How to prepare data to effectively train your machine Learning models

• It is estimated that on a typical machine learning project about 50-60% of the time is consumed in data preparation.
• Aim of this repository is to get an introduction to how can you clean and transform your raw data.

Table Of Content

• Chapter 1: Feature Selection
  • Visualizing correction between features
  • Handling High Multicolinearity - Using Feature Correlation
  • Handling High Multicolinearity - Using Variance Inflation Factor
  • Univariate feature selection (Chi2, ANOVA)
  • Wrapper Methods
  • Embedded Methods

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Work in progress: I will be adding more content to this repo. Stay tuned. ツ

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