Duttarishabh/Forest_fire--Flask

Flask app that uses the ridge model to predict the FWI in the forest of the Algeria. **About the Project** ** Algerian Forest Fires Dataset** Data Set Information: The dataset includes 244 instances that regroup a data of two regions of Algeria, namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria. 122 instances for each region. The period from June 2012 to September 2012. The dataset includes 11 attributes and 1 output attribute (class) The 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes. Attribute Information: Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42 RH : Relative Humidity in %: 21 to 90 Ws speed in km/h: 6 to 29 Rain: total day in mm: 0 to 16.8 FWI Components Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5 Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9 Drought Code (DC) index from the FWI system: 7 to 220.4 Initial Spread Index (ISI) index from the FWI system: 0 to 18.5 Buildup Index (BUI) index from the FWI system: 1.1 to 68 Fire Weather Index (FWI) Index: 0 to 31.1 **Machine Learning Model Used** **Linear Regression** Mean Absolute Error= 54.68 R2 score: 98.47 **Lasso Regression** Mean Absolute Error= 0.61 R2 score: 94.92 **Ridge Resgression** Mean Absolute Error= 56 R2 score: 98.82 **ElasticNet Regression** Mean Absolute Error=65.75 R2 score=98.14