Implementations of foundational ML models

This repository contains implementations of fundamental machine learning models and algorithms. The models are implemented in Python, while the model evaluations is in R.

Repository Structure

The following is an overview of the repository structure.

├── supervised_learning/        # Implementations of supervised learning algorithms
│   ├── kernels_SVR.py          # Support Vector Regression using kernel methods
│   ├── linear_regression.py    # Implementation of Linear Regression
│   ├── multinom_logreg.py      # Multinomial Logistic Regression
│   ├── neural_network.py       # Neural Network (regression and classification)
│   ├── ridge_regression.py     # Ridge Regression for regularization
│   └── tree_rf.py              # Decision Trees and Random Forests
├── unsupervised_learning/      # Implementations of unsupervised learning algorithms
│   ├── hierarchical_clustering.py  # Hierarchical Clustering
│   └── pca.py                      # Principal Component Analysis
├── other/                      # Miscellaneous files and additional resources
│   └── model_evaluation.Rmd    # R Markdown file for model evaluation (CV)