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.
Getting Started with scikit-learn Pipelines for Machine Learning: Building a pipeline from the ground up. (All code in this post is also included in this GitHub repository.)
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
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This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.