sanjai-b-2006/regression-model-on-algerian-forest-fire-dataset

This project predicts forest fire intensity using multiple linear regression models. Key environmental factors like temperature, humidity, wind speed, and rainfall are analyzed to build the predictive model. # Regression-model-on-algerian-forest-fire-dataset This project predicts forest fire intensity using multiple linear regression models. Key environmental factors like temperature, humidity, wind speed, and rainfall are analyzed to build the predictive model. There is a application which is done using basics of flask # Attribute Information: 1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) Weather data observations 2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42 3. RH : Relative Humidity in %: 21 to 90 4. Ws :Wind speed in km/h: 6 to 29 5. Rain: total day in mm: 0 to 16.8 FWI Components 6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5 7. Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9 8. Drought Code (DC) index from the FWI system: 7 to 220.4 9. Initial Spread Index (ISI) index from the FWI system: 0 to 18.5 10. Buildup Index (BUI) index from the FWI system: 1.1 to 68 11. Fire Weather Index (FWI) Index: 0 to 31.1 12. Classes: two classes, namely Fire and not Fire