📊 Day 02: Machine Learning Pipeline & Hyperparameter Tuning

An automated machine learning modeling pipeline that compares classical algorithms, tunes hyperparameters using Bayesian search with Optuna, and evaluates performance with classification metrics and saved model artifacts.

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🛠️ Key Features

• Model Comparison: Cross-validation across Logistic Regression, Random Forest, and XGBoost.
• Bayesian Optimization: Automated hyperparameter tuning using Optuna to maximize F1-Score.
• Evaluation Artifacts: Generates Confusion Matrix visual reports and computes ROC-AUC metrics.
• Model Serialization: Exports the trained model to best_xgboost_model.joblib for production serving.

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📂 Repository Structure

Day02_Machine_Learning_Pipeline/
├── artifacts/
│   ├── best_xgboost_model.joblib   # Serialized model
│   └── confusion_matrix.png        # Evaluation plot
├── ml_pipeline.py                  # Core ML training & tuning script
├── .gitignore
└── README.md

🚀 How to Run
1. Setup Environment
Bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install pandas numpy scikit-learn xgboost optuna matplotlib seaborn joblib
2. Execute Training & Tuning
Bash
python ml_pipeline.py
Part of the 7-Day Machine Learning Engineering Challenge.