Algerian Forest Fire Prediction Website

FirePrediction is a machine learning project designed to forecast the occurrence of forest fires using advanced modeling techniques and data-driven insights. Leveraging the Algeria Forest Fire Dataset sourced from Kaggle, this project aims to develop predictive models that can effectively anticipate fire incidents based on historical data and environmental variables.

Algeria Forest Fire Dataset : https://www.kaggle.com/datasets/nitinchoudhary012/algerian-forest-fires-dataset

Methodology

The project employs a multifaceted approach to model development, beginning with exploratory data analysis (EDA) and preprocessing tasks in Jupyter Notebooks. Data preprocessing involves handling missing values, feature scaling, feature selection and possibly feature engineering to extract more meaningful insights from the dataset.

Following preprocessing, multiple machine learning algorithms are explored using scikit-learn (SKLearn) within Jupyter Notebooks. Various models such as Ridge Regression, RidgeCV, Lasso, LassoCV, ElasticNet, ElasticNetCV are trained and evaluated to identify the most effective model for fire prediction.

Model Accuracy based on r2-Score : 98.4%

• This Linear relation between the Ground Truth and Predicted values of test data shows model is performing well.

Explore Notebooks:

Tech Stack

• Flask
• Jupyter Notebook
• SKLearn
• Python
• HTML
• CSS

Table of Contents

• Installation
• Usage
• Project Structure
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/JSM2512/FirePrediction.git

2. Navigate to the project directory:

   cd FirePrediction

3. Create and activate a virtual environment:

   python -m venv venv1
   source venv1/bin/activate  # On Windows use `venv1\Scripts\activate`

4. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the application:

   python application.py

2. Access the web interface at http://127.0.0.1:5000.

Project Structure

FirePrediction/
├── .ebextensions/
├── models/
│   ├── model1.pkl
│   ├── model2.pkl
│   └── ...
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   └── ...
├── templates/
│   ├── index.html
│   ├── result.html
│   └── ...
├── venv/
├── application.py
├── requirements.txt
└── README.md

• .ebextensions/: Configuration files for AWS Elastic Beanstalk.
• models/: Pre-trained machine learning models.
• notebooks/: Jupyter notebooks for data preprocessing and model training.
• templates/: HTML templates for the web interface.
• application.py: The main application file.
• requirements.txt: List of dependencies required for the project.

Contributions

Contributions are welcome! Please open an issue or submit a pull request for any changes or enhancements.