Ayhampt/End-To-End-ML-Project-Implementation

A Machine Learning web application that predicts the Fire Weather Index (FWI) using environmental and weather parameters from the Algerian Forest Fire dataset. # Algerian Forest Fire Prediction 🔥 A Machine Learning web application that predicts the **Fire Weather Index (FWI)** using environmental and weather parameters from the Algerian Forest Fire dataset. --- ## 🚀 Project Overview This project uses a trained **Ridge Regression** machine learning model to predict forest fire risk based on environmental and weather conditions. The application is built using: - Python - Flask - Scikit-Learn - Pandas - NumPy - HTML/CSS --- ## 📷 Features - Modern responsive UI - Real-time fire risk prediction - ML model integration with Flask - Feature scaling using StandardScaler - User-friendly web interface --- ## 📊 Input Features The model predicts the Fire Weather Index (FWI) using: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - ISI - Classes - Region --- ## 🧠 Machine Learning Workflow 1. Data Collection 2. Data Cleaning 3. Exploratory Data Analysis (EDA) 4. Feature Engineering 5. Model Training 6. Model Evaluation 7. Flask Deployment --- ## 🛠️ Tech Stack | Technology | Usage | |---|---| | Python | Backend | | Flask | Web Framework | | Scikit-Learn | Machine Learning | | Pandas | Data Processing | | NumPy | Numerical Operations | | HTML/CSS | Frontend | --- ## 📂 Project Structure ```bash Algerian-Forest-Fire/ │ ├── models/ │ ├── ridge.pkl │ └── scalar.pkl │ ├── templates/ │ ├── index.html │ └── home.html │ ├── application.py ├── requirements.txt └── README.md