parjun585/forestfire_prediction

Machine learning model with linear regression (Ridge regression) and standardscaler using sklearn module using the Algerian forest fire dataset with flask to open a webpage to add new data for prediction. ## 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 attribues and 1 output attribue (class) The 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes. Dataset was taken from: archive.ics.uci.edu ## 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 ### The Notebook folder contain two notebooks 1. 2.0-EDA And FE Algerian Forest Fires (Dataset was preprocessed. EDA and FE was done) 2. 3.0-Model Training (Once dataset was processed forwarded with linear regression model and dumped into pickle files) ``` ####To access your flask application open new tab in and paste the url: ``` ``` (127.0.0.1) ``` ``` ####To access the predictdata page go ``` ``` (127.0.0.1) ```