This educational project showcases the application of the KMeans Clustering algorithm across three scenarios:
- 🧮 Manual implementation using NumPy — to understand how KMeans works under the hood
- 🔍 Clustering 2D data using
scikit-learn - 🚢 Titanic dataset analysis with real-world insights on passenger survival
Whether you're a beginner learning unsupervised learning or a data science enthusiast, this project provides both conceptual clarity and practical depth.
- Understand the math and logic behind KMeans
- Perform real-world data preprocessing and transformation
- Visualize clusters using PCA and interpret silhouette scores
- Analyze survival patterns using feature-based segmentation
KMeans-Clustering-Project/
├── data/ # Titanic dataset (Excel)
├── src/ # Python scripts
├── images/ # Plots and visuals
├── analysis_report.txt # Summary of insights
├── requirements.txt # Dependencies
├── README.md # Documentation
├── LICENSE # MIT License
└── .gitignore
# Install all required libraries
pip install -r requirements.txt
# Run the main script
python src/kmeans_clustering_project.py📎 Make sure the titanic.xls dataset is placed inside the data/ folder.
The project includes rich exploratory data analysis (EDA) before and after clustering:
- ✅ Survival distribution
- ✅ Age and Fare distribution
- ✅ Survival by gender and age group
- ✅ PCA 2D projection of clusters
- ✅ Cluster evaluation using silhouette score
| Description | Image Preview |
|---|---|
| Raw Data - Manual KMeans |  |
| Manual KMeans Clustering Result |  |
| Raw Data - Scikit-learn Example |  |
| Sklearn KMeans Result |  |
| Survival Distribution |  |
| Age Distribution |  |
| Fare vs Survival |  |
| Survival by Gender |  |
| Survival by Age Group |  |
This project is open-source under the MIT License.
Feel free to fork this repository, open issues, or submit pull requests to make it even better!