Machine Learning

Introduction

Data analysis of various financial datasets and applying numerous ML models with strong emphasis on feature engineering and model evaluation and selection:

• Regression (OLS, PLS, Ridge, Lasso, Elastic Net)
• PCA
• Classification (Logistic Regression, KNN, Decision Trees, Random Forest)
• Clustering (K-Means, Gaussian Mixture Models, Hierarchical Clustering Algorithm, DBSCAN)
• Neural Networks (Forward Feed Neural Network)

Running Jupyter Notebook

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This repository represents group project work for course in Machine Learning for advanced degree Masters in Computational Finance, Union University.