🔍 Hyperparameter Tuning with Cross-Validation & MLflow Tracking

This project demonstrates hyperparameter tuning on a regression model using Scikit-Learn's GridSearchCV, evaluated with Mean Squared Error (MSE), and tracked using MLflow for experiment management.

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📌 Project Highlights

• Model Optimization via Grid Search over a defined hyperparameter space
• Cross-Validation with cv=3 to ensure model generalization
• Metric Used: Mean Squared Error (MSE) for evaluation
• Experiment Tracking with MLflow (parameters, metrics, artifacts, and models)
• Best Model Selection based on the lowest average MSE across folds

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🛠️ Tech Stack

• Python
• Scikit-Learn
• MLflow
• Pandas

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📂 Project Structure

├── mlruns/              # MLflow logs (auto-generated)
├── house_prediction.py  # Script to run tuning + tracking
├── requirements.txt     # Required packages
└── README.md            # You're here!