https://github.com/singhxtushar/forest-fire-prediction-ridgeregression
This model predicts the forest fire by using the Ridge-Regression algorithm.
https://github.com/singhxtushar/forest-fire-prediction-ridgeregression
algerian-forest-fire flask-api pickling ridge-regression standardscaler
Last synced: 4 months ago
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This model predicts the forest fire by using the Ridge-Regression algorithm.
- Host: GitHub
- URL: https://github.com/singhxtushar/forest-fire-prediction-ridgeregression
- Owner: SINGHxTUSHAR
- License: mit
- Created: 2023-09-29T09:32:59.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-30T05:03:09.000Z (about 2 years ago)
- Last Synced: 2025-02-19T04:58:20.804Z (8 months ago)
- Topics: algerian-forest-fire, flask-api, pickling, ridge-regression, standardscaler
- Language: Jupyter Notebook
- Homepage:
- Size: 473 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# This project is made to predict the Algerian Forest Fires.
## 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.
## 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
## FLOW OF THE PROJECT :
- Data collection for the Algerian Forest Fires.
- EDA on the Algerian Forest Fires Dataset.
- FE on the Algerian Forest Fires Dataset.
- Model training by using the Ridge Regression with an accuracy of 98.4%.
- Web Application Design by using the FLASK.
- Deployment of the Model on the AWS with Elastic Beanstalk and CodePipeline services.