ayushhh026/Algerian-Forest-Fire-Predictor

End-to-end ML regression pipeline predicting Forest Weather Index (FWI) using Ridge Regression • FastAPI • Deployed on AWS # Algerian Forest Fire Predictor 🔥 ``` ███████╗ ██████╗ ██████╗ ███████╗███████╗████████╗ ███████╗██╗██████╗ ███████╗ ██╔════╝██╔═══██╗██╔══██╗██╔════╝██╔════╝╚══██╔══╝ ██╔════╝██║██╔══██╗██╔════╝ █████╗ ██║ ██║██████╔╝█████╗ ███████╗ ██║ █████╗ ██║██████╔╝█████╗ ██╔══╝ ██║ ██║██╔══██╗██╔══╝ ╚════██║ ██║ ██╔══╝ ██║██╔══██╗██╔══╝ ██║ ╚██████╔╝██║ ██║███████╗███████║ ██║ ██║ ██║██║ ██║███████╗ ╚═╝ ╚═════╝ ╚═╝ ╚═╝╚══════╝╚══════╝ ╚═╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚══════╝ ``` ### End-to-End ML Regression Pipeline — From Raw Data to Cloud Deployment > **EDA · Feature Selection · Regularization · StandardScaler · FastAPI · AWS Deployment** --- ## What is This Project? A complete machine learning regression pipeline that predicts the **Fire Weather Index (FWI)** — a composite score used by meteorologists to assess wildfire danger — using the **Algerian Forest Fire Dataset**. The project covers the full ML lifecycle: - ✅ **Data Cleaning** — handling nulls, fixing dtypes, stripping whitespace, regional encoding - ✅ **EDA** — correlation heatmaps, pairplots, boxplots, class distribution analysis - ✅ **Feature Selection** — correlation-threshold-based multicollinearity removal - ✅ **Regularization Benchmarking** — Linear, Lasso, Ridge, ElasticNet, LassoCV, RidgeCV compared - ✅ **Deployment** — FastAPI REST API with HTML frontend, deployed on AWS --- ## Pipeline Overview ``` ┌──────────────────────────────────────────────────────────────┐ │ RAW DATASET │ │ Algerian Forest Fire (2 Regions · 244 rows) │ └────────────────────────┬─────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────┐ │ DATA CLEANING │ │ Null removal · dtype fixes · region encoding · dedup │ └────────────────────────┬─────────────────────────────────────┘ │ ▼ ┌───── …