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Regression models that iteratively select the most statistically significant variables based on R-squared values.
Distinct from Regression Models: Focuses on the iterative variable selection process rather than general continuous outcome estimation.
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This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro
Provides forward stepwise regression to iteratively select the most significant variables for a linear model.
This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The
Provides stepwise regression to identify the most impactful variables for predictive financial models.