https://github.com/muneeb706/occupancy-detection-analysis
Results of analysis and comparisons of different data mining algorithms.
https://github.com/muneeb706/occupancy-detection-analysis
classification clustering data-mining weka
Last synced: 5 months ago
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Results of analysis and comparisons of different data mining algorithms.
- Host: GitHub
- URL: https://github.com/muneeb706/occupancy-detection-analysis
- Owner: muneeb706
- Created: 2020-01-23T18:09:34.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-23T20:10:21.000Z (over 6 years ago)
- Last Synced: 2025-01-31T10:42:14.960Z (over 1 year ago)
- Topics: classification, clustering, data-mining, weka
- Size: 2.86 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data Mining Algorithms Analysis
It contains results of the analysis of following data mining algorithms that were applied to occupancy detection dataset.
* Naive Bayes
* Logistic Regression
* Decision Tree
* Artificial Neural Networks
* Support Vector Machine
* Ensemble Methods
* Bagging
* Boosting
* Random Forests
All of the experiments were performed on Weka.
## Tools
* Weka (https://www.cs.waikato.ac.nz/ml/weka/index.html)
## Dataset
Dataset can be downloaded from (https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+). It is also available in the dataset folder.