batman230-sai/forest-fire-prediction

End-to-end ML project predicting Algerian forest fires using weather & FWI indices. Built with modular Python, FastAPI backend, Flutter app, MLflow tracking, Docker & CI/CD. From raw data to deployed prediction system in 30 days. # Forest Fire Prediction End-to-end ML project predicting Algerian forest fires using weather & FWI indices. Applying a meticulous, rigorous approach to data preparation and experimentation, this project scales from raw data to a deployed prediction system. ## Overview This repository houses the complete lifecycle of a production-level machine learning system. It is built with modular Python, DVC for data versioning, MLflow for remote experiment tracking via DagsHub, and sets the foundation for a robust API backend. ## Model Experimentation & Training The model predicts the Fire Weather Index (FWI) target variable based on critical environmental features. The dataset was split 80/20 for training and testing, and scaled using a robust preprocessing pipeline. To ensure the best possible fit without assumptions, 6 distinct algorithms were evaluated as baseline models: * Linear Regression * Ridge Regression * Lasso Regression * Support Vector Regressor (SVR) * Random Forest Regressor * XGBoost Regressor ### Final Tuned Model Following a rigorous 5-fold `GridSearchCV` testing 35 parameter candidates (totalling 175 fits), **Ridge Regression** emerged as the winning model. * **Optimal Hyperparameters**: `alpha`: 1.0, `solver`: 'sparse_cg' * **Test Performance**: Achieved a highly accurate R² Score of 0.9746. ## MLOps: Remote Tracking & Model Registry This project utilizes **DagsHub** as the remote backend for production MLOps: * **DVC (Data Version Control)** tracks large datasets (`train.csv`, `test.csv`) and binary artifacts securely in cloud storage. * **MLflow** tracks all experiment parameters, metrics, and models in a remote tracking server. * The final optimized model is officially registered in the MLflow Model Registry as **`Forest-Fire-Ridge-Predictor`** (Version 1). ## Pipeline & Deployment With the experimentation phase successfully locked in, the final optimized model and standard preprocessor are automatically exported to `artifacts/tuned_model.pkl` an …