Update README.md

README.md

-# Data Mining (DM) course - HW1
-## Association rules using Apriori and Frequent Pattern Growth (FP Growth) Algorithms
+# Data Mining (DM) course project
 ***By: Almouhannad Hafez***
 
 ***Dataset link: [The Bread Basket](https://www.kaggle.com/datasets/mittalvasu95/the-bread-basket)***
 
-> ***This project contains a jupyter notebook `hw1.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**
+> ***This project contains a python file `constants.ipynb` containing some fixed values used in other files, refered as `CONSTANTS` class***
 
-> ***This project contains a python file `constants.ipynb` containing some fixed values used in `hw1.ipynb`, refered as `CONSTANTS` class***
+> ***This project contains a python file `helpers.ipynb` containing some helper functions used in other files, refered as `HELPERS` class***
+
+## Requirements
+1. **Open a Terminal or Command Prompt**
+1. **Navigate to the Directory containing this repository**
+    ```bash
+    cd path/to/repository/folder
+    ```
+1. **Install the Requirements**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+## Contents
+> ***`1.data_preprocessing.ipynb`***
+- **Performing some operations on dataset before actual work**
+    - **Handling nulls/duplicates**
+    - **Setting columns data types correctly**
+    - **Converting to one-hot encoded**
+
+> ***`2.1.association_rules_apriori_fpg.ipynb`***  
+> ***`2.2.association_rules_eclat.ipynb`***
+- **Extracting rules using**
+    - **Apriori**
+    - **FP Growth**
+    - **Eclat**
+- **Performance comparison between Apriori and FP Growth**
 
-> ***This project contains a python file `helpers.ipynb` containing some helper functions used in `hw1.ipynb`, refered as `HELPERS` class***
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