saicharan-devoju/Algerian_Forest_Fires

# Algerian Forest Fires - Fire Weather Index Prediction ## Project Overview This project focuses on predicting the **Fire Weather Index (FWI)** using the Algerian Forest Fires dataset and Machine Learning techniques. The Fire Weather Index is a numerical indicator used to estimate wildfire danger based on weather and environmental conditions. The model learns patterns from historical fire and weather data to predict FWI values for new observations. --- ## Dataset **Dataset:** Algerian Forest Fires Dataset ### Features - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - DC - ISI - BUI ### Target Variable - FWI (Fire Weather Index) --- ## Technologies Used - Python - Pandas - NumPy - Matplotlib - Seaborn - Scikit-Learn --- ## Project Workflow 1. Data Collection 2. Data Cleaning 3. Exploratory Data Analysis (EDA) 4. Feature Selection 5. Handling Multicollinearity 6. Train-Test Split 7. Model Training 8. Model Evaluation 9. Prediction --- ## Machine Learning Model - Linear Regression ### Evaluation Metrics - Mean Absolute Error (MAE) - Mean Squared Error (MSE) - Root Mean Squared Error (RMSE) - R² Score --- ## Repository GitHub Repository: ```text github.com ``` Clone the repository: ```bash git clone github.com ``` --- ## Installation Install the required packages: ```bash pip install -r requirements.txt ``` --- ## Usage Run the project: ```bash python app.py ``` or open the Jupyter Notebook: ```bash jupyter notebook ``` --- ## Project Goal To build a machine learning model capable of predicting the Fire Weather Index (FWI) from meteorological and fire-related attributes, helping assess potential wildfire risk. --- ## Author **Sai Charan Devoju** GitHub: github.com