FarhadAliShaikh/Algerian-Forest-Fires-Prediction

# Algerian Forest Fires Analysis and Prediction This project aims to analyze and predict forest fires in Algeria using various machine learning techniques. Forest fires are a significant environmental concern, and accurate prediction can help in timely intervention and resource allocation. The project includes **data cleaning**, **exploratory data analysis (EDA)**, **feature engineering**, **model training**, and **deployment using Flask**. ## Project Structure 1. `dataset/`: Contains the dataset used in this project. 2. `models/`: Contains the trained models. 3. `notebooks/`: Contains the Jupyter notebooks used in this project. 4. `templates/`: Contains the HTML templates for the Flask application. 5. `application.py`: Contains the Flask application for the model deployment. 6. `requirements.txt`: Contains the required Python packages for this project. ## Notebooks - `notebooks/EDA_And_FE_Algerian_Forest_Fires.ipynb`: This notebook contains the exploratory data analysis and feature engineering steps. - `notebooks/Model_Training.ipynb`: This notebook contains the model training and evaluation steps. ## Dataset The dataset used in this project is located in the `dataset/` directory: - `Algerian_forest_fires_cleaned_dataset.csv` - `Algerian_forest_fires_dataset_UPDATE.csv` You can find the original dataset on Kaggle at the following link:Algerian Forest Fires Dataset ## Models The trained models are saved in the `models/` directory: - `ridge.pkl`: Trained Ridge regression model. - `scaler.pkl`: Scaler used for data normalization. ## Deployment Using Flask The trained model is deployed as a web application using Flask. Users can interact with the model through a web interface, input relevant data, and receive predictions about forest fire likelihood. To run the Flask app, use the following command: ```sh python application.py ``` ## Requirements The required Python packages are listed in the `requirements.txt` file. You can install them using the followi …