In this Python Machine Learning Guided Project, we will explore how to manage a common problem for house price predictions. Too many features.
There are many parts to a house and real estate listings include them all. Starting with 173 features we were able to select our features on an interative process, getting down to only 33 features and achieving the same r-squared score.
This was done by using Shaply Values and examining the real impact of of features on the final output of the model. Removing the least important features, then repeat. Everytime you remove features the remaining features are used in a slightly different way and then which is the next best feature to remove.
This process is repeated until we end up with only 33 features.
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