5 Most important Data Pre-Processing Techniques – Feature Scaling – Part IV
what, why and how to do Feature Scaling, Normalization Vs Standardization, common mistakes a beginner may do on Feature Scaling
Category Data Preprocessing
5 Most important Data Pre-Processing Techniques – Encode Categorical Values – Part III
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
5 Most important Data Pre-Processing Techniques – Impute missing data – Part II
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
5 Most important Data Pre-Processing Techniques for Machine Learning – Part I
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?
How to impute missing values using SimpleImputer and ColumnTransformer, Disadvantages of SimpleImputer, Advantage of ColumnTransformer