![]() ![]() ![]() ![]() We will also cover scaling and normalization, creation of new features, handling imbalanced data, handling skewness and kurtosis, handling rare categories, handling time-series data, feature transformation, one-hot encoding, count and frequency encoding, binning, grouping, and text preprocessing.īy the end of this guide, you will have a comprehensive understanding of Feature Engineering techniques and how they can be used to enhance the performance of your machine learning models. Then, we will move on to encoding categorical variables, which is an essential step when working with non-numerical data. We will start with feature selection and extraction, which involves identifying the most important features in the data. In this guide, we will cover a range of techniques that are commonly used in Feature Engineering. ![]()
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