Refactor folders

1.Association_rules/README.md

+# Data Mining (DM) course project - Part1. Association rules
+
+***Dataset link: [The Bread Basket](https://www.kaggle.com/datasets/mittalvasu95/the-bread-basket)***
+
+> ***This folder contains a python file `constants.ipynb` containing some fixed values used in other files, refered as `CONSTANTS` class***
+
+> ***This folder contains a python file `helpers.ipynb` containing some helper functions used in other files, refered as `HELPERS` class***
+
+## 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.association_rules_apriori_fpg.ipynb`***  
+> ***`3.association_rules_eclat.ipynb`***
+- **Extracting rules using**
+    - **Apriori**
+    - **FP Growth**
+    - **Eclat**
+- **Performance comparison between Apriori and FP Growth**

README.md

 # 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 python file `constants.ipynb` containing some fixed values used in other files, refered as `CONSTANTS` class***
-
-> ***This project contains a python file `helpers.ipynb` containing some helper functions used in other files, refered as `HELPERS` class***
+***Almouhannad Hafez + Mariam Khierbek***
 
 ## 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**
+1. **To install the required modules**
     ```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**
-
-> ***`3.1.clustering_k_means.ipynb`***  
-- **Exctracting clusters and some useful information about them using**
-    - **K-Means**
-- **Trying to find optimal number of clusters using `Elbow method`**
+## Contents:
+- Part1. Association rules
+- Part2. Clustering