🔵 K-Means Clustering

An unsupervised machine learning project that applies K-Means Clustering using both the Silhouette Score method and the Elbow Method to determine the optimal number of clusters and visualize clustering results.

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📌 Project Overview

This project explores K-Means clustering on a dataset using two popular techniques to select the optimal number of clusters (k):

• Silhouette Score Method
• Elbow Method (WCSS)

Both methods are implemented and their clustering outputs are visualized to compare cluster separation and compactness.

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📁 Project Structure

• k_mean_silhouette.ipynb — K-Means clustering using Silhouette Score
• k_mean_clustering.ipynb — K-Means clustering using Elbow Method
• dataset.csv — Dataset used for clustering
• silhouette_output.png — Clustering output using Silhouette method
• elbow_output.png — Clustering output using Elbow method
• silhouette_score_vs_k.png — Silhouette Score vs Number of Clusters
• wcss_vs_k.png — WCSS vs Number of Clusters
• README.md — Project documentation

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⚙️ Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib
• scikit-learn
• Jupyter Notebook

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🧠 Machine Learning Model

• Algorithm: K-Means Clustering
• Learning Type: Unsupervised Learning
• Distance Metric: Euclidean Distance
• Evaluation Methods: Silhouette Score, WCSS

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📊 Silhouette Method Output

The Silhouette method evaluates how well each data point fits within its assigned cluster.
A higher silhouette score indicates better-defined and well-separated clusters.

Clustering Result (Silhouette Method)

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📊 Elbow Method Output

The Elbow method uses Within-Cluster Sum of Squares (WCSS) to assess cluster compactness.
The clustering output shows the data grouped based on the selected optimal k.

Clustering Result (Elbow Method)

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📈 Evaluation Curves

Silhouette Score vs Number of Clusters

WCSS vs Number of Clusters

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▶️ How to Run

1. Clone the repository

git clone https://github.com/btboilerplate/kmean-Clustering.git  

2. Install required libraries

pip install numpy pandas matplotlib scikit-learn  

3. Open and run the notebooks

• k_mean_silhouette.ipynb
• k_mean_clustering.ipynb

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🧪 Key Observations

• Silhouette method highlights cluster separation quality
• Elbow method focuses on cluster compactness
• Both methods suggest a consistent and meaningful choice of k
• Visual outputs confirm clear cluster formation

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🚀 Future Improvements

• Apply clustering on real-world, high-dimensional datasets
• Compare with DBSCAN and Hierarchical Clustering
• Use PCA for dimensionality reduction
• Automate optimal cluster selection

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