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 .

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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

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