harshp23/test_forest_fires

Algerian Forest Fire Prediction # 🌲 Algerian Forest Fire Prediction using Ridge Regression This project predicts the **Fire Weather Index (FWI)** for the **Algerian Forest Fire Dataset** using machine learning. It includes a **Flask web application** that allows users to input weather parameters (Temperature, RH, Wind Speed, Rain, etc.) and get a **predicted FWI** instantly. --- ## 📘 Project Overview Forest fires are among the most devastating natural disasters, and early prediction can help prevent massive environmental losses. This project focuses on predicting **Fire Weather Index (FWI)** using meteorological parameters from the Algerian Forest Fire dataset. After testing multiple regression algorithms, **Ridge Regression** was chosen because it provided the **best performance and least overfitting** compared to other models. --- ## 🚀 Features - End-to-end **Machine Learning pipeline** - **Data cleaning and EDA** using Jupyter notebooks - Model training with **Ridge Regression** - Scaled data using **StandardScaler** - Flask-based **web app** for user input and prediction - **Interactive form** for entering temperature, humidity, wind speed, rainfall, etc. - Instant FWI prediction on the same page (no reload) --- ## 🧠 Tech Stack | Category | Tools / Libraries | |-----------|-------------------| | Programming | Python | | Data Handling | Pandas, NumPy | | Model | Ridge Regression (scikit-learn) | | Visualization | Matplotlib, Seaborn | | Web Framework | Flask | | Frontend | HTML, CSS, JavaScript | | Deployment | Localhost / GitHub integration ready | --- ## 📊 Dataset **Algerian Forest Fire Dataset** Contains meteorological variables: - Temperature (°C) - Relative Humidity (%) - Wind Speed (Km/h) - Rain (mm) - FFMC, DMC, DC, ISI, BUI - Region (Bejaia, Sidi Bel-abbes) - Class (Fire / Not Fire) Dataset Source: UCI Machine Learning Repository --- ## 📈 Model Performance | Category | MAE | R2 Score| |----------|------|--------| |Linear Regression | 0.54 |0.98| |Lasso Regression | 1.13 …