A step-by-step guide to building a machine learning model that predicts S&P 500 market movements using Random Forest classifier and Python. The tutorial covers data preprocessing, model implementation
, backtesting, and feature engineering, improving prediction precision from 47% to 57%.
Reasons to Read -- Learn:
how to build a complete market prediction system using Python, with practical code examples for data preprocessing, model training, and backtesting that you can implement immediately
how feature engineering can improve model performance, specifically how adding rolling averages and trend indicators increased prediction precision from 47% to 57%
practical machine learning concepts through real-world financial data analysis, including backtesting methodology and model evaluation using precision scores
publisher: @anixlynch
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