Srishti1110/Algerian-Forest-Fires

# Algerian Forest Fire Prediction This project focuses on predicting the Fire Weather Index (FWI) using various regression models. The dataset used is specific to Algerian forest fires, with historical weather data and fire occurrences. ## Overview The goal of this project is to create an effective prediction model for FWI using different regression techniques. This involves data preprocessing, feature selection, model training, evaluation, and fine-tuning. Here's the updated README.md section incorporating the information about the HTML web page used for deployment: --- ## Dataset Information The dataset used in this project consists of 244 instances, gathered from two regions in Algeria: Bejaia, located in the northeast, and Sidi Bel-abbes, located in the northwest. The data covers the period from June 2012 to September 2012, with 122 instances for each region. ### Features: 1. **Date**: Day, month (June to September), and year (2012). 2. **Temperature**: Noon temperature (max temperature) in Celsius degrees, ranging from 22°C to 42°C. 3. **Relative Humidity (RH)**: Ranges from 21% to 90%. 4. **Wind Speed (Ws)**: Ranges from 6 km/h to 29 km/h. 5. **Rain**: Total daily rainfall in mm, ranging from 0 to 16.8 mm. 6. **Fine Fuel Moisture Code (FFMC)**: Index from the FWI system, ranging from 28.6 to 92.5. 7. **Duff Moisture Code (DMC)**: Index from the FWI system, ranging from 1.1 to 65.9. 8. **Drought Code (DC)**: Index from the FWI system, ranging from 7 to 220.4. 9. **Initial Spread Index (ISI)**: Index from the FWI system, ranging from 0 to 18.5. 10. **Buildup Index (BUI)**: Index from the FWI system, ranging from 1.1 to 68. 11. **Fire Weather Index (FWI)**: The primary target variable, ranging from 0 to 31.1. 12. **Classes**: Binary classification with two classes—'fire' and 'not fire'. ### Data Preprocessing: - **Region Assignment**: The dataset is split into two subsets: "Bejaia Region Dataset" and "Sidi-Bel Abbes Region Dataset". Rows until the 122n …