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Applied Data Science Coding in Python: How to standardize Data

Standardizing data in Python using scikit-learn is a way to bring all of the features in your dataset to the same scale. This is useful because some machine learning models can be sensitive to the scale of the input features. The process of standardizing involves subtracting the mean of the feature from each value and then dividing by the standard deviation. This way, the resulting values will have a mean of 0 and a standard deviation of 1. To standardize data in Python using scikit-learn, you can use the StandardScaler class from the sklearn.preprocessing module. This class has a fit_transform() method that can be used to standardize the data. It takes the input data as an argument and returns the standardized data.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to standardize Data.

Applied Data Science Coding in Python: How to standardize Data

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