Physics-informed fire occurrence prediction using structured fire indices (ISI, FFMC, DMC, DC, BUI, FWI), and latent clustering. Implements an interpretable neural model fulfilling ISI’s predictive role. Stage 1 of a modular fire propagation modeling framework grounded in physical science. Resulted in a perfect 100% accuracy

Explanatory Data Analysis for the Algerian forest fire dataset.

Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.

In this project, we aim to predict the temperature using Algerian forest fire dataset

This model predicts the forest fire by using the Ridge-Regression algorithm.

This project uses the Algerian Forest Fires dataset from the UCI Machine Learning Repository to predict fire risk using linear regression. The dataset includes temperature, humidity, wind, rain, and Fire Weather Index (FWI) components recorded across two Algerian regions. The project includes EDA, feature engineering, model training, & evaluations.