Machine Learning Decision Tree w Bagging Implementation

https://docs.google.com/document/d/1dw_kCWJv_WjaM9C-3Hvuna2rREuLzHWOajnhppyGpp0/edit?usp=sharing

This week I've implemented a decision tree algorithm that splits upon correlation creates a regressor based upon an input set of training data. I've also implemented bagging that uses randomized datasets with replacement to smooth out potential overfitting problems over many trees.

Machine Learning Stock Portfolio Optimization in Python

This week in Machine Learning for Trading I've implemented an optimizer using SciPy that takes any number of stock symbols and most effectively allocates one's portfolio among the provided options based upon previous data in a given date range. The metric for evaluating the profitability of a portfolio is based upon the Sharpe Ratio, which adjusts a stock's income against its risk.

This work correlates to work I'd done using optimizers to find the optimal behavior for a multi-agent system in the Reinforcement Learning class.