https://github.com/eeddaann/data-mining-project

https://github.com/eeddaann/data-mining-project

boosting confusion-matrix data-mining feature-engineering feature-selection gaussian-naive-bayes-implementation knn-classifier machine-learning mlp-classifier pca-analysis roc-curve

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/eeddaann/data-mining-project
• Owner: eeddaann
• Created: 2017-11-02T18:10:20.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-01-27T22:43:43.000Z (over 7 years ago)
• Last Synced: 2025-04-01T13:51:14.987Z (7 months ago)
• Topics: boosting, confusion-matrix, data-mining, feature-engineering, feature-selection, gaussian-naive-bayes-implementation, knn-classifier, machine-learning, mlp-classifier, pca-analysis, roc-curve
• Language: Jupyter Notebook
• Size: 7.04 MB
• Stars: 3
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Data Mining Project

This notebook is the final project for data-mining course.

For this project we applied data-mining techniques with python's scikit-learn library.

The project consist:

1. **Data Exploration:**

    - statistics about the features

    - scatter plots for the features

    - correlation matrix

    - violinplot

2. **Feature Engineering:**

    - LDA

    - PCA

    - Modification for PCA

    - Feature Generation

3. **KNN**

    - Hyperparameter optimization

    - apply with different preprocessing

4. **Gaussian Naive Bayes**

    - apply with different preprocessing

5. **Multilayer Perceptron:**

    - apply with different preprocessing 

6. **Boosting:**

    - Based on decision trees

    - Based on Gaussian Naive Bayes

7. **Evaluation**:

    - Confusion matrix

    - Receiver operating characteristic (ROC)