Initial commit

README.md

+# Data Mining (DM) course - HW1
+## Association rules using Apriori and Frequent Pattern Growth (FP Growth) Algorithms
+***By: Almouhannad Hafez***
+
+***Dataset link: [The Bread Basket](https://www.kaggle.com/datasets/mittalvasu95/the-bread-basket)***
+
+> ***This project contains a jupyter notebook `hw.ipynb` containing the following steps:***
+> 1. **Setup requirements**
+> 1. **Data preprocessing**
+> 1. **Extracting rules using**
+>       - **Apriori**
+>       - **FP Growth**
+> 1. **Performance comparison between the two algorithms**

constants.py

+class CONSTANTS:
+    DATASET_PATH = 'data/bread_basket.csv'    
\ No newline at end of file

hw1.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ***0. Setup***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "import constants"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ***1. Data preprocessing***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ***2. Extracting rules using Apriori***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ***3. Extracting rules using FP Growth***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ***4. Performance comparison***"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}