what, why and how to do Feature Scaling, Normalization Vs Standardization, common mistakes a beginner may do on Feature Scaling
5 Part series of most important Data Pre-Processing Techniques of Machine Learning: Encode Categorical Values, One-Hot Encoding, Ordinal Encoding, Categorical Data types explained with examples
Major imputation techniques in Machine learning, US census data, Listwise Deletion, Impute NaN with mean, Trimmed Mean, median, mode, Drop columns if >60% of data is missing
This is a 5 Part series of most important Data Pre-Processing Techniques of Machine Learning. Part 1 – Verify data types of the variables/features.
How to impute missing values using SimpleImputer and ColumnTransformer, Disadvantages of SimpleImputer, Advantage of ColumnTransformer