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Mastering Linear Regression with NumPy and Matplotlib
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(This is a portion of a live class via Zoom on February 18, 2021, for my Engineering Computations course at the George Washington University.)
Follow this live-coding lesson as we continue from the last session, diving deeper into linear regression using Python. Using the same notebook, we start by reviewing our Python script for computing linear regression parameters manually. Then, we introduce and utilize the powerful NumPy library functions such as `polyfit` and `poly1d` to simplify the process. Not only do we cover fitting a single line to data, but we also explore split regression to analyze potential changes in data trends over time. Finally, we polish our plots using Matplotlib for better visualization.
00:00 Introduction and Recap
01:36 Using NumPy for Linear Regression
07:53 Plotting Linear Regression with Matplotlib
11:31 Exploring Split Regression
19:09 Conclusion and Key Takeaways
Follow this live-coding lesson as we continue from the last session, diving deeper into linear regression using Python. Using the same notebook, we start by reviewing our Python script for computing linear regression parameters manually. Then, we introduce and utilize the powerful NumPy library functions such as `polyfit` and `poly1d` to simplify the process. Not only do we cover fitting a single line to data, but we also explore split regression to analyze potential changes in data trends over time. Finally, we polish our plots using Matplotlib for better visualization.
00:00 Introduction and Recap
01:36 Using NumPy for Linear Regression
07:53 Plotting Linear Regression with Matplotlib
11:31 Exploring Split Regression
19:09 Conclusion and Key Takeaways
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