S&P 500 company correlation table - Python Programming for Finance p. 8
Hello and welcome to part 8 of the Python for Finance tutorial series. In the previous tutorial, we showed how to combine all of the daily pricing data for the S&P 500 companies. In this tutorial, we're going to see if we can find any interesting correlation data. To do this, we'd like to visualize it, since it's a lot of data. We're going to use Matplotlib for this, along with Numpy.
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⚡
AI Lesson Summary
✦ V3 skills
⚖ Mixed
This video teaches how to create a correlation table for S&P 500 companies using Python and visualize the data using heatmaps, with plans to apply machine learning to analyze the relationships between companies. The video covers correlation analysis, data visualization, and machine learning concepts.
Key Takeaways
- Read CSV files using pandas
- Create a correlation table of data frames using correlation values
- Analyze relationships between companies over 15 years
- Identify positive and negative correlations between companies
- Visualize the correlation table using a heatmap
- Zoom in on specific companies in the graph
- Plan to create features and labels for machine learning
💡 Correlation analysis can help identify relationships between companies, and machine learning can be used to further analyze these relationships.
More on: ML Maths Basics
View skill →
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