pk7070-create/Algerian-Forest-Fire-FWI-Prediction

Machine Learning web application for predicting Fire Weather Index (FWI) using the Algerian Forest Fire dataset. # Algerian Forest Fire FWI Prediction ## Project Overview This project focuses on predicting the Fire Weather Index (FWI) using the Algerian Forest Fire dataset. The objective of this project is to analyze environmental and weather-related factors and build a machine learning model capable of predicting the Fire Weather Index. ## Project Workflow The project was completed following a complete machine learning pipeline: * Data Collection and Understanding * Exploratory Data Analysis (EDA) * Data Cleaning and Preprocessing * Feature Engineering * Feature Scaling using StandardScaler * Train-Test Split * Model Training and Evaluation ## Machine Learning Models Used During model development, the following regression algorithms were implemented and evaluated: * Linear Regression * Ridge Regression * Lasso Regression To improve model performance and select the optimal regularization parameter, cross-validation techniques were applied using: * LassoCV (Cross Validation) ## Model Evaluation The models were evaluated using standard regression metrics such as: * Mean Absolute Error (MAE) * Mean Squared Error (MSE) * R² Score Based on the evaluation results, the best-performing model was selected and saved using Pickle for deployment. ## Web Application Development After model training, a web application was developed using Flask. The application allows users to enter weather-related parameters and obtain the predicted Fire Weather Index in real time. ## Technologies Used * Python * Pandas * NumPy * Matplotlib * Seaborn * Scikit-Learn * Flask * HTML & CSS * Pickle ## Project Outcome Successfully built an end-to-end Machine Learning application that performs Fire Weather Index prediction and provides predictions through a user-friendly Flask web interface. ## AUTHOR PRINCE KUMAR AI/ML ENTHUSIAST