K-Means Clustering – README

This project demonstrates K-Means clustering using synthetic data generated with make_blobs. It includes data visualization, elbow method for optimal cluster selection, and clustering visualization with centroids.

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📌 Project Overview

This notebook covers:

• Synthetic dataset generation
• Applying the K-Means algorithm
• Visualizing clusters and centroids
• Using the Elbow Method to find the optimal number of clusters
• Plotting results clearly

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📁 Dataset Generation

The dataset is generated using:

X, y = make_blobs(n_samples=2000, random_state=130)

This creates:

• 2000 samples
• Multiple cluster centers (randomly assigned)
• Features suitable for clustering algorithms

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🧭 Workflow Summary

1. Import Required Libraries

Includes NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.

2. Generate Synthetic Data

make_blobs creates a well-separated dataset ideal for clustering.

3. Apply K-Means Clustering

Initial clustering is done with:

k = 5
kmeans = KMeans(n_clusters=k)

Outputs:

• Predicted cluster labels
• Cluster centroids
• Scatter plot of clustered data

4. Find Optimal Number of Clusters (k)

Using the Elbow Method:

• Run K-Means for k = 1 to 10
• Store inertia (WCSS)
• Plot WCSS vs. k to identify the elbow point

5. Re-Cluster Using Optimal k

Example:

k = 3
kmeans = KMeans(n_clusters=k)

Visualizes:

• Data points colored by cluster assignment
• Centroids marked with X
• Grid, labels, and layout improvements for readability

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📊 Visualizations Included

• Raw clustered data (k=5)
• Elbow Method WCSS plot
• Final clustering visualization (k=3) with centroids

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🎯 Goal of the Project

To understand:

• How K-Means clustering works
• How synthetic datasets are useful for unsupervised learning experiments
• How to select an appropriate number of clusters
• How clustering results can be visualized effectively

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⚙️ Technologies Used

• Python 3
• NumPy
• Matplotlib & Seaborn
• Scikit-Learn

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🚀 How to Run This Notebook

1. Install required dependencies:

   pip install numpy pandas matplotlib seaborn scikit-learn

2. Run the notebook sequentially.

3. Modify k values to experiment with different cluster counts.

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✨ Author

This project demonstrates basic unsupervised learning using K-Means clustering.

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If you'd like, I can also:

• Add silhouette score analysis
• Add comparison with DBSCAN or hierarchical clustering
• Combine all your READMEs into one portfolio guide