divyanshu666/Algerian-Forest-Fire-FWI-Prediction

# 🌲 Algerian Forest Fire Dataset – FWI Prediction This project predicts the **Fire Weather Index (FWI)** using the **Algerian Forest Fire dataset**. It involves **data cleaning, exploratory data analysis (EDA)**, feature selection, and training multiple machine learning models to find the best predictor. The best-performing model (**Ridge Regression**) achieved an **R² score of 98%**, and was deployed using **Flask** on **AWS Elastic Beanstalk** with a CI/CD pipeline via **GitHub Oauth App**. --- ## 📌 Project Workflow ### 1️⃣ Data Preprocessing - **Data Cleaning & EDA**: Handled missing values, outliers, and formatted dataset. - **Multicollinearity Check**: Used **correlation matrix** to detect and remove highly correlated features. - **Feature Selection**: Selected the most impactful variables for prediction. ### 2️⃣ Model Building - Trained models: - Linear Regression - Lasso Regressor - ElasticNet Regressor - Ridge Regression - **Best Model**: Ridge Regression with R² = **98%** - Saved the trained **scaler** and **Ridge Regression model** as `.pkl` files. ### 3️⃣ Deployment - Built a **Flask API** to serve the model. - Deployed on **AWS Elastic Beanstalk**. - Set up **AWS CodePipeline** for automated deployment via **GitHub**. --- ## 🛠 Tech Stack **Programming Language:** - Python **Libraries Used:** - `Flask` - `NumPy` - `pandas` - `scikit-learn` - `seaborn` - `matplotlib` **Deployment:** - AWS Elastic Beanstalk - AWS CodePipeline - GitHub Oauth App ---