Data Preprocessing Techniques: 6 Steps to Clean Data in Machine Learning

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Nicolas Azevedo
Data Scientist and Machine Learning Engineer
A man using a remote drone to clean a database
Originally published on Nov 22, 2021Last updated on Sep 29, 2023

Key Takeaways

What is an example of a data preprocessing technique?

An example of a data preprocessing technique is data cleaning. It is the process of detecting and fixing bad and inaccurate observations from your dataset.

Why is data preprocessing important?

If you skip the data preprocessing step, it will affect your work later on when applying this dataset to a machine learning model. Most of the models can’t handle missing values. By preprocessing the data, you’ll make the dataset more complete and accurate.

What are the major steps of data preprocessing?

Data cleaning: cleaning out meaningless data, incorrect records or duplicate observations, adjusting or deleting observations that have missing data points, and fixing typos and inconsistencies in the dataset. Secondly, we need to reduce the amount of attributes/features so as not to affect the model’s performance when we feed it the dataset.