tejareddyqs/forest-fire-prediction

*Machine Learning project to predict the Fire Weather Index (FWI) using the Algerian Forest Fires dataset with Ridge Regression, Flask, and Scikit-learn.** This description and README are suitable for showcasing the project on your GitHub profile and to recruiters. # forest-fire-prediction *Machine Learning project to predict the Fire Weather Index (FWI) using the Algerian Forest Fires dataset with Ridge Regression, Flask, and Scikit-learn.** This description and README are suitable for showcasing the project on your GitHub profile and to recruiters. # 🔥 Algerian Forest Fire FWI Prediction A Machine Learning web application that predicts the **Fire Weather Index (FWI)** using the Algerian Forest Fires dataset. The project demonstrates the complete ML lifecycle, from data preprocessing and model training to deployment using Flask. ## 🚀 Features - Data Cleaning and Preprocessing - Exploratory Data Analysis (EDA) - Feature Engineering - Correlation-based Feature Selection - Feature Scaling using StandardScaler - Ridge Regression Model - Model Serialization using Pickle - Flask Web Application - Real-time FWI Prediction through a Web Interface ## 📂 Project Structure project/ │ ├── models/ │ ├── ridge.pkl │ └── scaler.pkl │ ├── templates/ │ ├── index.html │ └── home.html │ ├── app.py ├── requirements.txt └── README.md ## 🛠 Technologies Used - Python - NumPy - Pandas - Matplotlib - Seaborn - Scikit-learn - Flask - HTML ## Machine Learning Pipeline 1. Data Cleaning 2. Exploratory Data Analysis 3. Feature Selection 4. Train-Test Split 5. Feature Scaling 6. Ridge Regression Training 7. Hyperparameter Tuning 8. Model Evaluation 9. Model Serialization (Pickle) 10. Flask Deployment ## Model Used - Ridge Regression ## Evaluation Metrics - Mean Absolute Error (MAE) - Mean Squared Error (MSE) - Root Mean Squared Error (RMSE) - R² Score ## How to Run Clone the repository ```bash git clone github.com