anujdutt9/Feature-Selection-for-Machine-Learning
Methods with examples for Feature Selection during Pre-processing in Machine Learning.
This project helps data scientists and machine learning engineers prepare their datasets more effectively for model training. It takes raw, potentially complex datasets and shows how to systematically remove irrelevant or redundant columns (features), resulting in a cleaner dataset that can lead to better model performance and faster training. The end user is a data practitioner looking to optimize their machine learning workflows.
364 stars. No commits in the last 6 months.
Use this if you are a data scientist or machine learning engineer struggling with high-dimensional datasets, slow model training, or poor model performance due to too many irrelevant input features.
Not ideal if you are looking for a no-code solution or do not have experience working with Python and machine learning libraries.
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Last pushed
May 24, 2020
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