K-Means Clustering Project 🚀

Welcome to the K-Means Clustering Project! This project demonstrates a complete pipeline for clustering data using the K-Means algorithm. It is designed to handle real-world datasets, preprocess them, and extract meaningful clusters. Perfect for showcasing your data analytics and machine learning skills in your portfolio! 🎓

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📂 Project Structure

force2020-ml-competition/
├── data/                     # Folder for datasets
│   └── force2020_data_unsupervised_learning.csv  # Dataset file
├── src/                      # Folder for source code
│   ├── kmeans_pipeline.py    # Main pipeline code
│   ├── utils.py              # Helper functions
│   ├── config.py             # Configuration settings
│   └── example.py            # Example execution script
├── requirements.txt          # Project dependencies
├── README.md                 # Project documentation
└── venv/                     # Virtual environment (not tracked)

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🌟 Features

• Data Preprocessing: Automatically handles missing values and scales data.
• Clustering: Implements K-Means with customizable parameters.
• Evaluation: Uses silhouette scores to evaluate cluster quality.
• Visualization: Includes plots for distributions, correlations, and cluster results.

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🚀 Getting Started

Follow these steps to set up and run the project:

1️⃣ Clone the Repository

git clone https://github.com/bautistao2/force2020-ml-competition.git
cd force2020-ml-competition

2️⃣ Set Up the Environment

Create and activate a virtual environment:

On Windows:

python -m venv venv
venv\Scripts\activate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

3️⃣ Add the Dataset

Place your dataset in the data/ folder. Make sure the file name matches the path specified in config.py:

DATA_PATH = "data/force2020_data_unsupervised_learning.csv"

4️⃣ Run the Example Script

Run the clustering pipeline:

python src/example.py

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📊 Example Outputs

• Silhouette Score: Quantifies the quality of the clustering.
• Cluster Visualizations: Plots showing how data points are grouped into clusters.
• CSV Output: Dataset with assigned cluster labels saved to data_with_clusters.csv.

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🛠 Technologies Used

• Python 🐍
• pandas for data manipulation
• numpy for numerical operations
• matplotlib and seaborn for visualization
• scikit-learn for machine learning algorithms

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📖 License

This project is licensed under the MIT License. Feel free to use it for learning, projects, or your portfolio! ✨

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💡 Ideas for Improvement

• Add support for additional clustering algorithms (e.g., DBSCAN, hierarchical clustering).
• Implement automated hyperparameter tuning for K-Means.
• Integrate PCA or t-SNE for dimensionality reduction.
• Build a simple web interface to upload datasets and visualize clusters.

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🙌 Contributing

Contributions are welcome! If you have ideas or want to report an issue, feel free to open a pull request or an issue on GitHub. 💻