04_​Dimensionality_​Reduction_​exercise

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Dimensionality Reduction - exercise

Introduction to Machine Learning Algorithms course - Session 4
Exercise 4
Apply the following dimensionality reduction techniques to the data:
- Filter out columns with a low variance
- Filter out one of two columns with a high linear correlation
- Replace numeric columns with principal components
- Filter out columns which are not important in predicting the target column

URL: Ames Housing Dataset on kaggle https://www.kaggle.com/prevek18/ames-housing-dataset
URL: Description of the Ames Iowa Housing Data https://rdrr.io/cran/AmesHousing/man/ames_raw.html
URL: Seven Techniques for Data Dimensionality Reduction https://www.knime.com/blog/seven-techniques-for-data-dimensionality-reduction
URL: 3 New Techniques for Data-Dimensionality Reduction in Machine Learning https://thenewstack.io/3-new-techniques-for-data-dimensionality-reduction-in-machine-learning/
URL: Slides (Introduction to ML Algorithms course) https://www.knime.com/form/material-download-registration

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