# π₯ Forest Fire Detection System
A comprehensive machine learning-based web application for predicting forest fire risks in Algeria using environmental factors such as temperature, humidity, wind conditions, and rainfall.
## π Features
### **ML-Powered Predictions**
- **Ridge Regression Model**: Advanced machine learning algorithm for accurate fire risk assessment
- **Real-time Analysis**: Instant predictions based on environmental parameters
- **Risk Level Classification**: LOW, MEDIUM, HIGH, and EXTREME risk categories
### **Interactive Dashboard**
- **Prediction History**: Track all your previous predictions with detailed analytics
- **Data Visualization**: Beautiful charts showing temperature trends, risk patterns, and distribution
- **Summary Statistics**: Total predictions, average risk, highest/lowest risk scores
- **Responsive Design**: Works seamlessly on desktop, tablet, and mobile devices
### **User Experience**
- **Smooth Navigation**: GSAP-powered smooth scrolling between dashboard sections
- **Active Page Indicators**: Clear navigation showing current page
- **Custom Scrollbars**: Beautiful, dark-themed scrollbars for better visibility
- **Hover Effects**: Interactive elements with smooth transitions and animations
### **Data Management**
- **Local Storage**: Secure storage of user prediction history
- **Data Persistence**: Predictions saved locally for privacy and convenience
- **Export Ready**: Structured data format for future analysis
## ποΈ Project Structure
```
ML Project 1/
βββ Backend/ # Python Flask Backend
β βββ application.py # Main Flask application
β βββ models/ # Trained ML models
β β βββ ridgereg.pkl # Ridge Regression model
β β βββ scaler.pkl # Data scaler
β βββ notebooks/ # Jupyter notebooks
β β βββ 1.EDA&FeatureEngi.ipynb # Exploratory Data Analysis
β β βββ 2.ModelTraining.ipynb # Model Training
β β β¦