sprakashx15/Algerian-forest-fire

The primary goal of your project is to develop a machine learning model that accurately predicts the Fire Weather Index (FWI) for Algerian forest fires. The FWI is a key metric used to assess the potential for wildfire occurrence, considering various weather-related factors like temperature, humidity, wind speed, and precipitation # Algerian Forest Fire Prediction: FWI using Linear Regression ## Overview This project aims to predict the **Fire Weather Index (FWI)** for **Algerian forest fires** using **linear regression** and its variants. The Fire Weather Index is a key metric used to assess the risk of wildfires based on weather conditions. By accurately predicting the FWI, this project helps improve wildfire prevention, early detection, and resource allocation. ### **Key Technologies Used:** - Python - **Libraries:** Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn - **Machine Learning Models:** Linear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression --- ## Table of Contents 1. Project Description 2. Installation 3. Usage 4. Data 5. Models 6. Evaluation Metrics 7. Results 8. Contributing 9. License --- ## Project Description The goal of this project is to create a machine learning model that predicts the **Fire Weather Index (FWI)** for forest fire risk in Algeria based on meteorological data. The model uses historical weather data (temperature, wind speed, humidity, and precipitation) along with the corresponding FWI to train and predict future fire risks. **Approach:** - The dataset is cleaned and preprocessed to handle missing values and normalize features. - Multiple linear regression techniques, including **Ridge**, **Lasso**, and **ElasticNet**, are implemented to predict the FWI based on the features. - The model is evaluated using metrics such as **Mean Squared Error (MSE)**, **Mean Absolute Error (MAE)**, and **R² (Coefficient of Determination)**. --- ## Installation To set up and run this project, follow these steps: 1. Clone this repository to your local machine: ```bash git clone github.com ``` 2. Navigate to the project directory: ```bash cd fwi-prediction ``` 3. Install the required dependencies using **pip**: ```bash pip install -r requirements.txt ``` --- ## Usage To train the model and make …