Pratham2518/WILDFIRE_Algerian_Forest_ML_Project

A machine learning project to predict wildfire occurrence in Algerian forests using meteorological data. Includes data preprocessing, model training, evaluation, and a deployed web application for real-time predictions. Forest Fire Prediction Web Application ## Overview The **Forest Fire Prediction Web Application** is a machine learning-powered tool designed to **predict forest fire risks** based on environmental and forest-related parameters. Built with **Flask**, it provides a simple and interactive web interface for real-time predictions. --- ## Uses - **Forest Risk Prediction:** Quickly identify areas at risk of wildfires. - **Decision Support:** Helps forest management and authorities plan preventive actions. - **Educational Tool:** Demonstrates practical use of machine learning for environmental analysis. - **Interactive Predictions:** Users can input custom environmental data and get instant predictions. --- ## Key Features - **User-Friendly Web Interface:** Simple input forms for entering environmental parameters. - **Machine Learning Model:** Pre-trained **Ridge Regression** model provides accurate predictions. - **Data Preprocessing:** Automatically scales input data using a pre-trained **StandardScaler**. - **Real-Time Predictions:** Predictions are generated dynamically through the `/predictdata` endpoint.