Feature Preprocessing for Machine Learning: One-Hot Encoding and Scaling

Feature preprocessing is the set of transformations that convert raw inputs into a form a Machine Learning algorithm can consume, applied before model training. When a dataset mixes feature types, we must prepare the data before feeding it into a Machine Learning algorithm. This happens when inputs (also called features or covariates) include categories such as gender or geographic region alongside features on different numerical scales, for instance a person’s weight or height.

A Machine Learning algorithm typically requires data in a specific type — often numerical only. ML algorithms also perform better or converge faster when data is preprocessed before training. Because the step happens before model training, we call it preprocessing. This article focuses on two main feature-preprocessing methods: feature scaling (normalisation) and feature standardisation.

Data exploration with Pandas, scaling, one-hot encoding and the Keras model follow below.

Data Exploration with Pandas: info(), describe(), groupby()

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Data Preprocessing: Normalisation vs. Standardisation

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Transforming Features with MinMaxScaler and OneHotEncoder

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Creating and Evaluating a Keras Neural Network on Preprocessed Data

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Conclusion: A One-Step Column Transformer Before Model Training

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References

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