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https://github.com/gaizkiaadeline/forest-fire-prediction-using-data-mining-techniques
This project focuses on predicting forest fire areas using the Fire Weather Index (FWI) dataset. Key methods include the use of Log Transform to normalize the burned area data, improving the performance of linear regression models. Data analysis and visualizations like histograms and QQ plots are utilized to explore patterns and model accuracy.
https://github.com/gaizkiaadeline/forest-fire-prediction-using-data-mining-techniques
Last synced: 1 day ago
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This project focuses on predicting forest fire areas using the Fire Weather Index (FWI) dataset. Key methods include the use of Log Transform to normalize the burned area data, improving the performance of linear regression models. Data analysis and visualizations like histograms and QQ plots are utilized to explore patterns and model accuracy.
- Host: GitHub
- URL: https://github.com/gaizkiaadeline/forest-fire-prediction-using-data-mining-techniques
- Owner: gaizkiaadeline
- Created: 2024-10-13T02:49:14.000Z (26 days ago)
- Default Branch: main
- Last Pushed: 2024-10-17T01:36:39.000Z (22 days ago)
- Last Synced: 2024-10-19T03:15:00.334Z (20 days ago)
- Language: R
- Homepage:
- Size: 421 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Forest Fire Prediction Using Data Mining Techniques
This repository contains a comprehensive analysis and modeling project focused on predicting forest fire areas using the Fire Weather Index (FWI). The project is based on data sourced from the UCI Machine Learning Repository's Forest Fires dataset.
Project Overview
The study applies various data mining and visualization techniques to explore the factors that influence forest fires and to develop predictive models. One key transformation used is the Log Transform to normalize the data for better accuracy in prediction models. This transformation was necessary due to the skewed nature of the burned area data, allowing for a more accurate application of linear regression.
Key Components:
- Data Analysis: Exploratory Data Analysis (EDA) to identify key environmental factors impacting forest fires, including temperature, wind speed, and humidity.
- Log Transform: Applied to normalize the burned area data and improve the linear regression model's performance.
- Predictive Modeling: Utilizes transformations and linear regression models to predict the burned area.
- Visualization: Multiple visualizations, such as histograms and QQ plots, to assess data distributions and model accuracy.
Files:
Forest Fire Prediction Using Data Mining Techniques.R: R script that contains the entire process of data filtering, Log Transform, and modeling.
forestfires.csv: Dataset of forest fires in Portugal used for training and testing the models.
Forest Fire Prediction Using Data Mining Techniques.pdf: Documentation of the project methodology, results, and conclusion.
