SaiReddy43/algerian-fire-weather-index-prediction

# 🔥 Forest Fire Weather Index (FWI) Prediction System > A Full-Stack Machine Learning application that predicts the **Forest Fire Weather Index (FWI)** using meteorological data through a trained Ridge Regression model. # 📌 Project Overview The **Forest Fire Weather Index (FWI) Prediction System** is a Full-Stack Machine Learning application that predicts wildfire severity using meteorological conditions. The project covers the complete machine learning workflow: - 📊 Exploratory Data Analysis (EDA) - 🛠️ Feature Engineering - 🤖 Machine Learning Model Development - 🌐 Flask REST API - 💻 React.js Frontend - 🔄 Full-Stack Integration Users provide weather information through an interactive React dashboard. The Flask backend preprocesses the input using **StandardScaler**, performs inference with a trained **Ridge Regression** model, and returns the predicted **Fire Weather Index (FWI)** in real time. --- # ✨ Features - 📊 Performed Exploratory Data Analysis (EDA) - 🛠️ Applied Feature Engineering and Data Preprocessing - 🤖 Trained a Ridge Regression Machine Learning model - ⚡ Real-time Forest Fire Weather Index Prediction - 🌐 Developed REST APIs using Flask - 💻 Built an interactive React.js dashboard - 🔄 React–Flask Integration using JSON APIs - 📱 Responsive User Interface - ✅ Robust Error Handling and Input Validation --- # 📈 Machine Learning Pipeline - Data Cleaning - Exploratory Data Analysis (EDA) - Feature Engineering - Data Preprocessing - Feature Scaling using StandardScaler - Ridge Regression Model Training - Model Serialization using Pickle - REST API Development using Flask - React Frontend Integration --- # 🏗️ System Architecture --- # 📸 Application Screenshots ## Home Page --- ## Prediction Result --- # ⚙️ Technology Stack ## Frontend - React.js - JavaScript - HTML5 - CSS3 - Fetch API ## Backend - Python - Flask - Flask-CORS - REST API ## Machine Learning - Scikit-learn - Ridge Regression - StandardScaler - NumPy - Pandas - Pick …