Cluster-Analysis-Elbow-Method

Introduction

This repository implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm. The elbow method is a common technique to find the ideal value of k by plotting the total within-cluster sum of squares (WCSS) against different k values.

Dataset

The dataset used for this project is available in the "Data.csv" file.

Code Explanation

The code is written in Python and uses the following functions:

• euclidean_distance(a, b): Calculates the Euclidean distance between two points a and b.
• k_means(data, k): Implements the K-means clustering algorithm with k clusters.
• total_within_cluster_sum_of_squares(data, centroids, assignments): Calculates the total within-cluster sum of squares (WCSS) for a given set of data points, centroids, and cluster assignments.
• elbow_method(data, max_k): Uses the K-means algorithm to find WCSS for different values of k and returns a list of WCSS values.
• visualize_elbow_method(wcss): Plots the WCSS values against different k values to visualize the elbow method.

Usage

1. Clone the repository to your local machine.
2. Make sure you have Python and the required libraries installed (NumPy, Pandas, and Matplotlib).
3. Run the Python script main.py.
4. The script will output the optimal value of k and plot the elbow method graph.

Screenshot

Results

After running the code, the elbow method graph will be displayed showing the WCSS values for different k values. The optimal number of clusters (k) can be identified at the "elbow point" in the graph where the rate of WCSS reduction starts to slow down.

Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open a pull request or submit an issue.

Contact

For any questions or inquiries, you can reach me at sabdurrazzaq124@gmail.com.