As data scientists, properly scaling data is a crucial step in data preprocessing, as it ensures that all features in a dataset are on the same scale, leading to optimal performance in many machine learning models. Whether you need to bring data into the same range or transform it into a normal distribution, mastering the techniques of standardization and normalization is essential for any data scientist or machine learning practitioner.

Through practical examples and easy-to-understand illustrations, you will learn how to effectively use standardization and normalization techniques to transform your data into a format that is more interpretable for machine learning models. We will explore various scaling methods offered by the popular Scikit-learn library, including MinMaxScaler, MaxAbsScaler, StandardScaler, and RobustScaler, and discuss their applications in different scenarios, such as handling categorical and numerical variables.

The project scenario involves a sport magazine conducting research on soccer players with a specific focus on left-footed players. The main questions the research aims to answer are:

  • Can a player’s playing style be used to predict if they are left-footed or not?

  • If so, which features are the most significant in making this prediction?

To address these questions, the research utilized Support Vector Machine (SVC) Classification as the machine learning algorithm. The research used three different sets of data for feature scaling: Original data, Normalized data, and Standardized data. Among these, the Standardized data achieved the highest accuracy score of 95.4%, making it the most effective method for feature scaling in this analysis.