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:
- Data Exploration:
- statistics about the features
- scatter plots for the features
- correlation matrix
- violinplot
- Feature Engineering:
- LDA
- PCA
- Modification for PCA
- Feature Generation
- KNN
- Hyperparameter optimization
- apply with different preprocessing
- Gaussian Naive Bayes
- apply with different preprocessing
- Multilayer Perceptron:
- apply with different preprocessing
- Boosting:
- Based on decision trees
- Based on Gaussian Naive Bayes
- Evaluation:
- Confusion matrix
- Receiver operating characteristic (ROC)