Master Stock Market Predictions with Machine Learning & Python 📈

In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most stock price models overfit in the real world. We'll start by downloading S&P 500 prices using a package called yfinance. Then, we'll clean up the data with pandas, and get it ready for machine learning. We'll train a random forest model and make predictions using backtesting. Then, we'll improve the model by adding predictors. We'll end with next steps you can use to improve the model on your own. You can find an overview of the project and the code here - https://github.com/dataquestio/project-walkthroughs/tree/master/sp_500 . If you enjoyed this tutorial, check out this link https://bit.ly/3O8MDef for free courses that will help you master data skills. Chapters 00:00 - Introduction 01:28 - Downloading S&P 500 price data 03:30 - Cleaning and visualizing our stock market data 04:29 - Setting up our target for machine learning 08:19 - Training an initial machine learning model 17:01 - Building a backtesting system 23:05 - Adding additional predictors to our model 28:45 - Improving our model 33:37 - Summary and next steps with the model --------------------------------- Join 1M+ Dataquest learners today! Master data skills and change your life. Sign up for free: https://bit.ly/3O8MDef