1rishu0/Forest-Fires

In this project, I predict forest fires in Algeria using meteorological and environmental data. I apply Ridge and Lasso regression for model training and feature selection. The Flask web application enables users to input data and receive predictions, designed with HTML and CSS. # Algerian Forest Fire Prediction This project predicts forest fire occurrences in Algeria using meteorological and environmental data, as well as Fire Weather Index (FWI) components. The dataset contains observations from June to September 2012. The project leverages Ridge and Lasso regression models to predict fire risks based on weather conditions and provides a web-based interface using Flask, HTML, and CSS. ## Dataset Description The dataset contains the following columns: - **Day:** Day of the observation (DD). - **Month:** Month of the observation (June to September). - **Year:** Year of the observation (2012). - **Temperature (Temp):** Temperature at noon in Celsius degrees (22 to 42). - **Relative Humidity (RH):** Relative humidity in % (21 to 90). - **Wind Speed (Ws):** Wind speed in km/h (6 to 29). - **Rain:** Total daily rainfall in mm (0 to 16.8). ## FWI Components The dataset includes Fire Weather Index (FWI) system components, which help assess fire risks based on weather and fuel conditions. - **Fine Fuel Moisture Code (FFMC):** Measures surface litter moisture content (28.6 to 92.5). - **Duff Moisture Code (DMC):** Measures moisture content in the duff layer (1.1 to 65.9). - **Drought Code (DC):** Indicates long-term drought conditions (7 to 220.4). - **Initial Spread Index (ISI):** Measures potential fire spread rate (0 to 18.5). - **Buildup Index (BUI):** Combines DMC and DC to indicate fire intensity potential (1.1 to 68). - **Fire Weather Index (FWI):** General fire intensity index (0 to 31.1). - **Classes:** Binary target variable indicating fire occurrence (Fire or Not Fire). ## Regions The dataset covers two regions within Algeria, allowing for region-based analysis of fire risks. # Project Workflow ## 1. Data Preprocessing - **Data Cleaning:** Handle missing values and outliers. - **Feature Scaling:** Normalize data for consistent input to the machine learning models. - **Feature Selection:** Use Lasso regression to identify the most …