VedantUplap/Algerian_Forest_Fire_ML_Project

ML project for predicting forest fire risk # Algerian Forest Fire Prediction Using Machine Learning ## Project Overview This project is an end-to-end Machine Learning web application developed using Flask. The model predicts the **Fire Weather Index (FWI)** based on environmental and weather-related input features from the Algerian Forest Fires dataset. The application takes user input through a web interface and predicts the FWI value using a trained Ridge Regression model. --- ## Problem Statement Forest fires are one of the major environmental threats affecting ecosystems and human life. The goal of this project is to predict the **Fire Weather Index (FWI)** using meteorological data so that fire risk can be analyzed efficiently. --- ## Technologies Used - Python - Pandas - NumPy - Scikit-learn - Flask - HTML --- ## Machine Learning Workflow The project follows a complete machine learning pipeline: 1. Data Collection 2. Data Cleaning 3. Exploratory Data Analysis (EDA) 4. Feature Selection 5. Data Standardization 6. Model Training 7. Model Evaluation 8. Model Deployment using Flask --- ## Models Compared The following regression algorithms were trained and evaluated: - Linear Regression - Ridge Regression - Lasso Regression - Elastic Net Regression The final model selected was: ## Ridge Regression because it provided the best performance based on the R² Score. --- ## Input Features The model predicts FWI using the following input parameters: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - DC - ISI - BUI - Classes - Region --- ## Project Structure ```text Algerian-Forest-Fire-Prediction/ │ ├── application.py ├── README.md ├── requirements.txt │ ├── models/ │ ├── ridge.pkl │ └── scaler.pkl │ ├── templates/ │ └── home.html │ └── notebooks/ └── model_training.ipynb ``` --- ## Flask Application The Flask web application allows users to: …