shreyas927/Algerian-forest-fire-prediction-model
Domain:
environment and energyRecord type:
model
Creator:
shr
Host:
# 🔥 Algerian Forest Fires Prediction Model
A machine learning application that predicts forest fire probability in Algeria using Ridge Regression. This project includes a web interface built with Flask for easy predictions.
## 📋 Table of Contents
- Features
- Project Structure
- Dataset
- Installation
- Usage
- Model Details
- Technologies
- Contributing
---
## ✨ Features
- **Machine Learning Model**: Ridge Regression for accurate fire probability prediction
- **Web Interface**: User-friendly Flask web application with HTML forms
- **Data Preprocessing**: StandardScaler for feature normalization
- **Model Serialization**: Pre-trained models saved as pickle files for quick deployment
- **RESTful API**: POST endpoint for predictions
- **Responsive UI**: HTML templates for intuitive user interaction
---
## 📁 Project Structure
```
s-by-s-ml-project/
├── application.py # Main Flask application
├── model_training.ipynb # Model training notebook
├── EDA_FE.ipynb # Exploratory Data Analysis & Feature Engineering
├── Algerian_forest_fires_cleaned_dataset.csv
├── Algerian_forest_fires_dataset_UPDATE.csv
├── ridge.pkl # Trained Ridge Regression model
├── scaler.pkl # StandardScaler for feature normalization
├── templates/
│ ├── index.html # Home page
│ └── home.html # Prediction form & results page
└── README.md
```
---
## 📊 Dataset
The project uses the Algerian Forest Fires dataset containing:
- **Features**: Temperature, Relative Humidity (RH), Wind Speed (Ws), Rainfall, FFMC, DMC, ISI, Classes, Region
- **Target**: Forest fire probability/severity
- **Preprocessing**: Data cleaning, feature scaling, and outlier handling
- **Files**:
- `Algerian_forest_fires_cleaned_dataset.csv` - Cleaned version
- `Algerian_forest_fires_dataset_UPDATE.csv` - Updated raw data
---
## 🚀 Installation …