nirbhay36/ML---Algerian-Forest-Fire

Forest Fire Prediction - Nirbhay Ahir A brief description of what this project is all about. Forest Fire Prediction is a Supervised Machine learning problem statements. Using Regression and Classification Algorithm I detected the future fires with temperture as dependent variable based on certain Weather report. Library Used in this Project Data Pre-Processing Numpy, Pandas, Matplotlib, Seaborn Model Building Sklearn, statsmodels 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 attribues and 1 output attribue (class) The 244 instances have been classified into fire (138 classes) and not fire (106 classes) classes. 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 185 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 Steps 1.Data Collection 2.Data Pre-Processing 3.Exploratory Data Analysis 4.Feature Engineering 5.Feature Selection 6.Model Building Model Used: 1.Linear regression 2.Lasso Regression 3.Ridge Regression Model Selection For Regression r2_score metrics is used to select best model.