/machine-learning

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

Primary LanguageJupyter Notebook

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)

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