whonancysoni/Linear-Regression-on-Algerian-Forest-Fire-Temperature-Prediction.

Practical Implementation of Linear Regression on Algerian Forest Fire Dataset. # Introduction Algerian Forest Fires * The dataset I used comes from UCI on Algerian Forest Fires. Forest fire observations and data from two regions of Algeria are included in this dataset: Bejaia and Sidi Bel-Abbes. * This dataset spans the period from June 2012 to September 2012. Our project examined the possibility of using Machine Learning algorithms to predict forest fires in these regions based on certain weather features. 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 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 # Steps 1. Data Collection 2. Data Pre-Processing 3. Exploratory Data Analysis 4. Feature Engineering 5. Feature Selection 6. Model Building # Model Building Regression * For regression analysis FWI(Fire weather Index) considered as dependent feature because it highly correlated with classes variable with more than 90% …