5 Essential Machine Learning Techniques to Master Your Data Preprocessing

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

A Comprehensive Data Science Guide to Preprocessing for Success: From Missing Data to Imbalanced Datasets

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In just about any organization, the state of information quality is at the same low level

– Olson, Data Quality

Data is everywhere! Furthermore, it is at the heart of many real-world problems. As data scientists and machine learning engineers, we spend the majority of our time working with data. It is important that we master it! The header image was created by the author.

In machine learning, the path from raw data to a well-tuned model is paved with preprocessing techniques that set the way for success. Data scientists and machine learning engineers spend significant time preparing data, as clean, well-structured, and engineered data leads to better model performance and insights.

We’ve all heard it:

Garbage in, garbage out!

This blog explores five crucial preprocessing techniques that every data scientist must master: handling missing data, scaling and normalization, encoding categorical data, feature engineering, and dealing with imbalanced data. Each topic is critical in transforming messy, real-world datasets into something your machine learning algorithms can genuinely learn from.

This comprehensive guide will cover everything you need to know about data preprocessing, whether cleaning up a dataset, scaling your features, encoding categorical variables, or fighting the imbalance… Read the full blog for free on Medium.

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Published via Towards AI