NataliaRodrigues_HDip_DViz_ML_IntegratedCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2764dfb0",
   "metadata": {},
   "source": [
    "## Module Title:\t Data Visualization Techniques  & Machine Learning for Business \n",
    "### Assessment Title: Integrated CA\n",
    "### Lecturer Name:\t David McQuaid  & PhD Muhammad Iqbal\n",
    "### Student Full Name & Student ID:\tNatalia de Oliveira Rodrigues 2023112"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbcc089",
   "metadata": {},
   "source": [
    "### GitHub Repository: \n",
    "https://github.com/norodrigues111/Recommendation-System-and-Market-Basket-Analysis.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8dcc790",
   "metadata": {},
   "source": [
    "### Dataset Source:\n",
    "https://www.kaggle.com/datasets/rashikrahmanpritom/groceries-dataset-for-market-basket-analysismba"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36bb8f79",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This academic paper aims to deliver a critical analysis of the knowledge produced, in the course: Higher Diploma in Science in Data Analytics for Business at CCT College. It integrates Machine Learning for Business, and Data Visualizations subjects. \n",
    "\n",
    "This innovative project involves Content and Collaborative filtering techniques to develop a Recommendation System, and so provide personalized suggestions. Going beyond traditional recommendation methodologies, this paper also incorporates Market Basket Analysis to identify associations and enhance the recommendation process. For the Market Basket Analysis, we have applied Apriori and FP-Growth algorithms, and the result's comparison is provided in this paper. \n",
    "\n",
    "To facilitate user interaction and understanding, we have worked in Python to develop a dynamic dashboard that presents the system's insights and recommendations in an intuitive and visually appealing manner.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "256999b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install dash dash-core-components dash-html-components dash-table plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e6f11e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install dash-bootstrap-components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "af6078aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6e041e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7a806c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from numpy import asarray\n",
    "import statistics as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%matplotlib notebook\n",
    "sns.set(color_codes=True)\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import sigmoid_kernel\n",
    "from scipy.spatial.distance import cosine\n",
    "\n",
    "from mlxtend.preprocessing import TransactionEncoder\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules\n",
    "from mlxtend.frequent_patterns import fpgrowth\n",
    "\n",
    "import plotly.express as px\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "\n",
    "import dash\n",
    "from dash import dcc\n",
    "from dash import html\n",
    "from dash.dependencies import Input, Output\n",
    "import plotly.express as px\n",
    "from dash import dash_table\n",
    "from dash.dash_table.Format import Group\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3c608786",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Groceries data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43991dd9",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3f15fb1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 38765 rows \n",
      "Attributes: 7 columns\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Member_number</th>\n",
       "      <th>Date</th>\n",
       "      <th>itemDescription</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1808</td>\n",
       "      <td>2015-07-21</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2552</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>pip fruit</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1187</td>\n",
       "      <td>2015-12-12</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3037</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Member_number        Date   itemDescription  year  month  day  day_of_week\n",
       "0           1808  2015-07-21    tropical fruit  2015      7   21            1\n",
       "1           2552  2015-05-01        whole milk  2015      5    1            4\n",
       "2           2300  2015-09-19         pip fruit  2015      9   19            5\n",
       "3           1187  2015-12-12  other vegetables  2015     12   12            5\n",
       "4           3037  2015-01-02        whole milk  2015      1    2            4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Member_number</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38765.000000</td>\n",
       "      <td>38765.000000</td>\n",
       "      <td>38765.000000</td>\n",
       "      <td>38765.000000</td>\n",
       "      <td>38765.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3003.641868</td>\n",
       "      <td>2014.528518</td>\n",
       "      <td>6.477570</td>\n",
       "      <td>15.753231</td>\n",
       "      <td>3.014498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1153.611031</td>\n",
       "      <td>0.499193</td>\n",
       "      <td>3.431561</td>\n",
       "      <td>8.801391</td>\n",
       "      <td>1.987669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3005.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4007.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Member_number          year         month           day   day_of_week\n",
       "count   38765.000000  38765.000000  38765.000000  38765.000000  38765.000000\n",
       "mean     3003.641868   2014.528518      6.477570     15.753231      3.014498\n",
       "std      1153.611031      0.499193      3.431561      8.801391      1.987669\n",
       "min      1000.000000   2014.000000      1.000000      1.000000      0.000000\n",
       "25%      2002.000000   2014.000000      4.000000      8.000000      1.000000\n",
       "50%      3005.000000   2015.000000      6.000000     16.000000      3.000000\n",
       "75%      4007.000000   2015.000000      9.000000     23.000000      5.000000\n",
       "max      5000.000000   2015.000000     12.000000     31.000000      6.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Missing values: 0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38765 entries, 0 to 38764\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Member_number    38765 non-null  int64 \n",
      " 1   Date             38765 non-null  object\n",
      " 2   itemDescription  38765 non-null  object\n",
      " 3   year             38765 non-null  int64 \n",
      " 4   month            38765 non-null  int64 \n",
      " 5   day              38765 non-null  int64 \n",
      " 6   day_of_week      38765 non-null  int64 \n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 2.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def glimpse(df):\n",
    "    print(f'Observations: {df.shape[0]} rows \\nAttributes: {df.shape[1]} columns')\n",
    "    print(\"-\" * 100)\n",
    "    display(df.head())\n",
    "    print(\"-\" * 100)\n",
    "    display(df.describe())\n",
    "    print(\"-\" * 100)\n",
    "    display(f'Missing values: {df.isnull().sum().sum()}')\n",
    "    print(\"-\" * 100)\n",
    "    display(df.info())\n",
    "    \n",
    "df = df\n",
    "glimpse(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "63624e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76e691a",
   "metadata": {},
   "source": [
    "# The Purpose of Recommendation Systems in Machine Learning for Online Retail Businesses\n",
    "\n",
    "According to (Wikipedia Contributors, 2019), online retail, also well known as eCommerce, allows customers to buy goods or services using web-based technology. Electronic Commerce permit businesses all over the world, the opportunity to collect customers' data. Technology makes it possible to build customers’ profiles based on demographic data, purchase history, preferences, and active data such as likes, views, clicks, and time spent. \n",
    "\n",
    "According to the research (Bertens, Guitart and Chen Andáfrica Periáñez, 2018), E-commerce frequently uses recommendation systems, which are typically implemented using cooperative filtering techniques. Based on user ratings, they compare comparable products or users.\n",
    "\n",
    "AI has helped businesses to build powerful analytics tools to improve customer satisfaction and drive revenue. Recommendation Systems enable businesses to learn about their customers and provide more meaningful content, increasing customer satisfaction, and driving business revenue (Nawrocka, Kot and Nawrocki, 2018).\n",
    "Industry leaders in online retail are using recommendation systems. According to (Arkadiusz, 2021), Amazon’s recommendations are responsible for 35% of revenue, and Netflix’s recommendations are responsible for 80% of the movies seen on the platform. In 2023, according to (Cooper and McLachlan, 2023), 70% of the videos watched on YouTube were recommended to their customers.\n",
    "\n",
    "Recommendation engines enable customers to find goods or services using different algorithms. They are Content-based filtering, based on item features and user profile data; Collaborative-based filtering, based on the similarity with other users (user-to-user or item-to-item); and Hybrid systems that combine Content-based filtering, and Collaborative-based filtering. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e6a8c8",
   "metadata": {},
   "source": [
    "## Item Based Content Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "80fca472",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2f8c46a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rs_df['Quantity'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "59b06714",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN with an empty string\n",
    "rs_df['itemDescription'] = rs_df['itemDescription'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7411e255",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Member_number</th>\n",
       "      <th>Date</th>\n",
       "      <th>itemDescription</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>Quantity</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1808</td>\n",
       "      <td>2015-07-21</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2552</td>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>pip fruit</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1187</td>\n",
       "      <td>2015-12-12</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3037</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Member_number       Date   itemDescription  year  month  day  day_of_week  \\\n",
       "0           1808 2015-07-21    tropical fruit  2015      7   21            1   \n",
       "1           2552 2015-05-01        whole milk  2015      5    1            4   \n",
       "2           2300 2015-09-19         pip fruit  2015      9   19            5   \n",
       "3           1187 2015-12-12  other vegetables  2015     12   12            5   \n",
       "4           3037 2015-01-02        whole milk  2015      1    2            4   \n",
       "\n",
       "   Quantity  \n",
       "0         1  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b37b5d8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      tropical fruit\n",
       "1          whole milk\n",
       "2           pip fruit\n",
       "3    other vegetables\n",
       "4          whole milk\n",
       "Name: itemDescription, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_df.head()['itemDescription']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e6d48e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfv = TfidfVectorizer(min_df=3,  max_features=None, \n",
    "            strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n",
    "            ngram_range=(1, 3),\n",
    "            stop_words = 'english')\n",
    "\n",
    "# Filling NaNs with empty string\n",
    "rs_df['itemDescription'] = rs_df['itemDescription'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1c0e9674",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the TF-IDF on the 'overview' text\n",
    "tfv_matrix = tfv.fit_transform(rs_df['itemDescription'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c38dfee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the sigmoid kernel\n",
    "sig = sigmoid_kernel(tfv_matrix[:1000], tfv_matrix[:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7fe737a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "       0.76159416, 0.76159416, 0.76159416, 0.76174927, 0.76294559,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76294559,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76188085,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76294559, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76294559, 0.76159416, 0.76159416, 0.76159416, 0.76294559,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76294559, 0.76188085,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76188085, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.7618768 , 0.76159416, 0.76159416,\n",
       "       0.76159416, 0.76174927, 0.76159416, 0.76159416, 0.76188085,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76188085, 0.76159416,\n",
       "       0.76159416, 0.76159416, 0.76159416, 0.76159416, 0.76159416])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "98967526",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reverse mapping of indices and items names\n",
    "indices = pd.Series(rs_df.index, index=rs_df['itemDescription']).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "81699fc8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "itemDescription\n",
       "tropical fruit               0\n",
       "whole milk                   1\n",
       "pip fruit                    2\n",
       "other vegetables             3\n",
       "whole milk                   4\n",
       "                         ...  \n",
       "sliced cheese            38760\n",
       "candy                    38761\n",
       "cake bar                 38762\n",
       "fruit/vegetable juice    38763\n",
       "cat food                 38764\n",
       "Length: 38765, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3eb022ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "itemDescription\n",
       "whole milk        1\n",
       "whole milk        4\n",
       "whole milk        8\n",
       "whole milk       21\n",
       "whole milk       53\n",
       "              ...  \n",
       "whole milk    38667\n",
       "whole milk    38672\n",
       "whole milk    38688\n",
       "whole milk    38689\n",
       "whole milk    38745\n",
       "Length: 2502, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices['whole milk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1ad2bbaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_idx = indices['oil']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "92611c4e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#sig[n_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1e4a6e00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#list(enumerate(sig[indices['whole milk']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "710d23f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def give_rec(itemDescription, sig=sig):\n",
    "    # Get the index corresponding to items\n",
    "    idx = indices[itemDescription].iloc[0]\n",
    "\n",
    "    # Get the pairwsie similarity scores \n",
    "    sig_scores = list(enumerate(sig[idx]))\n",
    "\n",
    "    # Sort the items \n",
    "    sig_scores = sorted(sig_scores, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Scores of the 10 most similar items\n",
    "    sig_scores = sig_scores[1:11]\n",
    "\n",
    "    # item indices\n",
    "    item_indices = [i[0] for i in sig_scores]\n",
    "\n",
    "    # Top 10 most similar items\n",
    "    return rs_df['itemDescription'].iloc[item_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2589a03e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      whole milk\n",
       "8      whole milk\n",
       "21     whole milk\n",
       "53     whole milk\n",
       "54     whole milk\n",
       "64     whole milk\n",
       "70     whole milk\n",
       "72     whole milk\n",
       "97     whole milk\n",
       "106    whole milk\n",
       "Name: itemDescription, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing our content-based recommendation system with the seminal film Spy Kids\n",
    "give_rec('whole milk')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e23b7ae",
   "metadata": {},
   "source": [
    "## Item Based Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f5411d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pivot the DataFrame from long to wide format\n",
    "user_item_matrix = rs_df.pivot_table(index='Member_number', columns='itemDescription', values='Quantity', aggfunc='sum', fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8cea3c84",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>itemDescription</th>\n",
       "      <th>Instant food products</th>\n",
       "      <th>UHT-milk</th>\n",
       "      <th>abrasive cleaner</th>\n",
       "      <th>artif. sweetener</th>\n",
       "      <th>baby cosmetics</th>\n",
       "      <th>bags</th>\n",
       "      <th>baking powder</th>\n",
       "      <th>bathroom cleaner</th>\n",
       "      <th>beef</th>\n",
       "      <th>berries</th>\n",
       "      <th>...</th>\n",
       "      <th>turkey</th>\n",
       "      <th>vinegar</th>\n",
       "      <th>waffles</th>\n",
       "      <th>whipped/sour cream</th>\n",
       "      <th>whisky</th>\n",
       "      <th>white bread</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole milk</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>zwieback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Member_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</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>1004</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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 167 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "itemDescription  Instant food products  UHT-milk  abrasive cleaner  \\\n",
       "Member_number                                                        \n",
       "1000                                 0         0                 0   \n",
       "1001                                 0         0                 0   \n",
       "1002                                 0         0                 0   \n",
       "1003                                 0         0                 0   \n",
       "1004                                 0         0                 0   \n",
       "\n",
       "itemDescription  artif. sweetener  baby cosmetics  bags  baking powder  \\\n",
       "Member_number                                                            \n",
       "1000                            0               0     0              0   \n",
       "1001                            0               0     0              0   \n",
       "1002                            0               0     0              0   \n",
       "1003                            0               0     0              0   \n",
       "1004                            0               0     0              0   \n",
       "\n",
       "itemDescription  bathroom cleaner  beef  berries  ...  turkey  vinegar  \\\n",
       "Member_number                                     ...                    \n",
       "1000                            0     0        0  ...       0        0   \n",
       "1001                            0     1        0  ...       0        0   \n",
       "1002                            0     0        0  ...       0        0   \n",
       "1003                            0     0        0  ...       0        0   \n",
       "1004                            0     0        0  ...       0        0   \n",
       "\n",
       "itemDescription  waffles  whipped/sour cream  whisky  white bread  white wine  \\\n",
       "Member_number                                                                   \n",
       "1000                   0                   0       0            0           0   \n",
       "1001                   0                   1       0            1           0   \n",
       "1002                   0                   0       0            0           0   \n",
       "1003                   0                   0       0            0           0   \n",
       "1004                   0                   0       0            0           0   \n",
       "\n",
       "itemDescription  whole milk  yogurt  zwieback  \n",
       "Member_number                                  \n",
       "1000                      2       1         0  \n",
       "1001                      2       0         0  \n",
       "1002                      1       0         0  \n",
       "1003                      0       0         0  \n",
       "1004                      3       0         0  \n",
       "\n",
       "[5 rows x 167 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8b90810f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "itemDescription\n",
       "Instant food products    0\n",
       "UHT-milk                 0\n",
       "abrasive cleaner         0\n",
       "artif. sweetener         0\n",
       "baby cosmetics           0\n",
       "                        ..\n",
       "white bread              0\n",
       "white wine               0\n",
       "whole milk               2\n",
       "yogurt                   1\n",
       "zwieback                 0\n",
       "Name: 1000, Length: 167, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_item_matrix.loc[1000,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "93624ed4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the Person column\n",
    "data_cf = user_item_matrix.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "047ba7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_cf = data_cf.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0d26e2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the member number column\n",
    "data_cf = data_cf.drop(\"Member_number\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bc7f9cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a placeholder dataframe listing item vs. item\n",
    "data_icf = pd.DataFrame(index=data_cf.columns,columns=data_cf.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f6522105",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(data_icf.columns)) :\n",
    "    # Loop through the columns for each column\n",
    "    for j in range(0,len(data_icf.columns)) :\n",
    "      # Fill in placeholder with cosine similarities\n",
    "      data_icf.iloc[i,j] = 1-cosine(data_cf.iloc[:,i],data_cf.iloc[:,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4604edbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_sims1 = user_item_matrix.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "107536fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>itemDescription</th>\n",
       "      <th>Member_number</th>\n",
       "      <th>Instant food products</th>\n",
       "      <th>UHT-milk</th>\n",
       "      <th>abrasive cleaner</th>\n",
       "      <th>artif. sweetener</th>\n",
       "      <th>baby cosmetics</th>\n",
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       "      <th>bathroom cleaner</th>\n",
       "      <th>beef</th>\n",
       "      <th>...</th>\n",
       "      <th>turkey</th>\n",
       "      <th>vinegar</th>\n",
       "      <th>waffles</th>\n",
       "      <th>whipped/sour cream</th>\n",
       "      <th>whisky</th>\n",
       "      <th>white bread</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole milk</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>zwieback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 168 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "itemDescription  Member_number  Instant food products  UHT-milk  \\\n",
       "0                         1000                      0         0   \n",
       "1                         1001                      0         0   \n",
       "2                         1002                      0         0   \n",
       "3                         1003                      0         0   \n",
       "4                         1004                      0         0   \n",
       "\n",
       "itemDescription  abrasive cleaner  artif. sweetener  baby cosmetics  bags  \\\n",
       "0                               0                 0               0     0   \n",
       "1                               0                 0               0     0   \n",
       "2                               0                 0               0     0   \n",
       "3                               0                 0               0     0   \n",
       "4                               0                 0               0     0   \n",
       "\n",
       "itemDescription  baking powder  bathroom cleaner  beef  ...  turkey  vinegar  \\\n",
       "0                            0                 0     0  ...       0        0   \n",
       "1                            0                 0     1  ...       0        0   \n",
       "2                            0                 0     0  ...       0        0   \n",
       "3                            0                 0     0  ...       0        0   \n",
       "4                            0                 0     0  ...       0        0   \n",
       "\n",
       "itemDescription  waffles  whipped/sour cream  whisky  white bread  white wine  \\\n",
       "0                      0                   0       0            0           0   \n",
       "1                      0                   1       0            1           0   \n",
       "2                      0                   0       0            0           0   \n",
       "3                      0                   0       0            0           0   \n",
       "4                      0                   0       0            0           0   \n",
       "\n",
       "itemDescription  whole milk  yogurt  zwieback  \n",
       "0                         2       1         0  \n",
       "1                         2       0         0  \n",
       "2                         1       0         0  \n",
       "3                         0       0         0  \n",
       "4                         3       0         0  \n",
       "\n",
       "[5 rows x 168 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sims1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "530e3ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a place holder matrix for similarities, and fill in the user name column\n",
    "data_sims = pd.DataFrame(index=data_sims1.index,columns=data_sims1.columns)\n",
    "data_sims.iloc[:,:1] = data_sims1.iloc[:,:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c796991e",
   "metadata": {
    "scrolled": true
   },
   "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>itemDescription</th>\n",
       "      <th>Member_number</th>\n",
       "      <th>Instant food products</th>\n",
       "      <th>UHT-milk</th>\n",
       "      <th>abrasive cleaner</th>\n",
       "      <th>artif. sweetener</th>\n",
       "      <th>baby cosmetics</th>\n",
       "      <th>bags</th>\n",
       "      <th>baking powder</th>\n",
       "      <th>bathroom cleaner</th>\n",
       "      <th>beef</th>\n",
       "      <th>...</th>\n",
       "      <th>turkey</th>\n",
       "      <th>vinegar</th>\n",
       "      <th>waffles</th>\n",
       "      <th>whipped/sour cream</th>\n",
       "      <th>whisky</th>\n",
       "      <th>white bread</th>\n",
       "      <th>white wine</th>\n",
       "      <th>whole milk</th>\n",
       "      <th>yogurt</th>\n",
       "      <th>zwieback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 168 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "itemDescription Member_number Instant food products UHT-milk abrasive cleaner  \\\n",
       "0                        1000                   NaN      NaN              NaN   \n",
       "1                        1001                   NaN      NaN              NaN   \n",
       "2                        1002                   NaN      NaN              NaN   \n",
       "3                        1003                   NaN      NaN              NaN   \n",
       "4                        1004                   NaN      NaN              NaN   \n",
       "\n",
       "itemDescription artif. sweetener baby cosmetics bags baking powder  \\\n",
       "0                            NaN            NaN  NaN           NaN   \n",
       "1                            NaN            NaN  NaN           NaN   \n",
       "2                            NaN            NaN  NaN           NaN   \n",
       "3                            NaN            NaN  NaN           NaN   \n",
       "4                            NaN            NaN  NaN           NaN   \n",
       "\n",
       "itemDescription bathroom cleaner beef  ... turkey vinegar waffles  \\\n",
       "0                            NaN  NaN  ...    NaN     NaN     NaN   \n",
       "1                            NaN  NaN  ...    NaN     NaN     NaN   \n",
       "2                            NaN  NaN  ...    NaN     NaN     NaN   \n",
       "3                            NaN  NaN  ...    NaN     NaN     NaN   \n",
       "4                            NaN  NaN  ...    NaN     NaN     NaN   \n",
       "\n",
       "itemDescription whipped/sour cream whisky white bread white wine whole milk  \\\n",
       "0                              NaN    NaN         NaN        NaN        NaN   \n",
       "1                              NaN    NaN         NaN        NaN        NaN   \n",
       "2                              NaN    NaN         NaN        NaN        NaN   \n",
       "3                              NaN    NaN         NaN        NaN        NaN   \n",
       "4                              NaN    NaN         NaN        NaN        NaN   \n",
       "\n",
       "itemDescription yogurt zwieback  \n",
       "0                  NaN      NaN  \n",
       "1                  NaN      NaN  \n",
       "2                  NaN      NaN  \n",
       "3                  NaN      NaN  \n",
       "4                  NaN      NaN  \n",
       "\n",
       "[5 rows x 168 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the same as our original data but with nothing filled in except the headers\n",
    "data_sims.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "15cd4abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_neighbours = pd.DataFrame(index=data_icf.columns,columns=range(1,12))\n",
    " \n",
    "# Loop through our similarity dataframe and fill in neighbouring item names\n",
    "for i in range(0,len(data_icf.columns)):\n",
    "    data_neighbours.iloc[i,:11] = data_icf.iloc[0:,i].sort_values(ascending=False)[:11].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8b5a94c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>itemDescription</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Instant food products</th>\n",
       "      <td>root vegetables</td>\n",
       "      <td>margarine</td>\n",
       "      <td>soda</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>chicken</td>\n",
       "      <td>soups</td>\n",
       "      <td>hard cheese</td>\n",
       "      <td>newspapers</td>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UHT-milk</th>\n",
       "      <td>other vegetables</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>soda</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>bottled water</td>\n",
       "      <td>shopping bags</td>\n",
       "      <td>frankfurter</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>root vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abrasive cleaner</th>\n",
       "      <td>frozen potato products</td>\n",
       "      <td>meat spreads</td>\n",
       "      <td>spread cheese</td>\n",
       "      <td>sausage</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>frankfurter</td>\n",
       "      <td>root vegetables</td>\n",
       "      <td>frozen meals</td>\n",
       "      <td>shopping bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>artif. sweetener</th>\n",
       "      <td>baby cosmetics</td>\n",
       "      <td>make up remover</td>\n",
       "      <td>bottled water</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>instant coffee</td>\n",
       "      <td>soda</td>\n",
       "      <td>hair spray</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>turkey</td>\n",
       "      <td>misc. beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baby cosmetics</th>\n",
       "      <td>skin care</td>\n",
       "      <td>artif. sweetener</td>\n",
       "      <td>oil</td>\n",
       "      <td>frankfurter</td>\n",
       "      <td>frozen dessert</td>\n",
       "      <td>cake bar</td>\n",
       "      <td>spread cheese</td>\n",
       "      <td>pot plants</td>\n",
       "      <td>domestic eggs</td>\n",
       "      <td>chewing gum</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>white bread</th>\n",
       "      <td>whole milk</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>soda</td>\n",
       "      <td>whipped/sour cream</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>root vegetables</td>\n",
       "      <td>bottled water</td>\n",
       "      <td>shopping bags</td>\n",
       "      <td>sausage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white wine</th>\n",
       "      <td>whole milk</td>\n",
       "      <td>soda</td>\n",
       "      <td>sausage</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>root vegetables</td>\n",
       "      <td>citrus fruit</td>\n",
       "      <td>domestic eggs</td>\n",
       "      <td>tropical fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whole milk</th>\n",
       "      <td>other vegetables</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>soda</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>sausage</td>\n",
       "      <td>bottled water</td>\n",
       "      <td>root vegetables</td>\n",
       "      <td>bottled beer</td>\n",
       "      <td>shopping bags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yogurt</th>\n",
       "      <td>whole milk</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>rolls/buns</td>\n",
       "      <td>soda</td>\n",
       "      <td>sausage</td>\n",
       "      <td>tropical fruit</td>\n",
       "      <td>root vegetables</td>\n",
       "      <td>bottled water</td>\n",
       "      <td>pip fruit</td>\n",
       "      <td>citrus fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zwieback</th>\n",
       "      <td>whole milk</td>\n",
       "      <td>soda</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>bottled beer</td>\n",
       "      <td>salty snack</td>\n",
       "      <td>curd</td>\n",
       "      <td>pastry</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>sugar</td>\n",
       "      <td>rolls/buns</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           2                 3   \\\n",
       "itemDescription                                                   \n",
       "Instant food products         root vegetables         margarine   \n",
       "UHT-milk                     other vegetables        whole milk   \n",
       "abrasive cleaner       frozen potato products      meat spreads   \n",
       "artif. sweetener               baby cosmetics   make up remover   \n",
       "baby cosmetics                      skin care  artif. sweetener   \n",
       "...                                       ...               ...   \n",
       "white bread                        whole milk        rolls/buns   \n",
       "white wine                         whole milk              soda   \n",
       "whole milk                   other vegetables        rolls/buns   \n",
       "yogurt                             whole milk  other vegetables   \n",
       "zwieback                           whole milk              soda   \n",
       "\n",
       "                                     4             5                   6   \\\n",
       "itemDescription                                                             \n",
       "Instant food products              soda    whole milk           chocolate   \n",
       "UHT-milk                     rolls/buns          soda      tropical fruit   \n",
       "abrasive cleaner          spread cheese       sausage    other vegetables   \n",
       "artif. sweetener          bottled water    whole milk      instant coffee   \n",
       "baby cosmetics                      oil   frankfurter      frozen dessert   \n",
       "...                                 ...           ...                 ...   \n",
       "white bread            other vegetables          soda  whipped/sour cream   \n",
       "white wine                      sausage        yogurt          rolls/buns   \n",
       "whole milk                       yogurt          soda      tropical fruit   \n",
       "yogurt                       rolls/buns          soda             sausage   \n",
       "zwieback               other vegetables  bottled beer         salty snack   \n",
       "\n",
       "                                     7                8                9   \\\n",
       "itemDescription                                                             \n",
       "Instant food products           chicken            soups      hard cheese   \n",
       "UHT-milk                  bottled water    shopping bags      frankfurter   \n",
       "abrasive cleaner             rolls/buns      frankfurter  root vegetables   \n",
       "artif. sweetener                   soda       hair spray   tropical fruit   \n",
       "baby cosmetics                 cake bar    spread cheese       pot plants   \n",
       "...                                 ...              ...              ...   \n",
       "white bread                      yogurt  root vegetables    bottled water   \n",
       "white wine             other vegetables  root vegetables     citrus fruit   \n",
       "whole milk                      sausage    bottled water  root vegetables   \n",
       "yogurt                   tropical fruit  root vegetables    bottled water   \n",
       "zwieback                           curd           pastry           yogurt   \n",
       "\n",
       "                                  10               11  \n",
       "itemDescription                                        \n",
       "Instant food products     newspapers           yogurt  \n",
       "UHT-milk                      yogurt  root vegetables  \n",
       "abrasive cleaner        frozen meals    shopping bags  \n",
       "artif. sweetener              turkey  misc. beverages  \n",
       "baby cosmetics         domestic eggs      chewing gum  \n",
       "...                              ...              ...  \n",
       "white bread            shopping bags          sausage  \n",
       "white wine             domestic eggs   tropical fruit  \n",
       "whole milk              bottled beer    shopping bags  \n",
       "yogurt                     pip fruit     citrus fruit  \n",
       "zwieback                       sugar       rolls/buns  \n",
       "\n",
       "[167 rows x 10 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print a sample\n",
    "data_neighbours.iloc[:,1:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d042bdb8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes for 'whole milk':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2     other vegetables\n",
       "3           rolls/buns\n",
       "4               yogurt\n",
       "5                 soda\n",
       "6       tropical fruit\n",
       "7              sausage\n",
       "8        bottled water\n",
       "9      root vegetables\n",
       "10        bottled beer\n",
       "11       shopping bags\n",
       "Name: whole milk, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the index you want to retrieve attributes for\n",
    "selected_index = 'whole milk'\n",
    "\n",
    "# Get the list of 5 attributes for the specified index\n",
    "attributes_list = data_neighbours.loc[selected_index] #.tolist()\n",
    "\n",
    "# Print the result\n",
    "print(f\"Attributes for '{selected_index}':\")\n",
    "attributes_list[1:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "10786d34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attributes for 'whole milk':\n",
      "['other vegetables', 'rolls/buns', 'yogurt', 'soda', 'tropical fruit', 'sausage', 'bottled water', 'root vegetables', 'bottled beer', 'shopping bags']\n"
     ]
    }
   ],
   "source": [
    "# Specify the index you want to retrieve attributes for\n",
    "selected_index = 'whole milk'\n",
    "\n",
    "# Get the list of 10 attributes for the specified index\n",
    "attributes_list = data_neighbours.loc[selected_index, 2:11].tolist()\n",
    "\n",
    "# Print the result\n",
    "print(f\"Attributes for '{selected_index}':\")\n",
    "print(attributes_list)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7e97b0",
   "metadata": {},
   "source": [
    "# Comparative Analysis of Content and Collaborative Filtering: Machine Learning Models for Item-Item Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324a8749",
   "metadata": {},
   "source": [
    "### Content Filtering Vs Collaborative Filtering:\n",
    "\n",
    "According to (Shivam, 2022), Content Filtering, recommends items based on the content similarity of items. It relies on the items’ characteristics. While Collaborative Filtering, recommends items based on user-item interactions, finding items that are similar in terms of user preferences. It is well known, that the choice between content-based and collaborative filtering depends on the specific use case and the available data.\n",
    "\n",
    "For Content Filtering, TF-IDF (Term Frequency-Inverse Document Frequency) and sigmoid kernel for similarity computation were used as a method. In this paper, content filtering fails to recommend similar items based on the users’ preferences. All results from the recommendation list are like the given item. The chosen dataset does not bring information regarding items, only the names. The paper evaluates that the dataset is not suitable for content filtering, once it relies too much on item features.\n",
    "\n",
    "For Collaborative Filtering, the cosine similarity method was used to build a user-item matrix between columns. Given an item 'whole milk', collaborative filtering recommends similar items based on the preferences of users who liked 'whole milk.' The similarity is calculated using the user-item interaction matrix. The results show the top 5 items that are most like 'whole milk' include 'other vegetables,' 'rolls/buns,' 'yogurt,' 'soda,' and 'tropical fruit.' It is successfully capturing personalized recommendations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ff3de1",
   "metadata": {},
   "source": [
    "# Justification of Recommendations: Conceptual Insights for the Chosen Scenario"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f311783",
   "metadata": {},
   "source": [
    "In conclusion, in the content filtering results, it seems like the system is recommending the same item ('whole milk') rather than similar items. This is because the content-based filtering approach is designed to recommend items that are similar in content to the given item, and in this case, 'whole milk' is considered most like itself.\n",
    "\n",
    "Given that, this paper recommends the use of Collaborative Filtering. It is effective in capturing personalized recommendations. Collaborative filtering is recommending items that are frequently co-purchased or interacted with by users who have also interacted with 'whole milk'. So, in collaborative filtering, you see a list of items that tend to be chosen by users who have chosen 'whole milk', and these items are different from 'whole milk' itself.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f8f834",
   "metadata": {},
   "source": [
    "# Market Basket Analysis: Applying Apriori and FP-Growth Algorithms to Extract Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2857d60c",
   "metadata": {},
   "source": [
    "## Market Basket Analysis\n",
    "\n",
    "In retail organizations, market basket analysis is a crucial part of analytical CRM. Finding correlations or co-occurrences from transactional data through analysis can enable you to offer related products together, increasing revenue in the process. Many businesses have heavily relied on market basket analysis to identify product linkages and inform retailer promotion strategies (Charlet and Kumar, 2012). \n",
    "\n",
    "According to (Manpreet and Shivani, 2016), affinity analysis or association rule learning are other names for market basket analysis (MBA). In the retail industry, it gives retailers the knowledge they need to comprehend customer purchasing patterns and make informed decisions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c16c6a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "mba_df = pd.read_csv('basket.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0a0088cf",
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>whole milk</td>\n",
       "      <td>pastry</td>\n",
       "      <td>salty snack</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sausage</td>\n",
       "      <td>whole milk</td>\n",
       "      <td>semi-finished bread</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>soda</td>\n",
       "      <td>pickled vegetables</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>canned beer</td>\n",
       "      <td>misc. beverages</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sausage</td>\n",
       "      <td>hygiene articles</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0                   1                    2       3    4    5  \\\n",
       "0   whole milk              pastry          salty snack     NaN  NaN  NaN   \n",
       "1      sausage          whole milk  semi-finished bread  yogurt  NaN  NaN   \n",
       "2         soda  pickled vegetables                  NaN     NaN  NaN  NaN   \n",
       "3  canned beer     misc. beverages                  NaN     NaN  NaN  NaN   \n",
       "4      sausage    hygiene articles                  NaN     NaN  NaN  NaN   \n",
       "\n",
       "     6    7    8    9   10  \n",
       "0  NaN  NaN  NaN  NaN  NaN  \n",
       "1  NaN  NaN  NaN  NaN  NaN  \n",
       "2  NaN  NaN  NaN  NaN  NaN  \n",
       "3  NaN  NaN  NaN  NaN  NaN  \n",
       "4  NaN  NaN  NaN  NaN  NaN  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mba_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cb7ed374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose the DataFrame\n",
    "df_transposed = mba_df.transpose()\n",
    "\n",
    "# Convert each row to a list of items, ensuring all items are strings\n",
    "transactions = df_transposed.apply(lambda row: [str(item) for item in row.dropna()], axis=1).tolist()\n",
    "\n",
    "# Convert the list of lists into a one-hot encoded DataFrame\n",
    "te = TransactionEncoder()\n",
    "te_ary = te.fit(transactions).transform(transactions)\n",
    "df_encoded = pd.DataFrame(te_ary, columns=te.columns_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5197ab79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apriori Algorithm\n",
    "frequent_itemsets_apriori = apriori(df_encoded, min_support=0.8, use_colnames=True, max_len=3)\n",
    "\n",
    "# Apriori Association Rules\n",
    "rules_apriori = association_rules(frequent_itemsets_apriori, metric=\"confidence\", min_threshold=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1fb7dbc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Apriori Association Rules:\n"
     ]
    },
    {
     "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",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(canned beer)</td>\n",
       "      <td>(butter)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
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       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6404</th>\n",
       "      <td>(sugar, frozen vegetables)</td>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6406</th>\n",
       "      <td>(sugar)</td>\n",
       "      <td>(hard cheese, frozen vegetables)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6407</th>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>(hard cheese, sugar)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6408</th>\n",
       "      <td>(hard cheese, whipped/sour cream)</td>\n",
       "      <td>(frozen vegetables)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6409</th>\n",
       "      <td>(hard cheese, frozen vegetables)</td>\n",
       "      <td>(whipped/sour cream)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6410</th>\n",
       "      <td>(whipped/sour cream, frozen vegetables)</td>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6411</th>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>(whipped/sour cream, frozen vegetables)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6412</th>\n",
       "      <td>(whipped/sour cream)</td>\n",
       "      <td>(hard cheese, frozen vegetables)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6414</th>\n",
       "      <td>(white bread, frozen vegetables)</td>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  antecedents  \\\n",
       "0                               (canned beer)   \n",
       "6404               (sugar, frozen vegetables)   \n",
       "6406                                  (sugar)   \n",
       "6407                      (frozen vegetables)   \n",
       "6408        (hard cheese, whipped/sour cream)   \n",
       "6409         (hard cheese, frozen vegetables)   \n",
       "6410  (whipped/sour cream, frozen vegetables)   \n",
       "6411                            (hard cheese)   \n",
       "6412                     (whipped/sour cream)   \n",
       "6414         (white bread, frozen vegetables)   \n",
       "\n",
       "                                  consequents  antecedent support  \\\n",
       "0                                    (butter)            0.818182   \n",
       "6404                            (hard cheese)            0.818182   \n",
       "6406         (hard cheese, frozen vegetables)            0.818182   \n",
       "6407                     (hard cheese, sugar)            0.818182   \n",
       "6408                      (frozen vegetables)            0.818182   \n",
       "6409                     (whipped/sour cream)            0.818182   \n",
       "6410                            (hard cheese)            0.818182   \n",
       "6411  (whipped/sour cream, frozen vegetables)            0.818182   \n",
       "6412         (hard cheese, frozen vegetables)            0.818182   \n",
       "6414                            (hard cheese)            0.818182   \n",
       "\n",
       "      consequent support   support  confidence      lift  leverage  \\\n",
       "0               0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6404            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6406            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6407            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6408            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6409            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6410            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6411            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6412            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "6414            0.818182  0.818182         1.0  1.222222   0.14876   \n",
       "\n",
       "      conviction  zhangs_metric  \n",
       "0            inf            1.0  \n",
       "6404         inf            1.0  \n",
       "6406         inf            1.0  \n",
       "6407         inf            1.0  \n",
       "6408         inf            1.0  \n",
       "6409         inf            1.0  \n",
       "6410         inf            1.0  \n",
       "6411         inf            1.0  \n",
       "6412         inf            1.0  \n",
       "6414         inf            1.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\nApriori Association Rules:\")\n",
    "rules_apriori_table = rules_apriori[['antecedents', 'consequents', 'antecedent support', 'consequent support',\n",
    "                                     'support', 'confidence', 'lift', 'leverage', 'conviction', 'zhangs_metric']]\n",
    "\n",
    "rules_apriori_table.sort_values(by='confidence', ascending=False).drop_duplicates(subset='antecedents').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "80eaca0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FP-Growth Algorithm\n",
    "frequent_itemsets_fpgrowth = fpgrowth(df_encoded, min_support=0.8, use_colnames=True, max_len=3) \n",
    "\n",
    "# FP-Growth Association Rules\n",
    "rules_fpgrowth = association_rules(frequent_itemsets_fpgrowth, metric=\"confidence\", min_threshold=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "4ab4c513",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FP-Growth Association Rules:\n"
     ]
    },
    {
     "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",
       "      <th>zhangs_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(whole milk)</td>\n",
       "      <td>(newspapers)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.07438</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6435</th>\n",
       "      <td>(hygiene articles, rolls/buns)</td>\n",
       "      <td>(sugar)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6437</th>\n",
       "      <td>(hygiene articles)</td>\n",
       "      <td>(sugar, rolls/buns)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6438</th>\n",
       "      <td>(sugar)</td>\n",
       "      <td>(hygiene articles, rolls/buns)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6439</th>\n",
       "      <td>(rolls/buns)</td>\n",
       "      <td>(hygiene articles, sugar)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6440</th>\n",
       "      <td>(hard cheese, sugar)</td>\n",
       "      <td>(rolls/buns)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6441</th>\n",
       "      <td>(hard cheese, rolls/buns)</td>\n",
       "      <td>(sugar)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6442</th>\n",
       "      <td>(sugar, rolls/buns)</td>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6443</th>\n",
       "      <td>(hard cheese)</td>\n",
       "      <td>(sugar, rolls/buns)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6447</th>\n",
       "      <td>(sugar, frozen vegetables)</td>\n",
       "      <td>(rolls/buns)</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.222222</td>\n",
       "      <td>0.14876</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         antecedents                     consequents  \\\n",
       "0                       (whole milk)                    (newspapers)   \n",
       "6435  (hygiene articles, rolls/buns)                         (sugar)   \n",
       "6437              (hygiene articles)             (sugar, rolls/buns)   \n",
       "6438                         (sugar)  (hygiene articles, rolls/buns)   \n",
       "6439                    (rolls/buns)       (hygiene articles, sugar)   \n",
       "6440            (hard cheese, sugar)                    (rolls/buns)   \n",
       "6441       (hard cheese, rolls/buns)                         (sugar)   \n",
       "6442             (sugar, rolls/buns)                   (hard cheese)   \n",
       "6443                   (hard cheese)             (sugar, rolls/buns)   \n",
       "6447      (sugar, frozen vegetables)                    (rolls/buns)   \n",
       "\n",
       "      antecedent support  consequent support   support  confidence      lift  \\\n",
       "0               0.818182            0.909091  0.818182         1.0  1.100000   \n",
       "6435            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6437            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6438            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6439            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6440            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6441            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6442            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6443            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "6447            0.818182            0.818182  0.818182         1.0  1.222222   \n",
       "\n",
       "      leverage  conviction  zhangs_metric  \n",
       "0      0.07438         inf            0.5  \n",
       "6435   0.14876         inf            1.0  \n",
       "6437   0.14876         inf            1.0  \n",
       "6438   0.14876         inf            1.0  \n",
       "6439   0.14876         inf            1.0  \n",
       "6440   0.14876         inf            1.0  \n",
       "6441   0.14876         inf            1.0  \n",
       "6442   0.14876         inf            1.0  \n",
       "6443   0.14876         inf            1.0  \n",
       "6447   0.14876         inf            1.0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rules_fpgrowth_table = rules_fpgrowth[['antecedents', 'consequents', 'antecedent support', 'consequent support',\n",
    "                                       'support', 'confidence', 'lift', 'leverage', 'conviction', 'zhangs_metric']]\n",
    "print(\"\\nFP-Growth Association Rules:\")\n",
    "rules_fpgrowth_table.sort_values(by='confidence', ascending=False).drop_duplicates(subset='antecedents').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ba31a0",
   "metadata": {},
   "source": [
    "# Major Divergence Between Models: A Comparative Analysis of Machine Learning Results Using Apriori and FP-Growth Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de8498b",
   "metadata": {},
   "source": [
    "### The major divergence between models:\n",
    "\n",
    "Machine Learning Models Apriori and FP-Growth have the same main objective of identifying frequent itemsets in transaction datasets. Both approaches are to find patterns in the data, revealing important patterns in the data, in machine learning outputs. Apriori and FP-Growth are machine learning algorithms used for Market Basket analysis, but they have different approaches. Apriori is a kind of candidate generation method, that scans candidate itemsets until finds the frequent ones. \n",
    "\n",
    "In contrast, FP-Growth uses the frequency pattern on the data to growth approach, creating a tree, that is called FP-Tree. The tree represents the dataset and efficiently mining in two passes. This distinction leads to significant divergences in efficiency. This distinction leads to significant divergences in efficiency. When working with large datasets and sparse data, Apriori necessitates numerous database scans, which increases computational costs and memory use. In contrast, FP-Growth reduces runtime, memory requirements, and scan times, which makes it a better option for association rule mining jobs.\n",
    "\n",
    "### Comparison and contrast of the machine learning results:\n",
    "\n",
    "In this Market Basket Analysis, as given before, the association rules are created from two different algorithms, Apriori and FP-Growth. The results show the revealed associations between items in data based on their support, confidence, lift, and other metrics. \n",
    "\n",
    "According to (Charlet and Kumar, 2012), frequent itemsets are used to generate association rules, with support and confidence serving as threshold values. The term \"frequent itemset\" refers to collections of items with the least amount of support. The percentage of transactions in the data set that contain an itemset is known as the itemset’s support. The degree of assurance or dependability connected to each pattern that is found is known as confidence. The generated association rules rely on confidence.\n",
    "\n",
    "In the Apriori results, the rules reveal strong associations between items such as \"canned beer\" and \"butter,\" \"sugar\" and \"hard cheese,\" and various combinations of \"frozen vegetables,\" \"whipped/sour cream,\" and \"hard cheese.\" \n",
    "On the other hand, the FP-Growth results show associations like \"whole milk\" and \"newspapers,\" as well as patterns involving \"hygiene articles,\" \"rolls/buns,\" and \"sugar.\" Like Apriori, the lift values are greater than 1, suggesting positive correlations.\n",
    "\n",
    "For both algorithms, the lift values are consistently above 1, indicating positive associations. Lift is a metric that quantifies the degree to which the consequent is more likely than expected given the antecedent. A rule with a high lift indicates that buying the antecedent also greatly increases the likelihood of buying the consequent. A confidence of 0.8 means there is an 80% likelihood that the associated set of items. it measures the likelihood that the consequent item will be bought if the antecedent item is bought. The high confidence and lift indicate a strong association between the antecedent and the consequent.\n",
    "\n",
    "In conclusion, it is crucial to note that, due to the algorithm’s methodologies, the specific associations and patterns discovered may vary. FP-Growth uses a frequent pattern growth strategy, while Apriori uses a candidate generation approach. FP-Growth shows to be more efficient, with quicker execution. Both algorithms reveal associations in the data, but the specific rules, efficiency of computation and memory usage are different from one to another. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1a5b25",
   "metadata": {},
   "source": [
    "# Designing an Interactive Dashboard for Older Adults (65+): Incorporating Features to Summarize Key Data Aspects and Justifying Dataset Suitability for Machine Learning Models in Online Retail\n",
    "\n",
    "This project developed a sophisticated and impressive interactive Uder-friendly dashboard aimed at older adults (+65). The objective of the dashboard is to summarise important aspects of the data used to apply recommendation systems algorithms, insights, trends, and machine learning results.\n",
    "\n",
    "Why old adults?  In 2022, 771 million individuals worldwide were over 65 years of age or older, making up about 10% of the world's population (Alvarez, 2023). And according to (Tong, 2023) by the end of 2031, more than 25% of the workforce will be composed of older adults, in the G7 countries. \n",
    "\n",
    "The Dashboard Header contains its title Groceries Sales Dashboard. \n",
    "\n",
    "On the header left corner, below the title, it is possible to select the date range. It allows users to visualize any period wanted in the data. \n",
    "\n",
    "On the central top section of the dashboard, users can utilize a dropdown to select their preferred frequency for data visualization including options like day, day of the week, and month. \n",
    "On the header right corner, another dropdown allows users to pick a specific item. The resulting dash table, positioned in the top right grid, displays the top 10 items recommended by the Item-based Recommendation System analysed in the first part of this paper.\n",
    "\n",
    "In addition to the recommendation table cited above, other three visualizations are provided: The Line graph showing the number of items sold that interacts with the date range picker and the data frequency dropdown. The Bar graph shows the Top 10 products sold, and the bottom 10 products sold table. Bar graph and bottom 10 table, both interact with the date range picker. \n",
    "\n",
    "Users can get significant insights from the data in a quick navigation, such as:\n",
    "\n",
    "- A noticeable increase in sales in 2015 compared to 2014.\n",
    "- August exhibits the highest sales performance, while February represents the lowest performance in the year 2015.\n",
    "- Wednesdays demonstrate the best sales performance, while Mondays indicate the lowest. This insight could drive decision-makers in scheduling advertising for Wednesdays when the supermarket is expected to be at full capacity and creating sales campaigns for Mondays to boost sales.\n",
    "- The bar graph reveals the most sold items, with the ability to discern seasonal patterns if used in collaboration with the data range picker, such as items selling the most in December close to Christmas.\n",
    "- The dash table showing the bottom 10 products sold assists decision-makers in evaluating products that may need to be discontinued or included in promotional deals to increase sales.\n",
    "- The dash table generated by the item-based recommendation system facilitates quick assessment and comparison of recommended items.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8cd7e0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from my_function import update_items_sold_bar_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "75dea64d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"650\"\n",
       "            src=\"http://127.0.0.1:8055/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x267c8287750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce the Dash app\n",
    "app = dash.Dash(__name__)\n",
    "\n",
    "# Produce the dashboard Layout \n",
    "app.layout = html.Div([\n",
    "    html.H1(\"Groceries Sales Dashboard\", style={'textAlign': 'center', 'fontSize': 50,\n",
    "                                                'font-family': 'Arial', 'color': '#440154'}), # Dashboard title\n",
    "\n",
    "    # Create dashboard top using html.Div\n",
    "    html.Div([\n",
    "        \n",
    "        # Create date range picker (position: TOP LEFT)\n",
    "        html.Div([\n",
    "            html.Div(children=\"Select Date Range\", className=\"menu-title\", \n",
    "                     style={'fontSize': 30, 'font-family': 'Arial'}),\n",
    "            dcc.DatePickerRange(\n",
    "                id=\"date-range-picker\",\n",
    "                start_date=df['Date'].min().date(),\n",
    "                end_date=df['Date'].max().date(),\n",
    "                display_format=\"YYYY-MM-DD\",\n",
    "                className='date-picker'),\n",
    "        ], style={'flex': 3.5, 'margin': '20px', 'textAlign': 'center'}),  \n",
    "\n",
    "        # Generate aggregation dropdown to select data frequency (position: TOP CENTER)\n",
    "        html.Div([\n",
    "            html.Div(children=\"Select Data Frequency\", className=\"menu-title\", \n",
    "                     style={'fontSize': 30, 'font-family': 'Arial'}),\n",
    "            dcc.Dropdown(\n",
    "                id='aggregation-dropdown',\n",
    "                options=[\n",
    "                    {'label': 'Day', 'value': 'day'},\n",
    "                    {'label': 'Day of Week', 'value': 'day_of_week'},\n",
    "                    {'label': 'Month', 'value': 'month'}],\n",
    "                value='month',\n",
    "                multi=False,\n",
    "                style={'width': '100%', 'margin': 'auto', 'font-family': 'Arial', 'fontSize': 24},),\n",
    "        ], style={'flex': 3, 'margin': '20px'}),  \n",
    "\n",
    "        # Create dropdown for select item for recommendation system  (position: TOP RIGHT)\n",
    "        html.Div([\n",
    "            html.Div(children=\"Select Item for Recommendations\", className=\"menu-title\", \n",
    "                     style={'fontSize': 30, 'font-family': 'Arial'}),\n",
    "            dcc.Dropdown(\n",
    "                id='item-dropdown',\n",
    "                options=[{'label': item, 'value': item} for item in user_item_matrix.columns],\n",
    "                value='whole milk',  \n",
    "                multi=False,\n",
    "                style={'width': '100%', 'margin': 'auto', 'font-family': 'Arial', 'fontSize': 24},),\n",
    "        ], style={'flex': 2.5, 'margin': '20px'}), \n",
    "    ], style={'display': 'flex', 'justify-content': 'space-between'}),  \n",
    "\n",
    "    # Produce the dashboard body (Arrange grid 2x2)\n",
    "    \n",
    "    # Dashboard TOP row\n",
    "    html.Div([\n",
    "              \n",
    "        # Produce a bar graph (position: TOP LEFT)\n",
    "        html.Div([\n",
    "            dcc.Graph(id='bar-plot-items-sold'),], style={'flex': 7.5, 'fontSize': 30}),\n",
    "\n",
    "        # Display recommendation system dash table (position: TOP RIGHT)\n",
    "        html.Div([\n",
    "            html.H3(\"Item-based Recommendation System\", style={'font-family': 'Arial', 'fontSize': 30}),\n",
    "            dash_table.DataTable(\n",
    "                id='recommendation-table',\n",
    "                columns=[\n",
    "                    {'name': 'Rank', 'id': 'Rank'},\n",
    "                    {'name': 'Recommended Item', 'id': 'RecommendedItem'},],\n",
    "                style_table={'height': '400px', 'overflowY': 'auto','border': '1px solid black','width': '500px'},\n",
    "                style_cell={'textAlign': 'center'},),\n",
    "        ], style={'flex': 2.5, 'fontSize': 24, 'margin-left': '20px'}),\n",
    "    ], style={'display': 'flex', 'justify-content': 'space-between', 'margin': '20px'}),\n",
    "\n",
    "    # Dashboard BOTTON row \n",
    "    html.Div([\n",
    "        \n",
    "        # Produce top 10 Products bar graph (position: BOTTOM LEFT)\n",
    "        html.Div([\n",
    "            dcc.Graph(id='top-10-bar-plot'),\n",
    "        ], style={'flex': 7.5, 'fontSize': 24, 'margin-left': '20px'}),\n",
    "\n",
    "        # Produce bottom 10 Products dash table (position: BOTTOM RIGHT)\n",
    "        html.Div([\n",
    "            html.H3(\"Bottom 10 Products\", style={'font-family': 'Arial', 'fontSize': 30}),\n",
    "            dash_table.DataTable(\n",
    "                id='bottom-10-table',\n",
    "                columns=[\n",
    "                    {'name': 'Item', 'id': 'itemDescription'},\n",
    "                    {'name': 'Quantity Sold', 'id': 'Quantity'}],\n",
    "                style_table={'height': '400px', 'overflowY': 'auto', 'border': '1px solid black', 'width': '500px'},\n",
    "                style_cell={'textAlign': 'center'},),\n",
    "        ], style={'flex': 2.5, 'fontSize': 24}),\n",
    "    ], style={'display': 'flex', 'justify-content': 'space-between', 'margin': '20px'}),\n",
    "\n",
    "])\n",
    "\n",
    "# Set callback to update the bar graph and tables based on the selected date range and aggregation period\n",
    "@app.callback(\n",
    "    Output('bar-plot-items-sold', 'figure'),\n",
    "    Output('top-10-bar-plot', 'figure'),\n",
    "    Output('bottom-10-table', 'data'),\n",
    "    Output('recommendation-table', 'data'),\n",
    "    [Input('date-range-picker', 'start_date'),\n",
    "     Input('date-range-picker', 'end_date'),\n",
    "     Input('aggregation-dropdown', 'value'),\n",
    "     Input('item-dropdown', 'value')])\n",
    "\n",
    "def update_items_sold_callback(start_date, end_date, aggregation_period, selected_item):\n",
    "    \n",
    "    # Call the function my_function.py from external source\n",
    "    line_fig, top_10_bar_fig, bottom_10_data, recommendation_table_data = update_items_sold_bar_plot(\n",
    "        df, start_date, end_date, aggregation_period, selected_item, data_neighbours)\n",
    "        \n",
    "    return line_fig, top_10_bar_fig, bottom_10_data, recommendation_table_data\n",
    "\n",
    "\n",
    "# Run the app\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True, port=8055)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8f8904",
   "metadata": {},
   "source": [
    "# To access the Dashboard on web browser\n",
    "\n",
    "- Replace \"your-machine-ip\" with your machin IP address: http://your-machine-ip:8055/\n",
    "\n",
    "- To find IP address run the command ipconfig (Windows) or ifconfig (Unix/Linux) in the terminal or command prompt.    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa4b948",
   "metadata": {},
   "source": [
    "# Interactive Sales Dashboard Illustration"
   ]
  },
  {
   "attachments": {
    "dash_pic.png": {
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    }
   },
   "cell_type": "markdown",
   "id": "6c090b38",
   "metadata": {},
   "source": [
    "![dash_pic.png](attachment:dash_pic.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d847dd32",
   "metadata": {},
   "source": [
    "# Designing a User-Friendly Dashboard for Older Adults: Considerations and Strategies for Addressing Age-Related Challenges in Interface Design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacd1700",
   "metadata": {},
   "source": [
    "It is well known, that ageing comes with inevitable physiological and cognitive changes. Design choices are made to overcome the challenges faced by older adults.\n",
    "\n",
    "According to (Polyuk, 2019) vision loss is what affect old user the most. It is important to consider visual accessibility when creating a visualization, especially increasing colour contrast. In this Dashboard project, the palette viridis is used. This palette contributes to a positive user experience, offering smooth transitions between colours, and avoiding confusion in interpreting the data usually caused by abrupt changes. \n",
    "\n",
    "(Polyuk, 2019)  Also states that text and font size should be kept large as much as possible. Minimum of 16px. In this paper, large fonts are used. Especially for the dashboard title 50px and visualization titles 30px. Other font sizes are set as 24px.\n",
    "\n",
    "To improve the interaction of older adults, this dashboard is sophisticated but simple. Containing gestures simple to perform such as simple horizontal, or vertical movements. \n",
    "\n",
    "According to (Barros, Leitão and Ribeiro, 2014) It is recommended pickers, dropdowns or checkboxes to the use of the keyboard be minimized. In this dashboard project, date ranger pick is added using dcc.DatePickerRange and two dropdowns are added as well, data frequency and item for recommendations dropdowns. \n",
    "\n",
    "In short, the dashboard layout is easy to understand. It's neat, making it clear what's important in the data, and it gives valuable insights to help decision-makers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c43a6f",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In conclusion, this academic paper explores the cross-disciplinary field of data analytics, combining Machine Learning and Data Visualization to produce a solid Recommendation System and Market Basket Analysis. This paper favours Collaborative filtering over Content filtering due its customized effectiveness. In the MBA making use of Apriori and FP-Growth algorithms, with FP-Growth proving more efficient for the chosen dataset.\n",
    "\n",
    "In the second part, the paper introduces a stunning and sophisticated interactive dashboard, produced in Dash, adapted for older adults. The dashboard prioritizes visual accessibility with large fonts, a user-friendly interface, simplified gestures, and a clean layout. Addressing the challenges associated with aging, the Groceries Sales dashboard allow users to select data ranges, visualize wanted data frequencies, and display personalized recommendations. Providing valuable insights to help decision-makers promote informed strategies. \n",
    "\n",
    "In heart, the project highlights the integration between advanced analytics, machine learning, and user-friendly interfaces in interpreting complex data related to the dynamic landscape of online retail.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf097d5b",
   "metadata": {},
   "source": [
    "# Reference List\n",
    "\n",
    "- Alvarez, P. (2023). Charted: The World’s Aging Population from 1950 to 2100. [online] Visual Capitalist. Available at: https://www.visualcapitalist.com/cp/charted-the-worlds-aging-population-1950-to-2100/ [Accessed 28 Nov. 2023].\n",
    "\n",
    "- Barros, A.C. de, Leitão, R. and Ribeiro, J. (2014). Design and Evaluation of a Mobile User Interface for Older Adults: Navigation, Interaction and Visual Design Recommendations. Procedia Computer Science, 27, pp.369–378. doi:https://doi.org/10.1016/j.procs.2014.02.041.\n",
    "\n",
    "- Ezz, M. and Elshenawy, A. (2019). Adaptive recommendation system using machine learning algorithms for predicting student’s best academic program. Education and Information Technologies, [online] 25(4), pp.2733–2746. doi:https://doi.org/10.1007/s10639-019-10049-7.\n",
    "\n",
    "- Khanal, S.S., Prasad, P.W.C., Alsadoon, A. and Maag, A. (2019). A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies. [online] doi:https://doi.org/10.1007/s10639-019-10063-9.\n",
    "\n",
    "- Polyuk, S. (2019). A guide to interface design for older adults. [online] Toptal Design Blog. Available at: https://www.toptal.com/designers/ui/ui-design-for-older-adults [Accessed 25 Nov. 2023].\n",
    "\n",
    "- Portugal, I., Alencar, P. and Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, [online] 97, pp.205–227. doi:https://doi.org/10.1016/j.eswa.2017.12.020.\n",
    "\n",
    "- Roy, P.K., Chowdhary, S.S. and Bhatia, R. (2020). A Machine Learning approach for automation of Resume Recommendation system. Procedia Computer Science, [online] 167, pp.2318–2327. doi:https://doi.org/10.1016/j.procs.2020.03.284.\n",
    "\n",
    "- Tong, G.C. (2023). ‘Massive shift’: Older people will exceed a quarter of G7’s workforce by 2031, report says. [online] CNBC. Available at: https://www.cnbc.com/2023/07/19/aging-workforce-older-people-to-fill-over-150-million-jobs-globally.html [Accessed 11 2023]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52bf8a09",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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