Complete Guide To Handling Categorical Data Using Scikit-Learn

Complete Guide To Handling Categorical Data Using Scikit-Learn

Complete Guide To Handling Categorical Data Using Scikit-Learn. Handling categorical features to preprocess before building machine learning models. Techniques of encoding categorical features to numeric.

Dealing with categorical features is a common thing to preprocess before building machine learning models. In real-life data science scenario, it means that the dataset has an attribute stored as text such as days of the week(Monday, Tuesday,.., Sunday), time, colour(Red, Blue, …), or place names, etc. 

Categorical features have a lot to say about the dataset thus it should be converted to numerical to make it into a machine-readable format. Focusing only on numerical variables in the dataset isn’t enough to get good accuracy. Often categorical variables prove to be the most important factor and thus identify them for further analysis. Most of the machine learning algorithms do not support categorical data, only a few as ‘CatBoost’ do. 

There are a variety of techniques to handle categorical data which I will be discussing in this article with their advantages and disadvantages.

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