🧠 Machine Learning Training Repository

Welcome to the Machine Learning Training repository!
This project is a collection of practical machine learning scripts and notebooks developed to demonstrate the core steps in building, training, and evaluating ML models. A key focus of this repo is predicting real estate prices using regression techniques.

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📂 Repository Structure

• Dragon Real Estates.ipynb – End-to-end implementation of a housing price predictor model.
• Model Usage.ipynb – Guide to using the saved model (Dragon.joblib) for real-world predictions.
• Outputs from different models/ – Contains results and outputs from different ML algorithms.
• data.csv – Dataset used for training and testing the model.
• Dragon.joblib – Pre-trained machine learning model saved using joblib.
• housing.data & housing.names – Additional dataset for experimentation.
• end-to-end-ml.zip – Compressed archive of the entire ML pipeline.

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🛠️ Technologies & Tools Used

• Language: Python
• Libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • joblib
  • jupyter

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🚀 How to Run the Project

1. Clone the repository

   git clone https://github.com/Reet-Kamlay/Machine-Learning.git
   cd Machine-Learning

2. Install dependencies

   pip install -r requirements.txt

3. Launch Jupyter Notebook

   jupyter notebook

   Open Dragon Real Estates.ipynb to explore and run the notebook.

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📌 Key Features

• 📊 Real-world dataset for housing price prediction.
• 🏗️ Full ML workflow: data preprocessing → model training → evaluation → deployment.
• 📈 Comparison of different regression models and their performance.
• 🔍 Hyperparameter tuning using GridSearchCV.
• 💾 Model saving and loading with joblib.

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🤝 Contributing

Feel free to fork the repository, open issues, or submit pull requests if you'd like to contribute or suggest improvements.

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Reet Kamlay
GitHub Profile • LinkedIn

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