Feature Selection in Python | Machine Learning Basics | Boston Housing Data

Hey Everyone! I'm a first year machine learning PhD student. My research focuses on recommender systems applications in sports science including case-based reasoning techniques to support marathon runners. I have a YouTube channel about my experience doing a PhD and productivity tips but I also have made a few videos related to my research. This video is about feature selection in Python using an example of a kNN regressor on the Boston Housing Data. There are timestamps in the description as well as the python notebook available for download. I hope this will be of interest to some of you and be sure to subscribe if you would like to see more.

Previous videos on Machine Learning:
Supervised vs Unsupervised Learning: https://youtu.be/2Z1B0xESzMw
Case-Based Reasoning: https://youtu.be/Iy2gO8svdMI
kNN: https://youtu.be/SDSC4yLLBKM

Link to downloadable python notebooks: https://drive.google.com/open?id=10jx0A4NBQl7i_BU00QUZlgILYg38HbJX

Timestamps:
00:00 - intro
0:55 - what is feature selection / why
3:30 - description of the Boston Housing Dataset
6:00 - recap of the last model
7:47 - filtering features by variance
10:20 - filtering features by correlation
15:40 - feature selection using a wrapper - sequential feature selection
21:24 - relationships between variables

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