preetham-11/forest_fire

Machine learning project predicting forest fire occurrence in Algeria using meteorological data and Fire Weather Index (FWI) components with Flask web deployment ## Algerian Forest Fire Prediction This project implements a machine learning pipeline to predict forest fire occurrence in two Algerian regions using meteorological and Fire Weather Index (FWI) data. It demonstrates the full data science workflow—from exploration and feature engineering to model training, evaluation, and deployment as a web application. *** ## Table of Contents - Overview - Dataset - Features - Modeling Approach - Results - Key Insights - Tech Stack - Applications *** ## Overview Forest fires are a major threat to the environment, ecology, and human livelihoods. Leveraging meteorological and environmental factors, this project applies supervised machine learning—specifically Ridge Regression—to forecast the likelihood of fire events. The end product is a web application where users can input relevant weather data to obtain predictions. *** ## Dataset The data set comprises 244 daily observations (June–September 2012) from Bejaia and Sidi Bel-Abbes regions in Algeria. - **Source:** UCI Algerian Forest Fires Dataset - **Instances:** 244 (122 per region) - **Target Classes:** `Fire`, `Not Fire` - **Attributes:** 11 features + 1 target (see below) *** ## Features | Feature | Description | |-----------------|----------------------------------------------| | Temperature | Maximum daily temperature (°C) | | RH | Relative Humidity (%) | | Ws | Wind Speed (km/h) | | Rain | Rainfall (mm) | | FFMC | Fine Fuel Moisture Code | | DMC | Duff Moisture Code | | DC | Drought Code | | ISI | Initial Spread Index | | BUI | Buildup Index | | FWI | Fire Weather Index …