https://github.com/prajeshshrestha/modeling-data-with-data-mining-algorithms
https://github.com/prajeshshrestha/modeling-data-with-data-mining-algorithms
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/prajeshshrestha/modeling-data-with-data-mining-algorithms
- Owner: prajeshshrestha
- Created: 2023-10-10T12:52:03.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-10-10T13:34:00.000Z (over 1 year ago)
- Last Synced: 2025-01-11T14:48:17.766Z (5 months ago)
- Language: Jupyter Notebook
- Size: 39 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Explore the data mining projects on various datasets from the UCI Machine Learning Repository. Each project includes an IEEE formatted paper summarizing the exploratory dataset analysis and the performance of corresponding machine learning algorithms on the dataset.
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### A) Nepali Handwritten Digit Recognition - [PCA (Principal Component Analysis)](PCA/)
- **Dataset:** Explored Nepali Handwritten Digit Recognition dataset.
- **Algorithm:** Employed Principal Component Analysis (PCA) for dimensionality reduction.
### B) Credit Card Approval - [KNN (K-Nearest Neighbors)](KNN/)
- **Dataset:** Analyzed Credit Card Approval dataset.
- **Algorithm:** Implemented K-Nearest Neighbors (KNN) for classification.
### C) Heart Disease Classification - [Decision Tree](Decision%20Tree/)
- **Dataset:** Investigated Heart Disease dataset for classification.
- **Algorithm:** Utilized Decision Trees for predictive modeling.
### D) Student Dropout Prediction - [Naive Bayes](Naive%20Bayes/)
- **Dataset:** Examined Student Dropout prediction task dataset.
- **Algorithm:** Employed Naive Bayes for classification.
### E) MNIST Digit Recognition - [Artificial Neural Network](Artificial%20Neural%20Networks/)
- **Dataset:** Explored the MNIST dataset.
- **Algorithm:** Developed an Artificial Neural Network (ANN) from scratch for digit recognition.
For detailed insights and results, refer to the respective IEEE formatted papers presented within each project.