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Categorical features are types of data that may be divided into groups. They are three common categorical data types: binary, binary and nominal. In this article, we will learn how combining categorical features can improve your machine learning model performance. We are going to use the Financial Inclusion in Africa dataset from the Zindi competition page. The objective of this dataset is to predict who is most likely to have a bank account. We have 13 variables in the dataset, 12 independent variables, and 1 dependent variable. The next step is to separate the variables and target(bank_account) from the data.