJEmS5to4JeNHAefOot2+wC+ARwK7AickeUyvikkWJ1maZOnP7vzh7COVJEmS5sBYJOPtFJMjgI91lJ3ZXtR54Tqa/wHw2ar6eVWtBr4ETPSqWFWnVdVEVU1suuVWcxW+JEmSNCtjkYwDvw1cV1Wrpguq6pj2ws5D1tH2e8Cz09gc2A+4bh5jlSRJkubEsJc2PBe4HNgjyaokr243vYR1TFFJcniSVcD+wAVJLmo3fRDYgma1la8AZ1bVNfNyApIkSdIcGvZqKkf1KX/lDNouAZb0KL+bZnlDSZIkaYMyLtNUJEmSpAVnqCPj4+SxW2/O+S/ab9RhSJIkaQFzZFySJEkaEZNxSZIkaUQW7DSVG394L3+65Mb12sf7D99ljqKRJEnSQuTIuCRJkjQiw15n/Iwkq5Ms7yjbJ8kV7d02lybZt0/bI5OsSHJfkomO8k3au3Vem+TqJAcO4VQkSZKk9TbskfGzgIO7yt4FTFXVPsDb29e9LAeOAC7rKv8jgKp6InAQ8HdJHPGXJEnS2Btq0lpVlwG3dxcDW7bPHw7c1Kftyqr6Ro9NewKXtnVWAz8EJnrUkyRJksbKOFzAeTxwUZL30Hw4+K0B218NHJbkX4FdgKe0j1+e0yglSZKkOTYO0zleA7yhqnYB3gCcPmD7M4BVwFLgfcB/Amt6VUyyuJ2XvvQnd3YP0EuSJEnDNQ7J+NHA+e3z/wPsC9BelLksyYVra1xVa6rqDVW1T1UdBmwFXN+n7mlVNVFVEw/Zcps5PAVJkiRpcOMwTeUm4JnA54Fn0ybSVXXMTBoneSiQqvpxkoOANVX19XmKVZIkSZozQ03Gk5wLHAhsl2QVMEmzGsr/TrIR8FNgcZ+2hwMfALYHLkiyrKqeB+xAM+f8PuD7wMvn/UQkSZKkOTDUZLyqjuqz6SkzaLsEWNKj/AZgj/WLTJIkSRq+cZgzLkmSJC1I4zBnfCR22WoT3n/4LqMOQ5IkSQuYI+OSJEnSiJiMS5IkSSOyYKep3H7HGs497wfrrHfUi7YfQjSSJElaiBwZlyRJkkZkqMl4kjOSrE6yvKPsN5JcnuTaJP+eZMs+bd+d5Lok1yRZkmSrjm0nJflWkm8ked4wzkWSJElaX8MeGT8LOLir7J+BN1XVE2nWEf+zPm0vAfaqqr2BbwInASTZE3gJ8IR233+f5MFzH7okSZI0t4aajFfVZcDtXcV7AJe1zy8BXtSn7cVVtaZ9eQWwc/v8MOBfq+pnVfUd4FvAvnMauCRJkjQPxmHO+HLgBe3zI4GZLP79KuAz7fOdgBs7tq1qyyRJkqSxNg7J+KuA45J8FXgYcO/aKid5C7AGOGe6qEe16tN2cZKlSZbededt6xGyJEmStP5GvrRhVV0HPBcgyeOA57fPzwSeBNxUVYe0ZUcDhwLPqarphHsV9x9N3xm4qc+xTgNOA3jMY/fpmbBLkiRJwzLyZDzJDlW1OsmDgLcCHwKoqmO66h0MnAg8s6ru6dj0KeCjSU4BHgnsDnx5KMFLkiRJ62GoyXiSc4EDge2SrAImgS2SHNdWOR84s0/zU4FNgUuSAFxRVcdW1YokHwe+TjN95biq+sU8noYkSZI0J4aajFfVUX02/e8ZtN1tLdveCbxztnFJkiRJozAOF3BKkiRJC9LI54yPyjZbb8RRL9p+1GFIkiRpAXNkXJIkSRoRk3FJkiRpRBbsNJW7b1vDlz78g/uVPe0VTluRJEnS8DgyLkmSJI3IUJPxJLsk+VySlUlWJHl9W75NkkuSXN8+bt2n/buTXJfkmiRLkmzVlm/b7vfuJKcO85wkSZKk2Rr2yPga4ISqejywH3Bckj2BNwGXVtXuwKXt614uAfaqqr2BbwInteU/Bd4GvHE+g5ckSZLm0lCT8aq6uaquap/fBawEdgIOA85uq50NvLBP+4urak378gpg57b8x1X1RZqkXJIkSdogjGzOeJJFwJOAK4Edq+pmaBJ2YIcZ7OJVwGfmKz5JkiRpvo0kGU+yBXAecHxV3TmL9m+hmfJyzoDtFidZmmTpD++6bdDDSpIkSXNq6Ml4ko1pEvFzqur8tviWJI9otz8CWN0+PzPJsiQXdrQ/GjgUeGlV1SDHrqrTqmqiqia2eti2c3E6kiRJ0qwNdZ3xJAFOB1ZW1Skdmz4FHA38Tfv4SYCqOqar/cHAicAzq+qeoQQtSZIkzZNh3/TnacDLgWuTLGvL3kyThH88yauB7wFH9ml/KrApcEmT13NFVR0LkOQGYEtgkyQvBJ5bVV+frxORJEmS1tdQk/F2xZP02fycGbTfbS3bFs0yLEmSJGkkvAOnJEmSNCLDnqYyNrbYdiOe9ortRx2GJEmSFjBHxiVJkqQRMRmXJEmSRmTBJuP33vLzUYcgSZKkBW7BJuOSJEnSqA01GU+yS5LPJVmZZEWS17flR7av70sysZb2705yXZJrkixJslVbflCSrya5tn189rDOSZIkSZqtYY+MrwFOqKrHA/sBxyXZE1gOHAFcto72lwB7VdXewDeBk9ryW4Hfraon0tzB8yPzEbwkSZI0l4Z905+bgZvb53clWQnsVFWXALR31Vxb+4s7Xl4B/F5b/rWO8hXAZkk2raqfzWH4kiRJ0pwa2ZzxJIuAJwFXznIXrwI+06P8RcDXTMQlSZI07kZy058kWwDnAcdX1Z2zaP8Wmikv53SVPwH4W+C5fdotBhYDPHLrnQY9rCRJkjSnhj4ynmRjmkT8nKo6fx11z0yyLMmFHWVHA4cCL62q6ijfGVgCvKKqvt1rf1V1WlVNVNXEtptvOxenI0mSJM3aUEfG00wKPx1YWVWnrKt+VR3T1f5g4ETgmVV1T0f5VsAFwElV9aW5jVqSJEmaH8MeGX8a8HLg2e2I97IkhyQ5PMkqYH/ggiQX9Wl/KvAw4JK27Yfa8tcBuwFv69jvDvN9MpIkSdL6SMdMjwVl711+o6658epRhyFJkqQHuCRfraqe99LxDpySJEnSiCzYZHyTHTcedQiSJEla4BZsMi5JkiSNmsm4JEmSNCILNhn/+S0/4Zb3XjPqMCRJkrSALdhkXJIkSRq1oSbjSXZJ8rkkK5OsSPL6tvzdSa5Lck2SJe1NfHq171kvyb4d64tfneTwYZ6XJEmSNBvDHhlfA5xQVY8H9gOOS7IncAmwV1XtDXwTOKlP+371lgMTVbUPcDDwj0mGendRSZIkaVBDTcar6uaquqp9fhewEtipqi6uqjVttSuAnfu071mvqu7pKN8MWJh3MpIkSdIGZWRzxpMsAp4EXNm16VXAZ2awi/vVS/KbSVYA1wLHdiTnkiRJ0lgaSTKeZAvgPOD4qrqzo/wtNFNZzllH+1+pV1VXVtUTgKcCJyXZrEe7xUmWJll6+4/vmJuTkSRJkmZp6Ml4ko1pEvFzqur8jvKjgUOBl1ZVtWVnthdlXri2ep2qaiXwY2CvHttOq6qJqprYZvOt5/rUJEmSpIEM9SLHJAFOB1ZW1Skd5QcDJwLPrKp7psur6piu9j3rJdkVuLGq1iR5NLAHcMN8noskSZK0voa94sjTgJcD1yZZ1pa9GXg/sClwSZOvc0VVHduj/al96j0deFOSnwP3Aa+tqlvn9UwkSZKk9TTUZLyqvgikx6YLe5T1ar9bn/KPAB9Zj9AkSZKkofMOnJIkSdKILNhkfOMdH8KOb9h71GFIkiRpAVuwybgkSZI0aibjkiRJ0ogs2GR8zeo7111JkiRJmkcLNhmXJEmSRm2oyXiSXZJ8LsnKJCuSvL4tf0eSa9q7bV6c5JF92r87yXVt3SVJtura/qgkdyd54zDOR5IkSVofwx4ZXwOcUFWPB/YDjkuyJ/Duqtq7qvYBPg28vU/7S4C9qmpv4JvASV3b3wt8Zn5ClyRJkubWUJPxqrq5qq5qn98FrAR2qqrOCdybA9Wn/cVVtaZ9eQWw8/S2JC8E/gtYMR+xS5IkSXNtqHfg7JRkEfAk4Mr29TuBVwA/Ap41g128CvhY23Zz4ETgIMApKpIkSdogjOQCziRbAOcBx0+PilfVW6pqF+Ac4HXraP8Wmikv57RFU8B7q+rudbRbnGRpkqW33f2j9T0NSZIkab2kqueMkPk7YLIxzbzwi6rqlB7bHw1cUFV7JTmTZvT8pqo6pN1+NHAs8Jyquqct+w9gl3YXWwH3AW+vqlP7xbHPox5Xy773zTk8M0mSJOlXJflqVU302jbUaSpJApwOrOxMxJPsXlXXty9fAFwHUFXHdLU/mGY6yjOnE/G23jM66pwM3L22RFySJEkaB8OeM/404OXAtUmWtWVvBl6dZA+aEe3v0ox893IqsClwSZPXc0VV9asrSZIkjbWhJuNV9UUgPTZdOMP2u82gzskDhiVJkiSNhHfglCRJkkZkwSbjG+2w5ahDkCRJ0gK3YJNxSZIkadSGvrThuEhyF/CNUcehnrYDbh11EOrJvhlf9s14sl/Gl30zvh6IffPoqtq+14aR3YFzDHyj33qPGq0kS+2b8WTfjC/7ZjzZL+PLvhlfC61vnKYiSZIkjYjJuCRJkjQiCzkZP23UAagv+2Z82Tfjy74ZT/bL+LJvxteC6psFewGnJEmSNGoLeWRckiRJGqkFmYwnOTjJN5J8K8mbRh3PQpPkhiTXJlmWZGlbtk2SS5Jc3z5u3VH/pLavvpHkeaOL/IEnyRlJVidZ3lE2cF8keUrbp99K8v4kGfa5PND06ZuTk3y/fe8sS3JIxzb7ZgiS7JLkc0lWJlmR5PVtue+bEVtL3/i+GbEkmyX5cpKr276Zast93wBU1YL6AR4MfBt4DLAJcDWw56jjWkg/wA3Adl1l7wLe1D5/E/C37fM92z7aFNi17bsHj/ocHig/wAHAk4Hl69MXwJeB/YEAnwF+Z9TntqH/9Ombk4E39qhr3wyvXx4BPLl9/jDgm+3v3/fN+PaN75vR902ALdrnGwNXAvv5vml+FuLI+L7At6rqv6rqXuBfgcNGHJOaPji7fX428MKO8n+tqp9V1XeAb9H0oeZAVV0G3N5VPFBfJHkEsGVVXV7Nv5Qf7mijWerTN/3YN0NSVTdX1VXt87uAlcBO+L4ZubX0TT/2zZBU4+725cbtT+H7BliY01R2Am7seL2Ktb9ZNfcKuDjJV5Msbst2rKqbofkHFdihLbe/hm/Qvtipfd5drvnxuiTXtNNYpr/StW9GIMki4Ek0o3y+b8ZIV9+A75uRS/LgJMuA1cAlVeX7prUQk/Fec4tcUma4nlZVTwZ+BzguyQFrqWt/jY9+fWEfDc8/AI8F9gFuBv6uLbdvhizJFsB5wPFVdefaqvYos2/mUY++8X0zBqrqF1W1D7AzzSj3XmupvqD6ZiEm46uAXTpe7wzcNKJYFqSquql9XA0soZl2ckv79RPt4+q2uv01fIP2xar2eXe55lhV3dL+h3Yf8E/8csqWfTNESTamSfbOqarz22LfN2OgV9/4vhkvVfVD4PPAwfi+ARZmMv4VYPckuybZBHgJ8KkRx7RgJNk8ycOmnwPPBZbT9MHRbbWjgU+2zz8FvCTJpkl2BXanuXhD82egvmi/WrwryX7tVe2v6GijOTT9n1brcJr3Dtg3Q9P+Hk8HVlbVKR2bfN+MWL++8X0zekm2T7JV+/whwG8D1+H7BoCNRh3AsFXVmiSvAy6iWVnljKpaMeKwFpIdgSXtSkQbAR+tqs8m+Qrw8SSvBr4HHAlQVSuSfBz4OrAGOK6qfjGa0B94kpwLHAhsl2QVMAn8DYP3xWuAs4CH0Fzd/pkhnsYDUp++OTDJPjRfy94A/DHYN0P2NODlwLXt/FeAN+P7Zhz065ujfN+M3COAs5M8mGYg+ONV9ekkl+P7xjtwSpIkSaOyEKepSJIkSWPBZFySJEkaEZNxSZIkaURMxiVJkqQRMRmXJEmSRsRkXJIGlOSVSa5M8uMkdyb5QpIXrOc+D01S7W28H9CSHNie69ruwNev7Q1J3jMfcUnSKJiMS9IAkvwD8M/AlcALgRfTrF38ySQnjjA0SdIGaMHd9EeSZivJC4FjgddU1Yc6Nn0myX8Df5Xkkqq6qk/7jYH75vPGVUkeUlU/ma/9L3TtXf82raqfjjoWSQ8MjoxL0sy9HvgW8E89tv0VcBfwuumCJJ9P8okki5N8G/gp8Mg0Tk6yOsldST4MbNm9wySbJXlXkhuT/CzJ1UkO6apzQ5K/S/K29k6dd7blT0jy2SS3t9NpViY5bm0nl+RBSd6U5Fvt8b6Z5OiuOs9Pckkb+51Jrkjy3B772jvJvyf5YZK7k3w5yUFd1bZL8n/a7f+V5LVri69r/29IsirJHUn+dfpW2x3bd03yb22Md7Wx7NaxfVE7VebQrnZnJVna8frkJLcmeXp7p+Cf0t4lUJLmgiPjkjQDSTYC9gf+vtfIdlX9KMnngAO6Nj0NeCxwInAP8CPgT4G30yTw/wEcAbyrx2E/AewLTALfBn4f+FSSiapa1lHvD4AVwGv55b/rnwKuA14G/AzYgx4Jf5cPAEcDfwFcBRwEnJHktqr6dFtnV+DfgfcA9wG/Q/PNwAFV9SWAJL8OfAn4Bs03CbcBE8AuXcf7J+Bs4DTgKOCDSZZW1ZfXEefvA9cAi4GdgVNofpevbY+/KXAp8HPgj2hupz0FfCHJE6vq9nXsv9tD2zjfBXwTuGnA9pLUl8m4JM3MdsCmwHfXUue7wMFdZVsBT6qq/wZI8mCaxPwfq+qtbZ2LklwC7DTdKMlzgOcDB1bVF9rii5M8jv/f3v2FWFVFcRz/rhJxoIxqsKbMoQghhVCKHkRBSoKoUEaoHpOMsiL6R6RSJBRlJBRY2IOSD1G+ZEZhNY1KJCHZP4IoCiEc0wb/VNSDzjSrh7WPnPace+9xmPG8/D5wmbvP2Xuffe68rLPv2vvCWsbOzt5WpE6YWTdwFbDM3b9P5wfa3VyaNV4FrHD3renwp2bWQzwMfADg7htLbc4BdgNzgXuIAJxU/09gUSllpr/ism+7+3Oprz3A7cSDSadgfDjd20hqOwe4ixSMAyuAWcBsdz+Q6uwDDgD3AS906D/XBTzm7jvOsJ2ISEdKUxERmVxfFYF4cgXQA+SB3btZeQlwBNhrZlOKFxFUX5/VHchymI8DB4FNZnanmc2oMc6biJnu7RXXm5ceIjCzmWa21cwOETPOw8DNwOxSXzcC22rkrn9SvHH3YeBnYqa7k91FIJ78AMwws6mpfAPwdRGIp/4HiYeFhTX6zzmwcxztREQ60sy4iEg9R4l0j942dXqBQ9mx37PypenvUHY8L3enusMV18nTZP53DXcfTXnczwNbgC4z2ws87O7ftBh7N3AuMaNdpcfMfiPSX84n0mx+Af4h0lrKAf/FwOEW/ZT9kZVPAdPG2c6Aqel9D2M/d9Kxdv+/Vk64+6lxtBMR6UjBuIhIDe4+YmZfALea2RPuPlo+b2bTgcXA9rxpVi5myfPZ6rx8nAjsl9UZXsV4fwSWpx1cFgHrgQ/NbGY+9tL1Rogc96rzQ8DVwHzgFnf/qDhhZl1Z3WNEQNyUw0TqTO4S4j4hFmJCBPBlF1W0G/P5iohMFKWpiIjU9yqRjrGy4txTxALJjRXnyg4SAfnS7HhfVh4gZsb/dvf9+avugN192N13EYsce4gc9iq7iJnxC6qul2aGi6D7ZNHIzHqJAD4f+x1mVmeWezLsA64zsyuLA2Z2ObAA+DwdGiK+dbimVOc8YpGuiMhZo5lxEZGa3P09M9tE7Poxh1jUOIX44Z+7gdWt9hgv9fGvmb0EvGxmR4ndVJZTCgqTfuBjoN/M1hO7pUwH5gHT3H11q2uY2bXEbifbiEWLFxKLRr9rtZOIu/+U7u2dNL79RMrIXGIh5Epid5ZBYIOZPU2kq6xjbGrOOuBL4DMz20DMlM8Hjrn7lnafzwR5k7jfnWb2DJHW8yyRavQGnE7l2QE8ama/EqkvjwPao11EzioF4yIiZ+YBYuZ1FbFt3iixDeBSd3+/Zh+vEOkQ9wOPEHnYTwJvFRXc3c2sD1iT6swiUiy+JbYgbOcIkR+9FriMCDR3EwFqOw8SW/fdS+SB/0UsjtycxnQyjek1YtvFQSIvfTFw+qftU2C/EHiR+LVSUj9rOlx/QqRxLiG+DdhM5JPvAfqyh5GHiG0VXwdOEPeygNK9iIhMNnNXKpyIiIiISBOUMy4iIiIi0hAF4yIiIiIiDVEwLiIiIiLSEAXjIiIiIiINUTAuIiIiItIQBeMiIiIiIg1RMC4iIiIi0hAF4yIiIiIiDVEwLiIiIiLSkP8Ar/VC2aoB5bAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hourTran = df.groupby('hour')['Transaction'].count().reset_index()\n",
    "hourTran.loc[:,\"hourorder\"] = [1,10,11,12,13,14,15,16,17,18,19,20,21,22,23,7,8,9]\n",
    "hourTran.sort_values(\"hourorder\",inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(data = hourTran, x = \"Transaction\", y = \"hour\")\n",
    "plt.ylabel('Hours', size = 15)\n",
    "plt.xlabel('Orders each hour', size = 15)\n",
    "plt.title('Count of orders received each hour', color = 'green', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.03766,
     "end_time": "2020-11-25T19:29:54.030816",
     "exception": false,
     "start_time": "2020-11-25T19:29:53.993156",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "People order more during the afternoon, since there are a lot of maximum order percentage between 12-5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:54.119033Z",
     "iopub.status.busy": "2020-11-25T19:29:54.118079Z",
     "iopub.status.idle": "2020-11-25T19:29:54.387915Z",
     "shell.execute_reply": "2020-11-25T19:29:54.387237Z"
    },
    "papermill": {
     "duration": 0.317743,
     "end_time": "2020-11-25T19:29:54.388044",
     "exception": false,
     "start_time": "2020-11-25T19:29:54.070301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dayTran = df.groupby('period_day')['Transaction'].count().reset_index()\n",
    "# dayTran.loc[:,\"hourorder\"] = [1,10,11,12,13,14,15,16,17,18,19,20,21,22,23,7,8,9]\n",
    "# dayTran.sort_values(\"hourorder\",inplace=True)\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.barplot(data = dayTran, x = \"Transaction\", y = \"period_day\")\n",
    "plt.ylabel('Period', size = 15)\n",
    "plt.xlabel('Orders each period of a day', size = 15)\n",
    "plt.title('Count of orders received each period of a day', color = 'green', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038275,
     "end_time": "2020-11-25T19:29:54.465911",
     "exception": false,
     "start_time": "2020-11-25T19:29:54.427636",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "People prefer to order in the morning and afternoon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:54.557584Z",
     "iopub.status.busy": "2020-11-25T19:29:54.556721Z",
     "iopub.status.idle": "2020-11-25T19:29:54.611536Z",
     "shell.execute_reply": "2020-11-25T19:29:54.610755Z"
    },
    "papermill": {
     "duration": 0.107085,
     "end_time": "2020-11-25T19:29:54.611665",
     "exception": false,
     "start_time": "2020-11-25T19:29:54.504580",
     "status": "completed"
    },
    "tags": []
   },
   "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>Transaction</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>275</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>2016-11-19</td>\n",
       "      <td>209</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2016-12-11</td>\n",
       "      <td>221</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>2017-01-28</td>\n",
       "      <td>237</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>2017-02-18</td>\n",
       "      <td>227</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>2017-03-25</td>\n",
       "      <td>246</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1415</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>292</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1707</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>257</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>205</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>2017-11-03</td>\n",
       "      <td>203</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  Transaction    weekday\n",
       "0     2016-05-11          275  Wednesday\n",
       "275   2016-11-19          209   Saturday\n",
       "484   2016-12-11          221     Sunday\n",
       "705   2017-01-28          237   Saturday\n",
       "942   2017-02-18          227   Saturday\n",
       "1169  2017-03-25          246   Saturday\n",
       "1415  2017-04-02          292     Sunday\n",
       "1707  2017-04-03          257     Monday\n",
       "1964  2017-08-04          205     Friday\n",
       "2169  2017-11-03          203     Friday"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = df.groupby('date')['Transaction'].count().reset_index()\n",
    "dates = dates[dates['Transaction']>=200].sort_values('date').reset_index(drop = True)\n",
    "\n",
    "dates = pd.merge(dates, df[['date','weekday']], on = 'date', how = 'inner')\n",
    "dates.drop_duplicates(inplace =True)\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.039713,
     "end_time": "2020-11-25T19:29:54.691109",
     "exception": false,
     "start_time": "2020-11-25T19:29:54.651396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Mostly transactions are on weekends, as we saw earlier in our graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:54.783599Z",
     "iopub.status.busy": "2020-11-25T19:29:54.782632Z",
     "iopub.status.idle": "2020-11-25T19:29:55.577265Z",
     "shell.execute_reply": "2020-11-25T19:29:55.577869Z"
    },
    "papermill": {
     "duration": 0.846918,
     "end_time": "2020-11-25T19:29:55.578048",
     "exception": false,
     "start_time": "2020-11-25T19:29:54.731130",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df.groupby(['period_day','Item'])['Transaction'].count().reset_index().sort_values(['period_day','Transaction'],ascending=False)\n",
    "day = ['morning','afternoon','evening','night']\n",
    "\n",
    "plt.figure(figsize=(15,8))\n",
    "for i,j in enumerate(day):\n",
    "    plt.subplot(2,2,i+1)\n",
    "    df1 = data[data.period_day==j].head(10)\n",
    "    sns.barplot(data=df1, y=df1.Item, x=df1.Transaction, color='pink')\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel('')\n",
    "    plt.title('Top 10 items people like to order in \"{}\"'.format(j), size=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045562,
     "end_time": "2020-11-25T19:29:55.665317",
     "exception": false,
     "start_time": "2020-11-25T19:29:55.619755",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Apriori Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:55.758573Z",
     "iopub.status.busy": "2020-11-25T19:29:55.757790Z",
     "iopub.status.idle": "2020-11-25T19:29:55.775917Z",
     "shell.execute_reply": "2020-11-25T19:29:55.775130Z"
    },
    "papermill": {
     "duration": 0.064835,
     "end_time": "2020-11-25T19:29:55.776042",
     "exception": false,
     "start_time": "2020-11-25T19:29:55.711207",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import association_rules, apriori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:55.866697Z",
     "iopub.status.busy": "2020-11-25T19:29:55.865876Z",
     "iopub.status.idle": "2020-11-25T19:29:55.896768Z",
     "shell.execute_reply": "2020-11-25T19:29:55.896058Z"
    },
    "papermill": {
     "duration": 0.078685,
     "end_time": "2020-11-25T19:29:55.896894",
     "exception": false,
     "start_time": "2020-11-25T19:29:55.818209",
     "status": "completed"
    },
    "tags": []
   },
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18882</th>\n",
       "      <td>9682</td>\n",
       "      <td>tacos/fajita</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18883</th>\n",
       "      <td>9682</td>\n",
       "      <td>tea</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18884</th>\n",
       "      <td>9683</td>\n",
       "      <td>coffee</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18885</th>\n",
       "      <td>9683</td>\n",
       "      <td>pastry</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18886</th>\n",
       "      <td>9684</td>\n",
       "      <td>smoothies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18887 rows × 3 columns</p>\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\n",
       "...            ...            ...    ...\n",
       "18882         9682   tacos/fajita      1\n",
       "18883         9682            tea      1\n",
       "18884         9683         coffee      1\n",
       "18885         9683         pastry      1\n",
       "18886         9684      smoothies      1\n",
       "\n",
       "[18887 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions_str = df.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')\n",
    "transactions_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:55.992845Z",
     "iopub.status.busy": "2020-11-25T19:29:55.992062Z",
     "iopub.status.idle": "2020-11-25T19:29:56.080112Z",
     "shell.execute_reply": "2020-11-25T19:29:56.080677Z"
    },
    "papermill": {
     "duration": 0.140185,
     "end_time": "2020-11-25T19:29:56.080840",
     "exception": false,
     "start_time": "2020-11-25T19:29:55.940655",
     "status": "completed"
    },
    "tags": []
   },
   "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>Item</th>\n",
       "      <th>adjustment</th>\n",
       "      <th>afternoon with the baker</th>\n",
       "      <th>alfajores</th>\n",
       "      <th>argentina night</th>\n",
       "      <th>art tray</th>\n",
       "      <th>bacon</th>\n",
       "      <th>baguette</th>\n",
       "      <th>bakewell</th>\n",
       "      <th>bare popcorn</th>\n",
       "      <th>basket</th>\n",
       "      <th>...</th>\n",
       "      <th>the bart</th>\n",
       "      <th>the nomad</th>\n",
       "      <th>tiffin</th>\n",
       "      <th>toast</th>\n",
       "      <th>truffles</th>\n",
       "      <th>tshirt</th>\n",
       "      <th>valentine's card</th>\n",
       "      <th>vegan feast</th>\n",
       "      <th>vegan mincepie</th>\n",
       "      <th>victorian sponge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction</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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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        0.0              0.0   \n",
       "2                   0.0                       0.0        0.0              0.0   \n",
       "3                   0.0                       0.0        0.0              0.0   \n",
       "4                   0.0                       0.0        0.0              0.0   \n",
       "5                   0.0                       0.0        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       0.0           0.0     0.0  ...   \n",
       "2                 0.0    0.0       0.0       0.0           0.0     0.0  ...   \n",
       "3                 0.0    0.0       0.0       0.0           0.0     0.0  ...   \n",
       "4                 0.0    0.0       0.0       0.0           0.0     0.0  ...   \n",
       "5                 0.0    0.0       0.0       0.0           0.0     0.0  ...   \n",
       "\n",
       "Item         the bart  the nomad  tiffin  toast  truffles  tshirt  \\\n",
       "Transaction                                                         \n",
       "1                 0.0        0.0     0.0    0.0       0.0     0.0   \n",
       "2                 0.0        0.0     0.0    0.0       0.0     0.0   \n",
       "3                 0.0        0.0     0.0    0.0       0.0     0.0   \n",
       "4                 0.0        0.0     0.0    0.0       0.0     0.0   \n",
       "5                 0.0        0.0     0.0    0.0       0.0     0.0   \n",
       "\n",
       "Item         valentine's card  vegan feast  vegan mincepie  victorian sponge  \n",
       "Transaction                                                                   \n",
       "1                         0.0          0.0             0.0               0.0  \n",
       "2                         0.0          0.0             0.0               0.0  \n",
       "3                         0.0          0.0             0.0               0.0  \n",
       "4                         0.0          0.0             0.0               0.0  \n",
       "5                         0.0          0.0             0.0               0.0  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a mxn matrice where m=transaction and n=items and each row represents whether the item was in the transaction or not\n",
    "my_basket = transactions_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0)\n",
    "\n",
    "\n",
    "my_basket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:56.176193Z",
     "iopub.status.busy": "2020-11-25T19:29:56.175333Z",
     "iopub.status.idle": "2020-11-25T19:29:56.970094Z",
     "shell.execute_reply": "2020-11-25T19:29:56.969463Z"
    },
    "papermill": {
     "duration": 0.844479,
     "end_time": "2020-11-25T19:29:56.970234",
     "exception": false,
     "start_time": "2020-11-25T19:29:56.125755",
     "status": "completed"
    },
    "tags": []
   },
   "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>Item</th>\n",
       "      <th>adjustment</th>\n",
       "      <th>afternoon with the baker</th>\n",
       "      <th>alfajores</th>\n",
       "      <th>argentina night</th>\n",
       "      <th>art tray</th>\n",
       "      <th>bacon</th>\n",
       "      <th>baguette</th>\n",
       "      <th>bakewell</th>\n",
       "      <th>bare popcorn</th>\n",
       "      <th>basket</th>\n",
       "      <th>...</th>\n",
       "      <th>the bart</th>\n",
       "      <th>the nomad</th>\n",
       "      <th>tiffin</th>\n",
       "      <th>toast</th>\n",
       "      <th>truffles</th>\n",
       "      <th>tshirt</th>\n",
       "      <th>valentine's card</th>\n",
       "      <th>vegan feast</th>\n",
       "      <th>vegan mincepie</th>\n",
       "      <th>victorian sponge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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  \\\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         valentine's card  vegan feast  vegan mincepie  victorian sponge  \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",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a function which returns 0 or 1\n",
    "# 0 means item was not in that transaction, 1 means item present in that transaction\n",
    "\n",
    "def encode(x):\n",
    "    if x<=0:\n",
    "        return 0\n",
    "    if x>=1:\n",
    "        return 1\n",
    "\n",
    "# applying the function to the dataset\n",
    "\n",
    "my_basket_sets = my_basket.applymap(encode)\n",
    "my_basket_sets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:57.068118Z",
     "iopub.status.busy": "2020-11-25T19:29:57.067309Z",
     "iopub.status.idle": "2020-11-25T19:29:57.176146Z",
     "shell.execute_reply": "2020-11-25T19:29:57.175346Z"
    },
    "papermill": {
     "duration": 0.161198,
     "end_time": "2020-11-25T19:29:57.176274",
     "exception": false,
     "start_time": "2020-11-25T19:29:57.015076",
     "status": "completed"
    },
    "tags": []
   },
   "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>0.036344</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>(toast, coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.014369</td>\n",
       "      <td>(tea, sandwich)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(cake, bread, coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.011199</td>\n",
       "      <td>(pastry, bread, coffee)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.010037</td>\n",
       "      <td>(cake, tea, coffee)</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          (toast, coffee)\n",
       "57  0.014369          (tea, sandwich)\n",
       "58  0.010037    (cake, bread, coffee)\n",
       "59  0.011199  (pastry, bread, coffee)\n",
       "60  0.010037      (cake, tea, coffee)\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the 'apriori algorithm' with min_support=0.01 (1% of 9465)\n",
    "# It means the item should be present in atleast 94 transaction out of 9465 transactions only when we considered that item in\n",
    "# frequent itemset\n",
    "frequent_items = apriori(my_basket_sets, min_support = 0.01,use_colnames = True)\n",
    "frequent_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:57.274339Z",
     "iopub.status.busy": "2020-11-25T19:29:57.273556Z",
     "iopub.status.idle": "2020-11-25T19:29:57.349108Z",
     "shell.execute_reply": "2020-11-25T19:29:57.348468Z"
    },
    "papermill": {
     "duration": 0.127753,
     "end_time": "2020-11-25T19:29:57.349237",
     "exception": false,
     "start_time": "2020-11-25T19:29:57.221484",
     "status": "completed"
    },
    "tags": []
   },
   "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>30</th>\n",
       "      <td>(toast)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(medialuna)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(alfajores)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(juice)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(scone)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(cookies)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(brownie)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.024293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(muffin)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.489011</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.020777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.338650</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.017305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(tea, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(cake, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(bread, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(bread, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.114399</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.011905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(bread, coffee)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.111502</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.008606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(pastry)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.099382</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.014740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(bread, coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(tea, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(bread)</td>\n",
       "      <td>(pastry)</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.089119</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.003306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.079947</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.008807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(medialuna)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.073542</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.012667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(tea)</td>\n",
       "      <td>(cake, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.061837</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.003749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(cookies)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.058966</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.004841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(juice)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.004705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(brownie)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.001080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(alfajores)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.004936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(muffin)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(scone)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.037765</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.003343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "30           (toast)          (coffee)            0.033597   \n",
       "28  (spanish brunch)          (coffee)            0.018172   \n",
       "18       (medialuna)          (coffee)            0.061807   \n",
       "22          (pastry)          (coffee)            0.086107   \n",
       "0        (alfajores)          (coffee)            0.036344   \n",
       "16           (juice)          (coffee)            0.038563   \n",
       "24        (sandwich)          (coffee)            0.071844   \n",
       "6             (cake)          (coffee)            0.103856   \n",
       "26           (scone)          (coffee)            0.034548   \n",
       "12         (cookies)          (coffee)            0.054411   \n",
       "15   (hot chocolate)          (coffee)            0.058320   \n",
       "5          (brownie)          (coffee)            0.040042   \n",
       "21          (muffin)          (coffee)            0.038457   \n",
       "2           (pastry)           (bread)            0.086107   \n",
       "10            (cake)             (tea)            0.103856   \n",
       "39     (tea, coffee)            (cake)            0.049868   \n",
       "33        (sandwich)             (tea)            0.071844   \n",
       "9    (hot chocolate)            (cake)            0.058320   \n",
       "38    (cake, coffee)             (tea)            0.054728   \n",
       "11             (tea)            (cake)            0.142631   \n",
       "37          (pastry)   (bread, coffee)            0.086107   \n",
       "36   (bread, coffee)          (pastry)            0.090016   \n",
       "7           (coffee)            (cake)            0.478394   \n",
       "34   (bread, coffee)            (cake)            0.090016   \n",
       "8             (cake)   (hot chocolate)            0.103856   \n",
       "32             (tea)        (sandwich)            0.142631   \n",
       "23          (coffee)          (pastry)            0.478394   \n",
       "35            (cake)   (bread, coffee)            0.103856   \n",
       "40            (cake)     (tea, coffee)            0.103856   \n",
       "3            (bread)          (pastry)            0.327205   \n",
       "25          (coffee)        (sandwich)            0.478394   \n",
       "19          (coffee)       (medialuna)            0.478394   \n",
       "41             (tea)    (cake, coffee)            0.142631   \n",
       "14          (coffee)   (hot chocolate)            0.478394   \n",
       "13          (coffee)         (cookies)            0.478394   \n",
       "31          (coffee)           (toast)            0.478394   \n",
       "17          (coffee)           (juice)            0.478394   \n",
       "4           (coffee)         (brownie)            0.478394   \n",
       "1           (coffee)       (alfajores)            0.478394   \n",
       "20          (coffee)          (muffin)            0.478394   \n",
       "27          (coffee)           (scone)            0.478394   \n",
       "29          (coffee)  (spanish brunch)            0.478394   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "30            0.478394  0.023666    0.704403  1.472431  0.007593    1.764582  \n",
       "28            0.478394  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "18            0.478394  0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "22            0.478394  0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "0             0.478394  0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "16            0.478394  0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "24            0.478394  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "6             0.478394  0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "26            0.478394  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "12            0.478394  0.028209    0.518447  1.083723  0.002179    1.083174  \n",
       "15            0.478394  0.029583    0.507246  1.060311  0.001683    1.058553  \n",
       "5             0.478394  0.019651    0.490765  1.025860  0.000495    1.024293  \n",
       "21            0.478394  0.018806    0.489011  1.022193  0.000408    1.020777  \n",
       "2             0.327205  0.029160    0.338650  1.034977  0.000985    1.017305  \n",
       "10            0.142631  0.023772    0.228891  1.604781  0.008959    1.111865  \n",
       "39            0.103856  0.010037    0.201271  1.937977  0.004858    1.121962  \n",
       "33            0.142631  0.014369    0.200000  1.402222  0.004122    1.071712  \n",
       "9             0.103856  0.011410    0.195652  1.883874  0.005354    1.114125  \n",
       "38            0.142631  0.010037    0.183398  1.285822  0.002231    1.049923  \n",
       "11            0.103856  0.023772    0.166667  1.604781  0.008959    1.075372  \n",
       "37            0.090016  0.011199    0.130061  1.444872  0.003448    1.046033  \n",
       "36            0.086107  0.011199    0.124413  1.444872  0.003448    1.043749  \n",
       "7             0.103856  0.054728    0.114399  1.101515  0.005044    1.011905  \n",
       "34            0.103856  0.010037    0.111502  1.073621  0.000688    1.008606  \n",
       "8             0.058320  0.011410    0.109868  1.883874  0.005354    1.057910  \n",
       "32            0.071844  0.014369    0.100741  1.402222  0.004122    1.032134  \n",
       "23            0.086107  0.047544    0.099382  1.154168  0.006351    1.014740  \n",
       "35            0.090016  0.010037    0.096643  1.073621  0.000688    1.007336  \n",
       "40            0.049868  0.010037    0.096643  1.937977  0.004858    1.051779  \n",
       "3             0.086107  0.029160    0.089119  1.034977  0.000985    1.003306  \n",
       "25            0.071844  0.038246    0.079947  1.112792  0.003877    1.008807  \n",
       "19            0.061807  0.035182    0.073542  1.189878  0.005614    1.012667  \n",
       "41            0.054728  0.010037    0.070370  1.285822  0.002231    1.016827  \n",
       "14            0.058320  0.029583    0.061837  1.060311  0.001683    1.003749  \n",
       "13            0.054411  0.028209    0.058966  1.083723  0.002179    1.004841  \n",
       "31            0.033597  0.023666    0.049470  1.472431  0.007593    1.016699  \n",
       "17            0.038563  0.020602    0.043065  1.116750  0.002154    1.004705  \n",
       "4             0.040042  0.019651    0.041078  1.025860  0.000495    1.001080  \n",
       "1             0.036344  0.019651    0.041078  1.130235  0.002264    1.004936  \n",
       "20            0.038457  0.018806    0.039311  1.022193  0.000408    1.000888  \n",
       "27            0.034548  0.018067    0.037765  1.093107  0.001539    1.003343  \n",
       "29            0.018172  0.010882    0.022747  1.251766  0.002189    1.004682  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now making the rules from frequent itemset generated above\n",
    "\n",
    "rules = association_rules(frequent_items, metric = \"lift\", min_threshold = 1)\n",
    "rules.sort_values('confidence', ascending = False, inplace = True)\n",
    "rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-25T19:29:57.474896Z",
     "iopub.status.busy": "2020-11-25T19:29:57.465873Z",
     "iopub.status.idle": "2020-11-25T19:29:57.518873Z",
     "shell.execute_reply": "2020-11-25T19:29:57.518114Z"
    },
    "papermill": {
     "duration": 0.121366,
     "end_time": "2020-11-25T19:29:57.519004",
     "exception": false,
     "start_time": "2020-11-25T19:29:57.397638",
     "status": "completed"
    },
    "tags": []
   },
   "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>30</th>\n",
       "      <td>(toast)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.033597</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(medialuna)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.569231</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.210871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.552147</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.164682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(alfajores)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.540698</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.135648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(juice)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.119919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.532353</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.115384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.526958</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.102664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(scone)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.522936</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.093366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(cookies)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.518447</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.083174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.507246</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.058553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(brownie)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.490765</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.024293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(muffin)</td>\n",
       "      <td>(coffee)</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.489011</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.020777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(bread)</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.338650</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.017305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(tea, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(cake, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(pastry)</td>\n",
       "      <td>(bread, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(bread, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.054728</td>\n",
       "      <td>0.114399</td>\n",
       "      <td>1.101515</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1.011905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(bread, coffee)</td>\n",
       "      <td>(cake)</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.111502</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.008606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(pastry)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.047544</td>\n",
       "      <td>0.099382</td>\n",
       "      <td>1.154168</td>\n",
       "      <td>0.006351</td>\n",
       "      <td>1.014740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(bread, coffee)</td>\n",
       "      <td>0.103856</td>\n",
       "      <td>0.090016</td>\n",
       "      <td>0.010037</td>\n",
       "      <td>0.096643</td>\n",
       "      <td>1.073621</td>\n",
       "      <td>0.000688</td>\n",
       "      <td>1.007336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(cake)</td>\n",
       "      <td>(tea, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(bread)</td>\n",
       "      <td>(pastry)</td>\n",
       "      <td>0.327205</td>\n",
       "      <td>0.086107</td>\n",
       "      <td>0.029160</td>\n",
       "      <td>0.089119</td>\n",
       "      <td>1.034977</td>\n",
       "      <td>0.000985</td>\n",
       "      <td>1.003306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(sandwich)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.071844</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.079947</td>\n",
       "      <td>1.112792</td>\n",
       "      <td>0.003877</td>\n",
       "      <td>1.008807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(medialuna)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.061807</td>\n",
       "      <td>0.035182</td>\n",
       "      <td>0.073542</td>\n",
       "      <td>1.189878</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>1.012667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(tea)</td>\n",
       "      <td>(cake, coffee)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(hot chocolate)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.058320</td>\n",
       "      <td>0.029583</td>\n",
       "      <td>0.061837</td>\n",
       "      <td>1.060311</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>1.003749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(cookies)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.028209</td>\n",
       "      <td>0.058966</td>\n",
       "      <td>1.083723</td>\n",
       "      <td>0.002179</td>\n",
       "      <td>1.004841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(juice)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038563</td>\n",
       "      <td>0.020602</td>\n",
       "      <td>0.043065</td>\n",
       "      <td>1.116750</td>\n",
       "      <td>0.002154</td>\n",
       "      <td>1.004705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(brownie)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.040042</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.025860</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>1.001080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(alfajores)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.019651</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>1.130235</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>1.004936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(muffin)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.038457</td>\n",
       "      <td>0.018806</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>1.022193</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>1.000888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(coffee)</td>\n",
       "      <td>(scone)</td>\n",
       "      <td>0.478394</td>\n",
       "      <td>0.034548</td>\n",
       "      <td>0.018067</td>\n",
       "      <td>0.037765</td>\n",
       "      <td>1.093107</td>\n",
       "      <td>0.001539</td>\n",
       "      <td>1.003343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         antecedents       consequents  antecedent support  \\\n",
       "30           (toast)          (coffee)            0.033597   \n",
       "28  (spanish brunch)          (coffee)            0.018172   \n",
       "18       (medialuna)          (coffee)            0.061807   \n",
       "22          (pastry)          (coffee)            0.086107   \n",
       "0        (alfajores)          (coffee)            0.036344   \n",
       "16           (juice)          (coffee)            0.038563   \n",
       "24        (sandwich)          (coffee)            0.071844   \n",
       "6             (cake)          (coffee)            0.103856   \n",
       "26           (scone)          (coffee)            0.034548   \n",
       "12         (cookies)          (coffee)            0.054411   \n",
       "15   (hot chocolate)          (coffee)            0.058320   \n",
       "5          (brownie)          (coffee)            0.040042   \n",
       "21          (muffin)          (coffee)            0.038457   \n",
       "2           (pastry)           (bread)            0.086107   \n",
       "10            (cake)             (tea)            0.103856   \n",
       "39     (tea, coffee)            (cake)            0.049868   \n",
       "33        (sandwich)             (tea)            0.071844   \n",
       "9    (hot chocolate)            (cake)            0.058320   \n",
       "38    (cake, coffee)             (tea)            0.054728   \n",
       "11             (tea)            (cake)            0.142631   \n",
       "37          (pastry)   (bread, coffee)            0.086107   \n",
       "36   (bread, coffee)          (pastry)            0.090016   \n",
       "7           (coffee)            (cake)            0.478394   \n",
       "34   (bread, coffee)            (cake)            0.090016   \n",
       "8             (cake)   (hot chocolate)            0.103856   \n",
       "32             (tea)        (sandwich)            0.142631   \n",
       "23          (coffee)          (pastry)            0.478394   \n",
       "35            (cake)   (bread, coffee)            0.103856   \n",
       "40            (cake)     (tea, coffee)            0.103856   \n",
       "3            (bread)          (pastry)            0.327205   \n",
       "25          (coffee)        (sandwich)            0.478394   \n",
       "19          (coffee)       (medialuna)            0.478394   \n",
       "41             (tea)    (cake, coffee)            0.142631   \n",
       "14          (coffee)   (hot chocolate)            0.478394   \n",
       "13          (coffee)         (cookies)            0.478394   \n",
       "31          (coffee)           (toast)            0.478394   \n",
       "17          (coffee)           (juice)            0.478394   \n",
       "4           (coffee)         (brownie)            0.478394   \n",
       "1           (coffee)       (alfajores)            0.478394   \n",
       "20          (coffee)          (muffin)            0.478394   \n",
       "27          (coffee)           (scone)            0.478394   \n",
       "29          (coffee)  (spanish brunch)            0.478394   \n",
       "\n",
       "    consequent support   support  confidence      lift  leverage  conviction  \n",
       "30            0.478394  0.023666    0.704403  1.472431  0.007593    1.764582  \n",
       "28            0.478394  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
       "18            0.478394  0.035182    0.569231  1.189878  0.005614    1.210871  \n",
       "22            0.478394  0.047544    0.552147  1.154168  0.006351    1.164682  \n",
       "0             0.478394  0.019651    0.540698  1.130235  0.002264    1.135648  \n",
       "16            0.478394  0.020602    0.534247  1.116750  0.002154    1.119919  \n",
       "24            0.478394  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
       "6             0.478394  0.054728    0.526958  1.101515  0.005044    1.102664  \n",
       "26            0.478394  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
       "12            0.478394  0.028209    0.518447  1.083723  0.002179    1.083174  \n",
       "15            0.478394  0.029583    0.507246  1.060311  0.001683    1.058553  \n",
       "5             0.478394  0.019651    0.490765  1.025860  0.000495    1.024293  \n",
       "21            0.478394  0.018806    0.489011  1.022193  0.000408    1.020777  \n",
       "2             0.327205  0.029160    0.338650  1.034977  0.000985    1.017305  \n",
       "10            0.142631  0.023772    0.228891  1.604781  0.008959    1.111865  \n",
       "39            0.103856  0.010037    0.201271  1.937977  0.004858    1.121962  \n",
       "33            0.142631  0.014369    0.200000  1.402222  0.004122    1.071712  \n",
       "9             0.103856  0.011410    0.195652  1.883874  0.005354    1.114125  \n",
       "38            0.142631  0.010037    0.183398  1.285822  0.002231    1.049923  \n",
       "11            0.103856  0.023772    0.166667  1.604781  0.008959    1.075372  \n",
       "37            0.090016  0.011199    0.130061  1.444872  0.003448    1.046033  \n",
       "36            0.086107  0.011199    0.124413  1.444872  0.003448    1.043749  \n",
       "7             0.103856  0.054728    0.114399  1.101515  0.005044    1.011905  \n",
       "34            0.103856  0.010037    0.111502  1.073621  0.000688    1.008606  \n",
       "8             0.058320  0.011410    0.109868  1.883874  0.005354    1.057910  \n",
       "32            0.071844  0.014369    0.100741  1.402222  0.004122    1.032134  \n",
       "23            0.086107  0.047544    0.099382  1.154168  0.006351    1.014740  \n",
       "35            0.090016  0.010037    0.096643  1.073621  0.000688    1.007336  \n",
       "40            0.049868  0.010037    0.096643  1.937977  0.004858    1.051779  \n",
       "3             0.086107  0.029160    0.089119  1.034977  0.000985    1.003306  \n",
       "25            0.071844  0.038246    0.079947  1.112792  0.003877    1.008807  \n",
       "19            0.061807  0.035182    0.073542  1.189878  0.005614    1.012667  \n",
       "41            0.054728  0.010037    0.070370  1.285822  0.002231    1.016827  \n",
       "14            0.058320  0.029583    0.061837  1.060311  0.001683    1.003749  \n",
       "13            0.054411  0.028209    0.058966  1.083723  0.002179    1.004841  \n",
       "31            0.033597  0.023666    0.049470  1.472431  0.007593    1.016699  \n",
       "17            0.038563  0.020602    0.043065  1.116750  0.002154    1.004705  \n",
       "4             0.040042  0.019651    0.041078  1.025860  0.000495    1.001080  \n",
       "1             0.036344  0.019651    0.041078  1.130235  0.002264    1.004936  \n",
       "20            0.038457  0.018806    0.039311  1.022193  0.000408    1.000888  \n",
       "27            0.034548  0.018067    0.037765  1.093107  0.001539    1.003343  \n",
       "29            0.018172  0.010882    0.022747  1.251766  0.002189    1.004682  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arranging the data from highest to lowest with respect to 'confidence'\n",
    "\n",
    "rules.sort_values('confidence', ascending=False)"
   ]
  }
 ],
 "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": "3.7.6"
  },
  "papermill": {
   "duration": 14.730779,
   "end_time": "2020-11-25T19:29:57.675008",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-11-25T19:29:42.944229",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}