Machine Learning Algorithms Documentation

Complete Code Collection - 20 Algorithms

Dataset: Heart Disease Dataset (UCI Machine Learning Repository)

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Table of Contents

1. AdaBoost Classification
2. Fuzzy C-Means Clustering
3. DBSCAN Clustering
4. Ensemble Learning (Voting Classifier)
5. Generalized Regression Neural Network (GRNN)
6. HDBSCAN Clustering
7. Hierarchical Clustering
8. Hidden Markov Model (HMM)
9. K-Means Clustering
10. Language Model (Bigram LM)
11. Multilayer Perceptron (MLP)
12. Modified K-Means (K-Means++)
13. Random Forest Classification
14. Random Forest Regression
15. Recurrent Neural Network (RNN)
16. Self-Training (Semi-Supervised)
17. Self-Organizing Map (SOM)
18. Support Vector Machine (SVM)
19. XGBoost Classification
20. CatBoost Classification

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Summary

#	Algorithm	Type	Use Case
1	AdaBoost	Ensemble	Classification
2	Fuzzy C-Means	Clustering	Soft clustering
3	DBSCAN	Clustering	Density-based clustering
4	Ensemble Learning	Ensemble	Voting classifier
5	GRNN	Neural Network	Regression
6	HDBSCAN	Clustering	Hierarchical density-based
7	Hierarchical	Clustering	Agglomerative clustering
8	HMM	Probabilistic	Sequential data
9	K-Means	Clustering	Hard clustering
10	Bigram LM	Language Model	Text generation
11	MLP	Neural Network	Classification
12	Modified K-Means	Clustering	Improved initialization
13	Random Forest	Ensemble	Classification
14	Random Forest	Ensemble	Regression
15	RNN	Neural Network	Sequence prediction
16	Self-Training	Semi-Supervised	Limited labels
17	SOM	Neural Network	Dimensionality reduction
18	SVM	Classification	Binary/multi-class
19	XGBoost	Gradient Boosting	Classification
20	CatBoost	Gradient Boosting	Classification

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