CapstoneProject2020x.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ec2-user/SageMaker'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the .csv into a pandas dataframe from S3 bucket\n",
    "engine = pd.read_csv('s3://togunniyi-bstn-bucket29/recommendationengine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>User_Rating</th>\n",
       "      <th>Rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1GI0U4ZRJA8WN</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1334707200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1QGNMC6O1VW39</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1397433600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A3J3BRHTDRFJ2G</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1397433600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A2TY0BTJOTENPG</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395878400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A34ATBPOK6HCHY</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395532800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A89DO69P0XZ27</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1395446400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AZYNQZ94U6VDB</td>\n",
       "      <td>0511189877</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1401321600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A1DA3W4GTFXP6O</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1405641600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A29LPQQDG7LD5J</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1352073600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AO94DHGC771SJ</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1370131200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AMO214LNFCEI4</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1290643200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>A28B1G1MSJ6OO1</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1280016000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>A3N7T0DY83Y4IG</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1283990400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>A1H8PY3QHMQQA0</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1290556800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A2CPBQ5W4OGBX</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1277078400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>A265MKAR2WEH3Y</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1294790400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>A37K02NKUIT68K</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1293235200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>A2AW1SSVUIYV9Y</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1289001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>A2AEHUKOV014BP</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1284249600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AMLFNXUIEMN4T</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1307836800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>A2O8FIJR9EBU56</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1278547200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>A3IQGFB959IR4P</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1327363200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AYTBGUX49LF3W</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1398470400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>A24QFSUU00IZ05</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1293580800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>A1NG5X8VYZWX0Q</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1286582400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A1E4WG8HRWWK4R</td>\n",
       "      <td>0528881469</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1390867200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           User_ID  Product_ID  User_Rating  Rating_timestamp\n",
       "0   A2CX7LUOHB2NDG  0321732944          5.0        1341100800\n",
       "1   A2NWSAGRHCP8N5  0439886341          1.0        1367193600\n",
       "2   A2WNBOD3WNDNKT  0439886341          3.0        1374451200\n",
       "3   A1GI0U4ZRJA8WN  0439886341          1.0        1334707200\n",
       "4   A1QGNMC6O1VW39  0511189877          5.0        1397433600\n",
       "5   A3J3BRHTDRFJ2G  0511189877          2.0        1397433600\n",
       "6   A2TY0BTJOTENPG  0511189877          5.0        1395878400\n",
       "7   A34ATBPOK6HCHY  0511189877          5.0        1395532800\n",
       "8    A89DO69P0XZ27  0511189877          5.0        1395446400\n",
       "9    AZYNQZ94U6VDB  0511189877          5.0        1401321600\n",
       "10  A1DA3W4GTFXP6O  0528881469          5.0        1405641600\n",
       "11  A29LPQQDG7LD5J  0528881469          1.0        1352073600\n",
       "12   AO94DHGC771SJ  0528881469          5.0        1370131200\n",
       "13   AMO214LNFCEI4  0528881469          1.0        1290643200\n",
       "14  A28B1G1MSJ6OO1  0528881469          4.0        1280016000\n",
       "15  A3N7T0DY83Y4IG  0528881469          3.0        1283990400\n",
       "16  A1H8PY3QHMQQA0  0528881469          2.0        1290556800\n",
       "17   A2CPBQ5W4OGBX  0528881469          2.0        1277078400\n",
       "18  A265MKAR2WEH3Y  0528881469          4.0        1294790400\n",
       "19  A37K02NKUIT68K  0528881469          5.0        1293235200\n",
       "20  A2AW1SSVUIYV9Y  0528881469          4.0        1289001600\n",
       "21  A2AEHUKOV014BP  0528881469          5.0        1284249600\n",
       "22   AMLFNXUIEMN4T  0528881469          1.0        1307836800\n",
       "23  A2O8FIJR9EBU56  0528881469          4.0        1278547200\n",
       "24  A3IQGFB959IR4P  0528881469          1.0        1327363200\n",
       "25   AYTBGUX49LF3W  0528881469          4.0        1398470400\n",
       "26  A24QFSUU00IZ05  0528881469          2.0        1293580800\n",
       "27  A1NG5X8VYZWX0Q  0528881469          1.0        1286582400\n",
       "28  A1E4WG8HRWWK4R  0528881469          5.0        1390867200"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first 29 rows of the dataset\n",
    "engine.head(29)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project Part 1: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(599999, 4)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of rows and columns in the dataframe\n",
    "engine.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns of the dataframe\n",
    "engine.rename(columns = {'AKM1MP6P0OYPR':'User_ID', '0132793040':'Product_ID',\\\n",
    "                             '5.0':'User_Rating','1365811200':'Rating_timestamp'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>User_Rating</th>\n",
       "      <th>Rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          User_ID  Product_ID  User_Rating  Rating_timestamp\n",
       "0  A2CX7LUOHB2NDG  0321732944          5.0        1341100800\n",
       "1  A2NWSAGRHCP8N5  0439886341          1.0        1367193600\n",
       "2  A2WNBOD3WNDNKT  0439886341          3.0        1374451200"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 3 rows of the dataframe to make sure that the column name have been changed\n",
    "engine.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    599999.000000\n",
       "mean          4.022373\n",
       "std           1.372565\n",
       "min           1.000000\n",
       "25%           3.000000\n",
       "50%           5.000000\n",
       "75%           5.000000\n",
       "max           5.000000\n",
       "Name: User_Rating, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 point summary statistics for the User_Rating column\n",
    "engine.describe()['User_Rating'].T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 599999 entries, 0 to 599998\n",
      "Data columns (total 4 columns):\n",
      "User_ID             599999 non-null object\n",
      "Product_ID          599999 non-null object\n",
      "User_Rating         599999 non-null float64\n",
      "Rating_timestamp    599999 non-null int64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 18.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Overview of the dataframe including data types\n",
    "engine.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset is clean and there are no null values to fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID             0.0\n",
       "Product_ID          0.0\n",
       "User_Rating         0.0\n",
       "Rating_timestamp    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double checking for null values.\n",
    "#Display the number of null values in each column as a percentage\n",
    "round((engine.isna().sum()/len(engine))* 100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the dataset for duplicates.If present, drop duplicate rows.\n",
    "engine.drop_duplicates(keep='first', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(599999, 4)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of rows and columns in the dataframe after checking for duplicate rows\n",
    "engine.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are no duplicate rows in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496908"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find the number of unique users in the dataset\n",
    "engine['User_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58766"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of different products in the dataset\n",
    "engine['Product_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5489"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of unique time stamps\n",
    "engine['Rating_timestamp'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the maximum rating of a product by a particular user\n",
    "engine['User_Rating'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the minimum rating of a product by a user \n",
    "engine['User_Rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a horizontal bar chart to find the top 10 most popular products \n",
    "plt.figure(figsize=(10,6))\n",
    "sorted_counts = engine['Product_ID'].value_counts().head(10).sort_values()\n",
    "plt.barh(sorted_counts.index, sorted_counts.values, color ='cornflowerblue')\n",
    "plt.title('Product ID \"B00007E7JU\" is the most popular electronic product on Amazon', fontsize=17)\n",
    "plt.xlabel('Quantity of each category', fontsize = 17)\n",
    "plt.xticks(fontsize = 13)\n",
    "plt.ylabel('Product ID', fontsize = 17)\n",
    "plt.yticks(rotation=30, fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a bar chart of the user ratings \n",
    "plt.figure(figsize=(10,6))\n",
    "sorted_counts = engine['User_Rating'].value_counts().head(10).sort_values()\n",
    "plt.bar(sorted_counts.index, sorted_counts.values, color ='orange')\n",
    "plt.title('Most users gave a product rating of 5', fontsize=17)\n",
    "plt.xlabel('User Ratings', fontsize = 17)\n",
    "plt.xticks(fontsize = 13)\n",
    "plt.ylabel('Total number of ratings ', fontsize = 17)\n",
    "plt.yticks(fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B00HAFDFDM    1\n",
       "B00JMPQM92    1\n",
       "B00KV3B66I    1\n",
       "B00KYK1CKI    1\n",
       "B00KB49P1A    1\n",
       "B00J1TLTWE    1\n",
       "B00005O7FU    1\n",
       "B00K9553YE    1\n",
       "B00GISZN6E    1\n",
       "B00008N6S7    1\n",
       "B00FGP1H10    1\n",
       "B00EK5VD98    1\n",
       "B00ITP2DOO    1\n",
       "B00005LD4L    1\n",
       "B00GUT3OQM    1\n",
       "B00G0PM584    1\n",
       "B00JPMDRGS    1\n",
       "B00009R96Y    1\n",
       "B00EDTU176    1\n",
       "B00JMAWDLS    1\n",
       "B00ET21WNO    1\n",
       "B00FF1BTYK    1\n",
       "B00006JPHS    1\n",
       "B00L4OYGOC    1\n",
       "B00FPQ3BUA    1\n",
       "B00EDM1PXC    1\n",
       "B00G48EG0C    1\n",
       "B00GNKSHV0    1\n",
       "B00JRFLGFM    1\n",
       "B00GLH19BU    1\n",
       "Name: Product_ID, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the bottom 30 products\n",
    "engine['Product_ID'].value_counts().tail(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of exploratory data analysis (EDA) for my capstone project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project Part 2: Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 of my capstone project involves using a Market Basket Analysis approach to build a recommendation system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Market Basket Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a copy of the training set dataframe for using in implementing a Market basket analysis.\n",
    "basket= engine.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(599999, 4)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of rows and columns in the dataframe\n",
    "basket.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>User_Rating</th>\n",
       "      <th>Rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A2CX7LUOHB2NDG</td>\n",
       "      <td>0321732944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1341100800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2NWSAGRHCP8N5</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1367193600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WNBOD3WNDNKT</td>\n",
       "      <td>0439886341</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1374451200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          User_ID  Product_ID  User_Rating  Rating_timestamp\n",
       "0  A2CX7LUOHB2NDG  0321732944          5.0        1341100800\n",
       "1  A2NWSAGRHCP8N5  0439886341          1.0        1367193600\n",
       "2  A2WNBOD3WNDNKT  0439886341          3.0        1374451200"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 3 rows of the dataframe\n",
    "basket.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Due to the large size of the data set, and too many issues with memory error, I will use only 40,000 rows of my dataset moving forward to build a contigency table prior to fitting the Apriori algorithm and then association tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a slice of the dataframe that contains 40,000 rows\n",
    "cart = basket.iloc[60000:100000,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>User_Rating</th>\n",
       "      <th>Rating_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60000</th>\n",
       "      <td>A1HCQ95EXAD08E</td>\n",
       "      <td>B00004WHV7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>983577600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60001</th>\n",
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      ],
      "text/plain": [
       "              User_ID  Product_ID  User_Rating  Rating_timestamp\n",
       "60000  A1HCQ95EXAD08E  B00004WHV7          1.0         983577600\n",
       "60001  A2WVJ0KW0QKH0P  B00004WHV7          2.0         974678400\n",
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     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 3 rows of the dataframe\n",
    "cart.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          User_ID  Product_ID  User_Rating  Rating_timestamp\n",
       "0  A1HCQ95EXAD08E  B00004WHV7          1.0         983577600\n",
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     "execution_count": 219,
     "metadata": {},
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   ],
   "source": [
    "# Reset the index of the dataframe and show only the first 7 rows\n",
    "cart.reset_index(drop=True).head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each basket is a list of products purchased a unique user\n",
    "online_basket = cart.groupby(['User_ID']).apply(lambda x: list(x['Product_ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID\n",
       "A03279253KKB83JP34CU                             [B00004ZCAT]\n",
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       "A1030POF0X91A                                    [B00005BMSN]\n",
       "A1031B11CMV49B                                   [B000051ZOA]\n",
       "                                         ...                 \n",
       "AZWOQXRCS1WA6                        [B00004ZC8Y, B00004ZCJE]\n",
       "AZWX8YF8K2DB                                     [B000050MGD]\n",
       "AZWZR9SXC9GA3                                    [B00005ATZN]\n",
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       "AZZ1T3GIDUUJS                                    [B00005AW1H]\n",
       "AZZ77XYX8O2WE                                    [B00004ZCJE]\n",
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       "AZZF28AU48P18                                    [B00005AXHW]\n",
       "AZZJ7P2D1POK4                                    [B00005I9R3]\n",
       "AZZLKKDJSSSGL                                    [B000059ZYA]\n",
       "AZZREGNDJCXP8                                    [B0000501TJ]\n",
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       "Length: 37361, dtype: object"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the dataframe\n",
    "online_basket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "\n",
    "# Ignore warning about SparseDataFrame deprecation\n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The transaction encoder will be applied to the online baskets in order to create a sparse transaction format dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a transaction encoder\n",
    "transaction_encoder = TransactionEncoder()\n",
    "\n",
    "# Fit the encoder to the \"baskets\" object and transform it\n",
    "encoded_matrix = transaction_encoder.fit(online_basket).transform(online_basket, sparse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a sparse dataframe from the sparse matridx from previous step\n",
    "encoded_basket = pd.SparseDataFrame(encoded_matrix, columns=transaction_encoder.columns_, default_fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
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       "      <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>10</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>11</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>12</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>13</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>14</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>15</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>16</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>17</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>18</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>19</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>20</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>21</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>22</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>23</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>24</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>25</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>26</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>27</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>28</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>29</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37331</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>37332</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>37333</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>37334</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",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37360</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37361 rows × 2779 columns</p>\n",
       "</div>"
      ],
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       "...           ...         ...         ...         ...         ...         ...   \n",
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       "\n",
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       "20              0           0           0           0  ...           0   \n",
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       "29              0           0           0           0  ...           0   \n",
       "...           ...         ...         ...         ...  ...         ...   \n",
       "37331           0           0           0           0  ...           0   \n",
       "37332           0           0           0           0  ...           0   \n",
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       "37360           0           0           0           0  ...           0   \n",
       "\n",
       "       B00005K2S8  B00005K2SA  B00005K2SC  B00005K3AN  B00005K3KD  B00005K3KT  \\\n",
       "0               0           0           0           0           0           0   \n",
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       "24              0           0           0           0           0           0   \n",
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       "26              0           0           0           0           0           0   \n",
       "27              0           0           0           0           0           0   \n",
       "28              0           0           0           0           0           0   \n",
       "29              0           0           0           0           0           0   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "37331           0           0           0           0           0           0   \n",
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       "37360           0           0           0           0           0           0   \n",
       "\n",
       "       B00005K3LZ  B00005K47N  B00005K47X  \n",
       "0               0           0           0  \n",
       "1               0           0           0  \n",
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       "10              0           0           0  \n",
       "11              0           0           0  \n",
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       "13              0           0           0  \n",
       "14              0           0           0  \n",
       "15              0           0           0  \n",
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       "19              0           0           0  \n",
       "20              0           0           0  \n",
       "21              0           0           0  \n",
       "22              0           0           0  \n",
       "23              0           0           0  \n",
       "24              0           0           0  \n",
       "25              0           0           0  \n",
       "26              0           0           0  \n",
       "27              0           0           0  \n",
       "28              0           0           0  \n",
       "29              0           0           0  \n",
       "...           ...         ...         ...  \n",
       "37331           0           0           0  \n",
       "37332           0           0           0  \n",
       "37333           0           0           0  \n",
       "37334           0           0           0  \n",
       "37335           0           0           0  \n",
       "37336           0           0           0  \n",
       "37337           0           0           0  \n",
       "37338           0           0           0  \n",
       "37339           0           0           0  \n",
       "37340           0           0           0  \n",
       "37341           0           0           0  \n",
       "37342           0           0           0  \n",
       "37343           0           0           0  \n",
       "37344           0           0           0  \n",
       "37345           0           0           0  \n",
       "37346           0           0           0  \n",
       "37347           0           0           0  \n",
       "37348           0           0           0  \n",
       "37349           0           0           0  \n",
       "37350           0           0           0  \n",
       "37351           0           0           0  \n",
       "37352           0           0           0  \n",
       "37353           0           0           0  \n",
       "37354           0           0           0  \n",
       "37355           0           0           0  \n",
       "37356           0           0           0  \n",
       "37357           0           0           0  \n",
       "37358           0           0           0  \n",
       "37359           0           0           0  \n",
       "37360           0           0           0  \n",
       "\n",
       "[37361 rows x 2779 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the sparse dataframe\n",
    "encoded_basket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The one hot encoded sparse Dataframe above has a total of 37361 rows and 2779 columns. Each **column** header represents an item, while each **row** represents a basket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B00004WHV7     10\n",
       "B00004WHWD      2\n",
       "B00004WI43      9\n",
       "B00004WIM9      1\n",
       "B00004WIMB      1\n",
       "B00004WIMF      1\n",
       "B00004WIMM      1\n",
       "B00004WIMW     68\n",
       "B00004WIN3      1\n",
       "B00004WIN5      3\n",
       "B00004WINU      3\n",
       "B00004WJJZ      4\n",
       "B00004WJO4      5\n",
       "B00004WKU1      3\n",
       "B00004WLHH      2\n",
       "B00004WLJ2     58\n",
       "B00004WLJ4     36\n",
       "B00004WLJ5     28\n",
       "B00004WLJ6      3\n",
       "B00004WLJ7     31\n",
       "B00004WLJ8     20\n",
       "B00004WLJ9     68\n",
       "B00004WLL8      2\n",
       "B00004WLP7      1\n",
       "B00004WLQK      1\n",
       "B00004WLQX      2\n",
       "B00004WLQY     36\n",
       "B00004WYQR      5\n",
       "B00004WYQU      2\n",
       "B00004WZJM      2\n",
       "             ... \n",
       "B00005JIM3      1\n",
       "B00005JIMF      2\n",
       "B00005JIMH      4\n",
       "B00005JIML      1\n",
       "B00005JJEN      6\n",
       "B00005JJEO     20\n",
       "B00005JJES      2\n",
       "B00005JJRF      2\n",
       "B00005JJRJ      2\n",
       "B00005JJRK      1\n",
       "B00005JS8P      4\n",
       "B00005JS8S      2\n",
       "B00005JTB9      3\n",
       "B00005JTVV      3\n",
       "B00005JTW1      3\n",
       "B00005JXI6      2\n",
       "B00005K2EX      2\n",
       "B00005K2Q3      2\n",
       "B00005K2QY      2\n",
       "B00005K2R0     35\n",
       "B00005K2R1      1\n",
       "B00005K2S8      3\n",
       "B00005K2SA      3\n",
       "B00005K2SC      4\n",
       "B00005K3AN      4\n",
       "B00005K3KD      6\n",
       "B00005K3KT      1\n",
       "B00005K3LZ     21\n",
       "B00005K47N      1\n",
       "B00005K47X    390\n",
       "Length: 2779, dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the sum of each basket \n",
    "encoded_basket.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Prior to using the `Apriori` algorithm to determine **association rules**, a contigency table is created to visualize the top 10 items based on `support`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the top 10 items(products) and assign them to a variable (top_10_items) \n",
    "top_10_items = encoded_basket.sum(axis=0).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B00004ZCJE    2547\n",
       "B00005ATMB    1015\n",
       "B00004Z5M1     815\n",
       "B00005ARK3     795\n",
       "B00004ZC8Y     720\n",
       "B00005ATMI     597\n",
       "B00005A1K1     522\n",
       "B000051ZOA     410\n",
       "B00005K47X     390\n",
       "B000058AKE     363\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for the top_10_items\n",
    "top_10_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</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>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>13</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</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>17</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>26</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37331</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>37332</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37333</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37334</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37335</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37336</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37337</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37338</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37339</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37340</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37341</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37342</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37343</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37344</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37345</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37346</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37347</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37348</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "<p>37361 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       B00004ZCJE  B00005ATMB  B00004Z5M1  B00005ARK3  B00004ZC8Y  B00005ATMI  \\\n",
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       "...           ...         ...         ...         ...         ...         ...   \n",
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       "\n",
       "[37361 rows x 10 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the encoded dataframe (i.e. encoded_basket) to obtain only the top 10 items\n",
    "top_10_dataframe = encoded_basket[top_10_items.index]\n",
    "\n",
    "# Check the dataframe\n",
    "top_10_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>B00004ZCJE</th>\n",
       "      <th>B00005ATMB</th>\n",
       "      <th>B00004Z5M1</th>\n",
       "      <th>B00005ARK3</th>\n",
       "      <th>B00004ZC8Y</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00004Z5M1</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>815</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>B00005ARK3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>795</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>B00004ZC8Y</th>\n",
       "      <td>97</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005ATMI</th>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005A1K1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>522</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "            B00004ZCJE  B00005ATMB  B00004Z5M1  B00005ARK3  B00004ZC8Y  \\\n",
       "B00004ZCJE        2547           0           6           0          97   \n",
       "B00005ATMB           0        1015           2           0           0   \n",
       "B00004Z5M1           6           2         815           0           0   \n",
       "B00005ARK3           0           0           0         795           0   \n",
       "B00004ZC8Y          97           0           0           0         720   \n",
       "B00005ATMI           2          11           1           1           0   \n",
       "B00005A1K1           1           0           0           0           0   \n",
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       "B00005K47X          18           0           0           0           4   \n",
       "B000058AKE           0           0           0           0           0   \n",
       "\n",
       "            B00005ATMI  B00005A1K1  B000051ZOA  B00005K47X  B000058AKE  \n",
       "B00004ZCJE           2           1           1          18           0  \n",
       "B00005ATMB          11           0           0           0           0  \n",
       "B00004Z5M1           1           0           1           0           0  \n",
       "B00005ARK3           1           0           0           0           0  \n",
       "B00004ZC8Y           0           0           1           4           0  \n",
       "B00005ATMI         597           0           0           1           0  \n",
       "B00005A1K1           0         522           0           0           0  \n",
       "B000051ZOA           0           0         410           0           1  \n",
       "B00005K47X           1           0           0         390           0  \n",
       "B000058AKE           0           0           1           0         363  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a 10 * 10 contigency table\n",
    "# This is made possible via matrix transposition and multiplication\n",
    "contigency_table = top_10_dataframe.T.dot(top_10_dataframe.astype(int))\n",
    "\n",
    "# Check the contigency table\n",
    "contigency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide each count by total number of baskets to find support\n",
    "Support_table = contigency_table/len(online_basket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
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       "    .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>B00004ZCJE</th>\n",
       "      <th>B00005ATMB</th>\n",
       "      <th>B00004Z5M1</th>\n",
       "      <th>B00005ARK3</th>\n",
       "      <th>B00004ZC8Y</th>\n",
       "      <th>B00005ATMI</th>\n",
       "      <th>B00005A1K1</th>\n",
       "      <th>B000051ZOA</th>\n",
       "      <th>B00005K47X</th>\n",
       "      <th>B000058AKE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B00004ZCJE</th>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005ATMB</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027167</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00004Z5M1</th>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.021814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005ARK3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00004ZC8Y</th>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005ATMI</th>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005A1K1</th>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013972</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B000051ZOA</th>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00005K47X</th>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010439</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B000058AKE</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            B00004ZCJE  B00005ATMB  B00004Z5M1  B00005ARK3  B00004ZC8Y  \\\n",
       "B00004ZCJE    0.068173    0.000000    0.000161    0.000000    0.002596   \n",
       "B00005ATMB    0.000000    0.027167    0.000054    0.000000    0.000000   \n",
       "B00004Z5M1    0.000161    0.000054    0.021814    0.000000    0.000000   \n",
       "B00005ARK3    0.000000    0.000000    0.000000    0.021279    0.000000   \n",
       "B00004ZC8Y    0.002596    0.000000    0.000000    0.000000    0.019271   \n",
       "B00005ATMI    0.000054    0.000294    0.000027    0.000027    0.000000   \n",
       "B00005A1K1    0.000027    0.000000    0.000000    0.000000    0.000000   \n",
       "B000051ZOA    0.000027    0.000000    0.000027    0.000000    0.000027   \n",
       "B00005K47X    0.000482    0.000000    0.000000    0.000000    0.000107   \n",
       "B000058AKE    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "            B00005ATMI  B00005A1K1  B000051ZOA  B00005K47X  B000058AKE  \n",
       "B00004ZCJE    0.000054    0.000027    0.000027    0.000482    0.000000  \n",
       "B00005ATMB    0.000294    0.000000    0.000000    0.000000    0.000000  \n",
       "B00004Z5M1    0.000027    0.000000    0.000027    0.000000    0.000000  \n",
       "B00005ARK3    0.000027    0.000000    0.000000    0.000000    0.000000  \n",
       "B00004ZC8Y    0.000000    0.000000    0.000027    0.000107    0.000000  \n",
       "B00005ATMI    0.015979    0.000000    0.000000    0.000027    0.000000  \n",
       "B00005A1K1    0.000000    0.013972    0.000000    0.000000    0.000000  \n",
       "B000051ZOA    0.000000    0.000000    0.010974    0.000000    0.000027  \n",
       "B00005K47X    0.000027    0.000000    0.000000    0.010439    0.000000  \n",
       "B000058AKE    0.000000    0.000000    0.000027    0.000000    0.009716  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the Support table\n",
    "Support_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of the 10x10 dataframe of supports above\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(Support_table, vmax=0.0002)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The heatmap shows that there are products that do appear together frequently in baskets. An example is **Product_ID** `B00004ZC8Y` which seems to be frequently bought together with **Product_ID** `B00004ZCJE`. The product that corresponds to the AMAZON (ASIN-Amazon Standard Identification Number) of `B00004ZC8Y` is a Tiffen 46mm Circular Polarizer, while the product name for the ASIN (`B00004ZCJE`) is a Tiffen 46mm Digital camera UV Protection Filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Prior to fitting the `Apriori` algorithm to the dataset, a histogram is plotted below to estimate the ideal value for minimum \n",
    "`support`. A vertical line representing the 99th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram to view the distribution of support for the items in order to help determine minimum support.\n",
    "# (prior to fitting the Apriori algorithm)\n",
    "\n",
    "\n",
    "basket_support = encoded_basket.sum(axis=0)/len(online_basket)\n",
    "percentile_99 = np.percentile(basket_support,99,axis=0)\n",
    "\n",
    "# Look at the distribution of supports\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(basket_support, bins=30)\n",
    "\n",
    "plt.title('99 percent of items have a threshold for support below 0.00520', fontsize=15)\n",
    "plt.xlabel('Support', fontsize=15)\n",
    "plt.ylabel('Count', fontsize=15)\n",
    "plt.yscale('log')\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.axvline(percentile_99, color='r', linestyle='dashed', linewidth=1)\n",
    "\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "plt.text(percentile_99,max_ylim*0.6, '  99th percentile: {:.5f}'.format(percentile_99), fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the histogram plot, 99 % of eletronic products do have a a threshold for `support` under 0.0052. I am going to use a minimum support value below this threshold so as to ensure that at least 99% of the dataset is included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe with itemsets\n",
    "apriori_dataframe = apriori(encoded_basket, min_support=0.0002, use_colnames=True,max_len=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>support</th>\n",
       "      <th>itemsets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000268</td>\n",
       "      <td>(B00004WHV7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00004WI43)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001820</td>\n",
       "      <td>(B00004WIMW)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001552</td>\n",
       "      <td>(B00004WLJ2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000964</td>\n",
       "      <td>(B00004WLJ4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000749</td>\n",
       "      <td>(B00004WLJ5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000830</td>\n",
       "      <td>(B00004WLJ7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000535</td>\n",
       "      <td>(B00004WLJ8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001820</td>\n",
       "      <td>(B00004WLJ9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000964</td>\n",
       "      <td>(B00004WLQY)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000401</td>\n",
       "      <td>(B00004WZOG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000428</td>\n",
       "      <td>(B00004WZON)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00004WZPY)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00004WZQS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000883</td>\n",
       "      <td>(B00004X036)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.001204</td>\n",
       "      <td>(B00004X0ZG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000509</td>\n",
       "      <td>(B00004X0ZH)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000910</td>\n",
       "      <td>(B00004X0ZI)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000268</td>\n",
       "      <td>(B00004X0ZL)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B00004X0ZM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.002703</td>\n",
       "      <td>(B00004X0ZO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000428</td>\n",
       "      <td>(B00004X0ZP)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000776</td>\n",
       "      <td>(B00004X107)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.001231</td>\n",
       "      <td>(B00004X109)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.001927</td>\n",
       "      <td>(B00004X10B)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.001231</td>\n",
       "      <td>(B00004X10C)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.002222</td>\n",
       "      <td>(B00004XO1V)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.007949</td>\n",
       "      <td>(B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.000562</td>\n",
       "      <td>(B00004XOND)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.000857</td>\n",
       "      <td>(B00004XONF)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>(B00005JC8J)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>0.001847</td>\n",
       "      <td>(B00005JDDZ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>0.000348</td>\n",
       "      <td>(B00005JGFK)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B00005JGFO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00005JHDS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00005JHE4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>0.000535</td>\n",
       "      <td>(B00005JJEO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>0.000937</td>\n",
       "      <td>(B00005K2R0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>0.000562</td>\n",
       "      <td>(B00005K3LZ)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>0.010439</td>\n",
       "      <td>(B00005K47X)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>(B000053HH5, B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B000053HH5, B00004YZQ8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>0.000375</td>\n",
       "      <td>(B00004Z5D1, B00004Z5VS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>0.000241</td>\n",
       "      <td>(B00004Z5D1, B00004Z6XS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>0.000348</td>\n",
       "      <td>(B00004ZC9V, B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B00004ZC8Y, B00004ZCAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B00004ZCB4, B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>0.002596</td>\n",
       "      <td>(B00004ZC8Y, B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>(B00004ZC9V, B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>(B00004ZCKV, B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>(B000053HH5, B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>0.000482</td>\n",
       "      <td>(B00004ZCJE, B00005K47X)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>0.000268</td>\n",
       "      <td>(B0000511U7, B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>0.000214</td>\n",
       "      <td>(B0000513US, B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>(B00005854B, B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.001392</td>\n",
       "      <td>(B000056SSM, B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>(B00005AW1H, B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.000857</td>\n",
       "      <td>(B00005AW1H, B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>0.000321</td>\n",
       "      <td>(B00005ARK3, B00005B8HO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>0.000294</td>\n",
       "      <td>(B00005ATMB, B00005ATMI)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>753 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      support                  itemsets\n",
       "0    0.000268              (B00004WHV7)\n",
       "1    0.000241              (B00004WI43)\n",
       "2    0.001820              (B00004WIMW)\n",
       "3    0.001552              (B00004WLJ2)\n",
       "4    0.000964              (B00004WLJ4)\n",
       "5    0.000749              (B00004WLJ5)\n",
       "6    0.000830              (B00004WLJ7)\n",
       "7    0.000535              (B00004WLJ8)\n",
       "8    0.001820              (B00004WLJ9)\n",
       "9    0.000964              (B00004WLQY)\n",
       "10   0.000401              (B00004WZOG)\n",
       "11   0.000428              (B00004WZON)\n",
       "12   0.000241              (B00004WZPY)\n",
       "13   0.000241              (B00004WZQS)\n",
       "14   0.000883              (B00004X036)\n",
       "15   0.001204              (B00004X0ZG)\n",
       "16   0.000509              (B00004X0ZH)\n",
       "17   0.000910              (B00004X0ZI)\n",
       "18   0.000268              (B00004X0ZL)\n",
       "19   0.000214              (B00004X0ZM)\n",
       "20   0.002703              (B00004X0ZO)\n",
       "21   0.000428              (B00004X0ZP)\n",
       "22   0.000776              (B00004X107)\n",
       "23   0.001231              (B00004X109)\n",
       "24   0.001927              (B00004X10B)\n",
       "25   0.001231              (B00004X10C)\n",
       "26   0.002222              (B00004XO1V)\n",
       "27   0.007949              (B00004XOM3)\n",
       "28   0.000562              (B00004XOND)\n",
       "29   0.000857              (B00004XONF)\n",
       "..        ...                       ...\n",
       "723  0.000294              (B00005JC8J)\n",
       "724  0.001847              (B00005JDDZ)\n",
       "725  0.000348              (B00005JGFK)\n",
       "726  0.000214              (B00005JGFO)\n",
       "727  0.000241              (B00005JHDS)\n",
       "728  0.000241              (B00005JHE4)\n",
       "729  0.000535              (B00005JJEO)\n",
       "730  0.000937              (B00005K2R0)\n",
       "731  0.000562              (B00005K3LZ)\n",
       "732  0.010439              (B00005K47X)\n",
       "733  0.000321  (B000053HH5, B00004XOM3)\n",
       "734  0.000214  (B000053HH5, B00004YZQ8)\n",
       "735  0.000375  (B00004Z5D1, B00004Z5VS)\n",
       "736  0.000241  (B00004Z5D1, B00004Z6XS)\n",
       "737  0.000348  (B00004ZC9V, B00004ZC8Y)\n",
       "738  0.000214  (B00004ZC8Y, B00004ZCAT)\n",
       "739  0.000214  (B00004ZCB4, B00004ZC8Y)\n",
       "740  0.002596  (B00004ZC8Y, B00004ZCJE)\n",
       "741  0.000294  (B00004ZC9V, B00004ZCJE)\n",
       "742  0.000321  (B00004ZCKV, B00004ZCJE)\n",
       "743  0.000294  (B000053HH5, B00004ZCJE)\n",
       "744  0.000482  (B00004ZCJE, B00005K47X)\n",
       "745  0.000268  (B0000511U7, B0000513O4)\n",
       "746  0.000214  (B0000513US, B0000513O4)\n",
       "747  0.000321  (B00005854B, B000056SSM)\n",
       "748  0.001392  (B000056SSM, B00005ARK3)\n",
       "749  0.000294  (B00005AW1H, B000056SSM)\n",
       "750  0.000857  (B00005AW1H, B00005ARK3)\n",
       "751  0.000321  (B00005ARK3, B00005B8HO)\n",
       "752  0.000294  (B00005ATMB, B00005ATMI)\n",
       "\n",
       "[753 rows x 2 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dataframe\n",
    "apriori_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Association rules are determined below after fitting the **`Apriori`** algorithm to the sparse Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Association rules are determined for the itemsets using LIFT with a minimum threshold of 1\n",
    "basket_rules=association_rules(apriori_dataframe,metric='lift',min_threshold=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.034582</td>\n",
       "      <td>4.350246</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1.027587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>4.350246</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1.032426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.023055</td>\n",
       "      <td>8.879884</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.020941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>8.879884</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.079765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.040698</td>\n",
       "      <td>15.675318</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>1.039718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.144330</td>\n",
       "      <td>15.675318</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>1.157914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.026163</td>\n",
       "      <td>7.518985</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1.023293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>7.518985</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1.064488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.005648</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.061611</td>\n",
       "      <td>3.197031</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>1.045120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.005648</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>3.197031</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>1.012636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>4.717298</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>1.008854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>4.717298</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>1.078801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>5.462135</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>1.096108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>5.462135</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>1.009179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>1.976190</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>1.076911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.038084</td>\n",
       "      <td>1.976190</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>1.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(B0000511U7)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.055249</td>\n",
       "      <td>15.066742</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.054598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000511U7)</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.072993</td>\n",
       "      <td>15.066742</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.073514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(B0000513US)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.096386</td>\n",
       "      <td>26.285111</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>1.102609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000513US)</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.058394</td>\n",
       "      <td>26.285111</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>1.059656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(B00005854B)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.079470</td>\n",
       "      <td>9.734709</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>1.077463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005854B)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.039344</td>\n",
       "      <td>9.734709</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>1.036748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.170492</td>\n",
       "      <td>8.012257</td>\n",
       "      <td>0.001218</td>\n",
       "      <td>1.179881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.065409</td>\n",
       "      <td>8.012257</td>\n",
       "      <td>0.001218</td>\n",
       "      <td>1.061252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.036184</td>\n",
       "      <td>4.432388</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>1.029073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.036066</td>\n",
       "      <td>4.432388</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>1.028974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>4.946839</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.093865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.040252</td>\n",
       "      <td>4.946839</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.033462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005B8HO)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.015094</td>\n",
       "      <td>4.699497</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>1.012065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(B00005B8HO)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.699497</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>1.087468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     antecedents   consequents  antecedent support  consequent support  \\\n",
       "0   (B000053HH5)  (B00004XOM3)            0.009288            0.007949   \n",
       "1   (B00004XOM3)  (B000053HH5)            0.007949            0.009288   \n",
       "2   (B000053HH5)  (B00004YZQ8)            0.009288            0.002596   \n",
       "3   (B00004YZQ8)  (B000053HH5)            0.002596            0.009288   \n",
       "4   (B00004Z5D1)  (B00004Z5VS)            0.009207            0.002596   \n",
       "5   (B00004Z5VS)  (B00004Z5D1)            0.002596            0.009207   \n",
       "6   (B00004Z5D1)  (B00004Z6XS)            0.009207            0.003480   \n",
       "7   (B00004Z6XS)  (B00004Z5D1)            0.003480            0.009207   \n",
       "8   (B00004ZC9V)  (B00004ZC8Y)            0.005648            0.019271   \n",
       "9   (B00004ZC8Y)  (B00004ZC9V)            0.019271            0.005648   \n",
       "10  (B00004ZC8Y)  (B00004ZCAT)            0.019271            0.002355   \n",
       "11  (B00004ZCAT)  (B00004ZC8Y)            0.002355            0.019271   \n",
       "12  (B00004ZCB4)  (B00004ZC8Y)            0.002034            0.019271   \n",
       "13  (B00004ZC8Y)  (B00004ZCB4)            0.019271            0.002034   \n",
       "14  (B00004ZC8Y)  (B00004ZCJE)            0.019271            0.068173   \n",
       "15  (B00004ZCJE)  (B00004ZC8Y)            0.068173            0.019271   \n",
       "16  (B0000511U7)  (B0000513O4)            0.004845            0.003667   \n",
       "17  (B0000513O4)  (B0000511U7)            0.003667            0.004845   \n",
       "18  (B0000513US)  (B0000513O4)            0.002222            0.003667   \n",
       "19  (B0000513O4)  (B0000513US)            0.003667            0.002222   \n",
       "20  (B00005854B)  (B000056SSM)            0.004042            0.008164   \n",
       "21  (B000056SSM)  (B00005854B)            0.008164            0.004042   \n",
       "22  (B000056SSM)  (B00005ARK3)            0.008164            0.021279   \n",
       "23  (B00005ARK3)  (B000056SSM)            0.021279            0.008164   \n",
       "24  (B00005AW1H)  (B000056SSM)            0.008137            0.008164   \n",
       "25  (B000056SSM)  (B00005AW1H)            0.008164            0.008137   \n",
       "26  (B00005AW1H)  (B00005ARK3)            0.008137            0.021279   \n",
       "27  (B00005ARK3)  (B00005AW1H)            0.021279            0.008137   \n",
       "28  (B00005ARK3)  (B00005B8HO)            0.021279            0.003212   \n",
       "29  (B00005B8HO)  (B00005ARK3)            0.003212            0.021279   \n",
       "\n",
       "     support  confidence       lift  leverage  conviction  \n",
       "0   0.000321    0.034582   4.350246  0.000247    1.027587  \n",
       "1   0.000321    0.040404   4.350246  0.000247    1.032426  \n",
       "2   0.000214    0.023055   8.879884  0.000190    1.020941  \n",
       "3   0.000214    0.082474   8.879884  0.000190    1.079765  \n",
       "4   0.000375    0.040698  15.675318  0.000351    1.039718  \n",
       "5   0.000375    0.144330  15.675318  0.000351    1.157914  \n",
       "6   0.000241    0.026163   7.518985  0.000209    1.023293  \n",
       "7   0.000241    0.069231   7.518985  0.000209    1.064488  \n",
       "8   0.000348    0.061611   3.197031  0.000239    1.045120  \n",
       "9   0.000348    0.018056   3.197031  0.000239    1.012636  \n",
       "10  0.000214    0.011111   4.717298  0.000169    1.008854  \n",
       "11  0.000214    0.090909   4.717298  0.000169    1.078801  \n",
       "12  0.000214    0.105263   5.462135  0.000175    1.096108  \n",
       "13  0.000214    0.011111   5.462135  0.000175    1.009179  \n",
       "14  0.002596    0.134722   1.976190  0.001283    1.076911  \n",
       "15  0.002596    0.038084   1.976190  0.001283    1.019557  \n",
       "16  0.000268    0.055249  15.066742  0.000250    1.054598  \n",
       "17  0.000268    0.072993  15.066742  0.000250    1.073514  \n",
       "18  0.000214    0.096386  26.285111  0.000206    1.102609  \n",
       "19  0.000214    0.058394  26.285111  0.000206    1.059656  \n",
       "20  0.000321    0.079470   9.734709  0.000288    1.077463  \n",
       "21  0.000321    0.039344   9.734709  0.000288    1.036748  \n",
       "22  0.001392    0.170492   8.012257  0.001218    1.179881  \n",
       "23  0.001392    0.065409   8.012257  0.001218    1.061252  \n",
       "24  0.000294    0.036184   4.432388  0.000228    1.029073  \n",
       "25  0.000294    0.036066   4.432388  0.000228    1.028974  \n",
       "26  0.000857    0.105263   4.946839  0.000683    1.093865  \n",
       "27  0.000857    0.040252   4.946839  0.000683    1.033462  \n",
       "28  0.000321    0.015094   4.699497  0.000253    1.012065  \n",
       "29  0.000321    0.100000   4.699497  0.000253    1.087468  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the dataframe for the association rules\n",
    "basket_rules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The **most important rules** from the association rules table above are determined based on `Support`(how frequent the item(s) occur in a transaction) and `Lift`(measure of how much sales is driven by the relationship ). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the association rules table (association_rules) above in a descending order using support and lift\n",
    "final_basket =basket_rules.sort_values(['support','lift'],ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "      <th>antecedent support</th>\n",
       "      <th>consequent support</th>\n",
       "      <th>support</th>\n",
       "      <th>confidence</th>\n",
       "      <th>lift</th>\n",
       "      <th>leverage</th>\n",
       "      <th>conviction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>1.976190</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>1.076911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.068173</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.038084</td>\n",
       "      <td>1.976190</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>1.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.170492</td>\n",
       "      <td>8.012257</td>\n",
       "      <td>0.001218</td>\n",
       "      <td>1.179881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.065409</td>\n",
       "      <td>8.012257</td>\n",
       "      <td>0.001218</td>\n",
       "      <td>1.061252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>4.946839</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.093865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.040252</td>\n",
       "      <td>4.946839</td>\n",
       "      <td>0.000683</td>\n",
       "      <td>1.033462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.040698</td>\n",
       "      <td>15.675318</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>1.039718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.144330</td>\n",
       "      <td>15.675318</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>1.157914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.005648</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.018056</td>\n",
       "      <td>3.197031</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>1.012636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.005648</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.061611</td>\n",
       "      <td>3.197031</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>1.045120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(B00005854B)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.079470</td>\n",
       "      <td>9.734709</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>1.077463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005854B)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.004042</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.039344</td>\n",
       "      <td>9.734709</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>1.036748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005B8HO)</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.015094</td>\n",
       "      <td>4.699497</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>1.012065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(B00005B8HO)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>0.003212</td>\n",
       "      <td>0.021279</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.699497</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>1.087468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.034582</td>\n",
       "      <td>4.350246</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1.027587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>4.350246</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1.032426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.036184</td>\n",
       "      <td>4.432388</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>1.029073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>0.008164</td>\n",
       "      <td>0.008137</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.036066</td>\n",
       "      <td>4.432388</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>1.028974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(B0000511U7)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.055249</td>\n",
       "      <td>15.066742</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.054598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000511U7)</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.072993</td>\n",
       "      <td>15.066742</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>1.073514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.026163</td>\n",
       "      <td>7.518985</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1.023293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.069231</td>\n",
       "      <td>7.518985</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>1.064488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000513US)</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.058394</td>\n",
       "      <td>26.285111</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>1.059656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(B0000513US)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0.003667</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.096386</td>\n",
       "      <td>26.285111</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>1.102609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.023055</td>\n",
       "      <td>8.879884</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.020941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>0.002596</td>\n",
       "      <td>0.009288</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>8.879884</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.079765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>5.462135</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>1.096108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>5.462135</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>1.009179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>4.717298</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>1.008854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.019271</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>4.717298</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>1.078801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     antecedents   consequents  antecedent support  consequent support  \\\n",
       "14  (B00004ZC8Y)  (B00004ZCJE)            0.019271            0.068173   \n",
       "15  (B00004ZCJE)  (B00004ZC8Y)            0.068173            0.019271   \n",
       "22  (B000056SSM)  (B00005ARK3)            0.008164            0.021279   \n",
       "23  (B00005ARK3)  (B000056SSM)            0.021279            0.008164   \n",
       "26  (B00005AW1H)  (B00005ARK3)            0.008137            0.021279   \n",
       "27  (B00005ARK3)  (B00005AW1H)            0.021279            0.008137   \n",
       "4   (B00004Z5D1)  (B00004Z5VS)            0.009207            0.002596   \n",
       "5   (B00004Z5VS)  (B00004Z5D1)            0.002596            0.009207   \n",
       "9   (B00004ZC8Y)  (B00004ZC9V)            0.019271            0.005648   \n",
       "8   (B00004ZC9V)  (B00004ZC8Y)            0.005648            0.019271   \n",
       "20  (B00005854B)  (B000056SSM)            0.004042            0.008164   \n",
       "21  (B000056SSM)  (B00005854B)            0.008164            0.004042   \n",
       "28  (B00005ARK3)  (B00005B8HO)            0.021279            0.003212   \n",
       "29  (B00005B8HO)  (B00005ARK3)            0.003212            0.021279   \n",
       "0   (B000053HH5)  (B00004XOM3)            0.009288            0.007949   \n",
       "1   (B00004XOM3)  (B000053HH5)            0.007949            0.009288   \n",
       "24  (B00005AW1H)  (B000056SSM)            0.008137            0.008164   \n",
       "25  (B000056SSM)  (B00005AW1H)            0.008164            0.008137   \n",
       "16  (B0000511U7)  (B0000513O4)            0.004845            0.003667   \n",
       "17  (B0000513O4)  (B0000511U7)            0.003667            0.004845   \n",
       "6   (B00004Z5D1)  (B00004Z6XS)            0.009207            0.003480   \n",
       "7   (B00004Z6XS)  (B00004Z5D1)            0.003480            0.009207   \n",
       "19  (B0000513O4)  (B0000513US)            0.003667            0.002222   \n",
       "18  (B0000513US)  (B0000513O4)            0.002222            0.003667   \n",
       "2   (B000053HH5)  (B00004YZQ8)            0.009288            0.002596   \n",
       "3   (B00004YZQ8)  (B000053HH5)            0.002596            0.009288   \n",
       "12  (B00004ZCB4)  (B00004ZC8Y)            0.002034            0.019271   \n",
       "13  (B00004ZC8Y)  (B00004ZCB4)            0.019271            0.002034   \n",
       "10  (B00004ZC8Y)  (B00004ZCAT)            0.019271            0.002355   \n",
       "11  (B00004ZCAT)  (B00004ZC8Y)            0.002355            0.019271   \n",
       "\n",
       "     support  confidence       lift  leverage  conviction  \n",
       "14  0.002596    0.134722   1.976190  0.001283    1.076911  \n",
       "15  0.002596    0.038084   1.976190  0.001283    1.019557  \n",
       "22  0.001392    0.170492   8.012257  0.001218    1.179881  \n",
       "23  0.001392    0.065409   8.012257  0.001218    1.061252  \n",
       "26  0.000857    0.105263   4.946839  0.000683    1.093865  \n",
       "27  0.000857    0.040252   4.946839  0.000683    1.033462  \n",
       "4   0.000375    0.040698  15.675318  0.000351    1.039718  \n",
       "5   0.000375    0.144330  15.675318  0.000351    1.157914  \n",
       "9   0.000348    0.018056   3.197031  0.000239    1.012636  \n",
       "8   0.000348    0.061611   3.197031  0.000239    1.045120  \n",
       "20  0.000321    0.079470   9.734709  0.000288    1.077463  \n",
       "21  0.000321    0.039344   9.734709  0.000288    1.036748  \n",
       "28  0.000321    0.015094   4.699497  0.000253    1.012065  \n",
       "29  0.000321    0.100000   4.699497  0.000253    1.087468  \n",
       "0   0.000321    0.034582   4.350246  0.000247    1.027587  \n",
       "1   0.000321    0.040404   4.350246  0.000247    1.032426  \n",
       "24  0.000294    0.036184   4.432388  0.000228    1.029073  \n",
       "25  0.000294    0.036066   4.432388  0.000228    1.028974  \n",
       "16  0.000268    0.055249  15.066742  0.000250    1.054598  \n",
       "17  0.000268    0.072993  15.066742  0.000250    1.073514  \n",
       "6   0.000241    0.026163   7.518985  0.000209    1.023293  \n",
       "7   0.000241    0.069231   7.518985  0.000209    1.064488  \n",
       "19  0.000214    0.058394  26.285111  0.000206    1.059656  \n",
       "18  0.000214    0.096386  26.285111  0.000206    1.102609  \n",
       "2   0.000214    0.023055   8.879884  0.000190    1.020941  \n",
       "3   0.000214    0.082474   8.879884  0.000190    1.079765  \n",
       "12  0.000214    0.105263   5.462135  0.000175    1.096108  \n",
       "13  0.000214    0.011111   5.462135  0.000175    1.009179  \n",
       "10  0.000214    0.011111   4.717298  0.000169    1.008854  \n",
       "11  0.000214    0.090909   4.717298  0.000169    1.078801  "
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the 30 association rules in order of importance\n",
    "final_basket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Function provided below is a recommendation system that returns a list of up to 3 items (`consequents`) based on the basket `antecents` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Market Basket Analysis recommendation system\n",
    "\n",
    "def basket_recommendation_system(basket, association_rules_dataframe):\n",
    "    '''\n",
    "    Function is used to recommend items to customers\n",
    "    based on current items(products) in the basket.\n",
    "    \n",
    "    ------------Allowable inputs for a basket are strings. These include the following:\n",
    "    - List of strings\n",
    "    - Numpy array of strings\n",
    "    - Frozenset of strings\n",
    "    - Set of strings\n",
    "    \n",
    "    ------------Allowable input for a dataframe is an association rules dataframe.\n",
    "    '''\n",
    "    assert isinstance('basket',(str,list,set,frozenset,np.string_)),\\\n",
    "    'Input basket has to be a string, or a list/set/frozenset/Numpy array of string(s)'\n",
    "    \n",
    "    assert isinstance(association_rules_dataframe, pd.DataFrame),\\\n",
    "    ' Input dataframe has to be a dataframe of association rules'\n",
    "    \n",
    "    # Turn the input basket into a set\n",
    "    basket_set = set(basket)\n",
    "    \n",
    "    # Filter the association rules dataframe to obtain rows whose antecedents match the set\n",
    "    filtered_df = rules_dataframe[rules_dataframe['antecedents'] == basket_set]\n",
    "    \n",
    "    # Sort the filtered dataframe in place by support and lift in descending order\n",
    "    filtered_df.sort_values(['support', 'lift'], ascending=False, inplace=True)\n",
    "    \n",
    "    # Set up an empty recommendation list\n",
    "    recommendation_list = []\n",
    "\n",
    "    # Loop over the consequents\n",
    "    for consequent in filtered_df['consequents']:\n",
    "    \n",
    "        # We use a nested for loop here to access each element (item) in each frozenset (consequent)\n",
    "        for item in consequent:\n",
    "        \n",
    "            # Append non-duplicate items to the recommendation list\n",
    "            if item not in recommendation_list:\n",
    "                recommendation_list.append(item)\n",
    "    \n",
    "    # If three or more recommendations were found, return a slice of the list containing the first three elements\n",
    "    if len(recommendation_list) >= 3:\n",
    "        return recommendation_list[:3]\n",
    "    \n",
    "    # If two recommendations were found, return the list\n",
    "    elif len(recommendation_list) == 2:\n",
    "        return recommendation_list\n",
    "\n",
    "    # If only one recommendation was found, return the list\n",
    "    elif len(recommendation_list) == 1:\n",
    "        return recommendation_list\n",
    "     # Do nothing (this returns None),if there are no recommendations\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### END OF MARKET BASKET ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The 30 `association rules` are further explored to visualize a product network with the purpose of determining expressive clusters in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the dataframe that will be used as `Edge` for network analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a subset of the dataframe that contains only the antecedent and consequent columns\n",
    "rule_objects = basket_rules[['antecedents', 'consequents']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(B0000511U7)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000511U7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(B0000513US)</td>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "      <td>(B0000513US)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(B00005854B)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005854B)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "      <td>(B00005B8HO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(B00005B8HO)</td>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     antecedents   consequents\n",
       "0   (B000053HH5)  (B00004XOM3)\n",
       "1   (B00004XOM3)  (B000053HH5)\n",
       "2   (B000053HH5)  (B00004YZQ8)\n",
       "3   (B00004YZQ8)  (B000053HH5)\n",
       "4   (B00004Z5D1)  (B00004Z5VS)\n",
       "5   (B00004Z5VS)  (B00004Z5D1)\n",
       "6   (B00004Z5D1)  (B00004Z6XS)\n",
       "7   (B00004Z6XS)  (B00004Z5D1)\n",
       "8   (B00004ZC9V)  (B00004ZC8Y)\n",
       "9   (B00004ZC8Y)  (B00004ZC9V)\n",
       "10  (B00004ZC8Y)  (B00004ZCAT)\n",
       "11  (B00004ZCAT)  (B00004ZC8Y)\n",
       "12  (B00004ZCB4)  (B00004ZC8Y)\n",
       "13  (B00004ZC8Y)  (B00004ZCB4)\n",
       "14  (B00004ZC8Y)  (B00004ZCJE)\n",
       "15  (B00004ZCJE)  (B00004ZC8Y)\n",
       "16  (B0000511U7)  (B0000513O4)\n",
       "17  (B0000513O4)  (B0000511U7)\n",
       "18  (B0000513US)  (B0000513O4)\n",
       "19  (B0000513O4)  (B0000513US)\n",
       "20  (B00005854B)  (B000056SSM)\n",
       "21  (B000056SSM)  (B00005854B)\n",
       "22  (B000056SSM)  (B00005ARK3)\n",
       "23  (B00005ARK3)  (B000056SSM)\n",
       "24  (B00005AW1H)  (B000056SSM)\n",
       "25  (B000056SSM)  (B00005AW1H)\n",
       "26  (B00005AW1H)  (B00005ARK3)\n",
       "27  (B00005ARK3)  (B00005AW1H)\n",
       "28  (B00005ARK3)  (B00005B8HO)\n",
       "29  (B00005B8HO)  (B00005ARK3)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rule_objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `Edge` for network analysis will be exported as `.csv` file into S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the dataframe\n",
    "Edge = rule_objects.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>antecedents</th>\n",
       "      <th>consequents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    antecedents   consequents\n",
       "0  (B000053HH5)  (B00004XOM3)\n",
       "1  (B00004XOM3)  (B000053HH5)\n",
       "2  (B000053HH5)  (B00004YZQ8)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 3 rows after copying the dataframe\n",
    "Edge.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '238EF6FD65EBC1C8',\n",
       "  'HostId': 'tkoX7opdPBHbRTyyrd6AA0o3E8r5xHxJgpEDaNLwfxqBZV0/ON1rC+mO1ZLv3nYQZrKAzNyGf7w=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': 'tkoX7opdPBHbRTyyrd6AA0o3E8r5xHxJgpEDaNLwfxqBZV0/ON1rC+mO1ZLv3nYQZrKAzNyGf7w=',\n",
       "   'x-amz-request-id': '238EF6FD65EBC1C8',\n",
       "   'date': 'Sun, 29 Mar 2020 13:57:26 GMT',\n",
       "   'etag': '\"5e3609218d01596574d0899c7ada58c3\"',\n",
       "   'content-length': '0',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ETag': '\"5e3609218d01596574d0899c7ada58c3\"'}"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export Edge dataframe as a .csv file into the S3 bucket\n",
    "from io import StringIO\n",
    "import boto3\n",
    "\n",
    "bucket = 'togunniyi-bstn-bucket29' # already created on S3\n",
    "csv_buffer = StringIO()\n",
    "Edge.to_csv(csv_buffer)\n",
    "s3_resource = boto3.resource('s3')\n",
    "s3_resource.Object(bucket, 'Edge.csv').put(Body=csv_buffer.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the dataframe that will be used as the `Node` for network analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new dataframe that contains 1 colum with the antecedent and consequent columns stacked on top of each other\n",
    "stack_columns=pd.DataFrame(rule_objects.stack().reset_index(drop=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(B00004XOM3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(B00004YZQ8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(B000053HH5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(B00004Z5VS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(B00004Z6XS)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(B00004Z5D1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(B00004ZC9V)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(B00004ZCAT)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(B00004ZCB4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(B00004ZCJE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(B00004ZC8Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(B0000511U7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(B0000511U7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(B0000513US)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(B0000513O4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(B0000513US)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(B00005854B)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(B00005854B)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>(B000056SSM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>(B00005AW1H)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>(B00005B8HO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>(B00005B8HO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>(B00005ARK3)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "0   (B000053HH5)\n",
       "1   (B00004XOM3)\n",
       "2   (B00004XOM3)\n",
       "3   (B000053HH5)\n",
       "4   (B000053HH5)\n",
       "5   (B00004YZQ8)\n",
       "6   (B00004YZQ8)\n",
       "7   (B000053HH5)\n",
       "8   (B00004Z5D1)\n",
       "9   (B00004Z5VS)\n",
       "10  (B00004Z5VS)\n",
       "11  (B00004Z5D1)\n",
       "12  (B00004Z5D1)\n",
       "13  (B00004Z6XS)\n",
       "14  (B00004Z6XS)\n",
       "15  (B00004Z5D1)\n",
       "16  (B00004ZC9V)\n",
       "17  (B00004ZC8Y)\n",
       "18  (B00004ZC8Y)\n",
       "19  (B00004ZC9V)\n",
       "20  (B00004ZC8Y)\n",
       "21  (B00004ZCAT)\n",
       "22  (B00004ZCAT)\n",
       "23  (B00004ZC8Y)\n",
       "24  (B00004ZCB4)\n",
       "25  (B00004ZC8Y)\n",
       "26  (B00004ZC8Y)\n",
       "27  (B00004ZCB4)\n",
       "28  (B00004ZC8Y)\n",
       "29  (B00004ZCJE)\n",
       "30  (B00004ZCJE)\n",
       "31  (B00004ZC8Y)\n",
       "32  (B0000511U7)\n",
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       "35  (B0000511U7)\n",
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       "43  (B00005854B)\n",
       "44  (B000056SSM)\n",
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       "46  (B00005ARK3)\n",
       "47  (B000056SSM)\n",
       "48  (B00005AW1H)\n",
       "49  (B000056SSM)\n",
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       "51  (B00005AW1H)\n",
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       "53  (B00005ARK3)\n",
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       "55  (B00005AW1H)\n",
       "56  (B00005ARK3)\n",
       "57  (B00005B8HO)\n",
       "58  (B00005B8HO)\n",
       "59  (B00005ARK3)"
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     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# Check the dataframe\n",
    "stack_columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the name of the column from  \"0\" to \"Node\"\n",
    "stack_columns.columns=[\"Node\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
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       "           Node\n",
       "0  (B000053HH5)\n",
       "1  (B00004XOM3)\n",
       "2  (B00004XOM3)"
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     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Check the first 3 rows to ensure that the change has been made\n",
    "stack_columns.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the dataframe\n",
    "copy_stack=stack_columns.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(60, 1)"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# Check the number of rows and columns in the copy of the dataframe\n",
    "copy_stack.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the dataset for duplicates.If present, drop duplicate rows.\n",
    "stack_columns.drop_duplicates(keep='first', inplace=True)"
   ]
  },
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   "execution_count": 302,
   "metadata": {},
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       "            Node\n",
       "0   (B000053HH5)\n",
       "1   (B00004XOM3)\n",
       "5   (B00004YZQ8)\n",
       "8   (B00004Z5D1)\n",
       "9   (B00004Z5VS)\n",
       "13  (B00004Z6XS)\n",
       "16  (B00004ZC9V)\n",
       "17  (B00004ZC8Y)\n",
       "21  (B00004ZCAT)\n",
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       "45  (B00005ARK3)\n",
       "48  (B00005AW1H)\n",
       "57  (B00005B8HO)"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the index of the dataframe\n",
    "stacked_col= stack_columns.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
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       "            Node\n",
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       "2   (B00004YZQ8)\n",
       "3   (B00004Z5D1)\n",
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       "6   (B00004ZC9V)\n",
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       "9   (B00004ZCB4)\n",
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       "17  (B00005AW1H)\n",
       "18  (B00005B8HO)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the dataframe after resetting its index\n",
    "stacked_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the dataframe\n",
    "Node= stacked_col.copy()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
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       "      <td>(B00005AW1H)</td>\n",
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       "            Node\n",
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       "2   (B00004YZQ8)\n",
       "3   (B00004Z5D1)\n",
       "4   (B00004Z5VS)\n",
       "5   (B00004Z6XS)\n",
       "6   (B00004ZC9V)\n",
       "7   (B00004ZC8Y)\n",
       "8   (B00004ZCAT)\n",
       "9   (B00004ZCB4)\n",
       "10  (B00004ZCJE)\n",
       "11  (B0000511U7)\n",
       "12  (B0000513O4)\n",
       "13  (B0000513US)\n",
       "14  (B00005854B)\n",
       "15  (B000056SSM)\n",
       "16  (B00005ARK3)\n",
       "17  (B00005AW1H)\n",
       "18  (B00005B8HO)"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to ensure that the new dataframe has been created\n",
    "Node"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `Node` for network analysis will be exported as `.csv` file into S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': 'EA17EEA413B5FDA3',\n",
       "  'HostId': 'lnWH5qrMXBwwDPoXsPzuPdFN1By5rkfK164CZn0PMz87KOqJo9blOWwX0mok4AAkUIRysPOtwXY=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': 'lnWH5qrMXBwwDPoXsPzuPdFN1By5rkfK164CZn0PMz87KOqJo9blOWwX0mok4AAkUIRysPOtwXY=',\n",
       "   'x-amz-request-id': 'EA17EEA413B5FDA3',\n",
       "   'date': 'Sun, 29 Mar 2020 13:59:56 GMT',\n",
       "   'etag': '\"2c6c6c65a66aaaba26ea4e1d26cb3088\"',\n",
       "   'content-length': '0',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ETag': '\"2c6c6c65a66aaaba26ea4e1d26cb3088\"'}"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export the Node dataframe as a .csv file into the S3 bucket\n",
    "from io import StringIO \n",
    "import boto3\n",
    "\n",
    "bucket = 'togunniyi-bstn-bucket29' # already created on S3\n",
    "csv_buffer = StringIO()\n",
    "Node.to_csv(csv_buffer)\n",
    "s3_resource = boto3.resource('s3')\n",
    "s3_resource.Object(bucket, 'Node.csv').put(Body=csv_buffer.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the original dataframe used to create the `Node` for network analysis as .`csv` file into S3 bucket (for extra safety)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '4CAA762FAD008E69',\n",
       "  'HostId': 'L3Xc/mtYlNr/g1oHaDoQcfKCJP8167PXeoEftMrIU9nnx4QI/cQaTzv3ZI8U4PxTs3wLIc09w1U=',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amz-id-2': 'L3Xc/mtYlNr/g1oHaDoQcfKCJP8167PXeoEftMrIU9nnx4QI/cQaTzv3ZI8U4PxTs3wLIc09w1U=',\n",
       "   'x-amz-request-id': '4CAA762FAD008E69',\n",
       "   'date': 'Sun, 29 Mar 2020 14:05:20 GMT',\n",
       "   'etag': '\"c1ee345989a4f7bbc0570497bbe7d562\"',\n",
       "   'content-length': '0',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ETag': '\"c1ee345989a4f7bbc0570497bbe7d562\"'}"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from io import StringIO \n",
    "import boto3\n",
    "\n",
    "bucket = 'togunniyi-bstn-bucket29' # already created on S3\n",
    "csv_buffer = StringIO()\n",
    "copy_stack.to_csv(csv_buffer)\n",
    "s3_resource = boto3.resource('s3')\n",
    "s3_resource.Object(bucket, 'Original_Node.csv').put(Body=csv_buffer.getvalue())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .`csv` files have been successfully extracted and exported into S3 bucket. I will download the files directly from the S3 bucket for use in producing a Network analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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