vsuraj25/Forest-Fire-Prediction

This project aims to predict forest fire using best machine learning models. Regression and Classification both Supervised learning is portrayed in this project. The dataset used in this project is taken from UCI Machine Learning Repository - Algerian Forest Fire Dataset. Forest Fire Prediction Machine Learning Project Explore my Repositories. » Introduction · Data Information · Contact ## **About The Project** * This project aims to predict forest fire using best machine learning models. Regression and Classification both streams Supervised learning is portrayed in this project. The dataset used in this project is taken from UCI Machine Learning Repository - Algerian Forest Fire Dataset. Both Single input and Bulk input prediction can be exceuted. ## **Deployed app** LINK TO HEROKU APP ## **Introduction** * In this project, i have worked on solving Regression and Classification both with a usecase of predicting the Forest Fire Weather index and Forest Fire possibility.The dataset used in this project is taken from UCI Machine Learning Repository - Algerian Forest Fire Dataset. MongoDB database is used to store the dataset. EDA and Model Building both could be found in the repo. Logging is maintained accordingly. ## **Dataset Information** * Download the original dataset here : Algerian Forest Fire Dataset * 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. * The period of data is 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 …