Machine Learning Preprocessing

Machine Learning Preprocessing is a critical step in the data science pipeline that involves cleaning, transforming, and preparing raw data into a suitable format for training machine learning models. It includes techniques such as handling missing values, scaling features, encoding categorical variables, and reducing dimensionality to improve model performance and accuracy. This process ensures that the data is consistent, normalized, and free from biases that could negatively impact the learning algorithms.

Also known as: Data Preprocessing, Feature Engineering, Data Wrangling, ML Preprocessing, Preprocessing

Why learn Machine Learning Preprocessing?

Developers should learn and apply preprocessing techniques when working with real-world datasets, which are often messy, incomplete, or inconsistent, to enhance model robustness and predictive power. It is essential in use cases like fraud detection, recommendation systems, and image classification, where data quality directly affects outcomes. Without proper preprocessing, models may suffer from issues like overfitting, poor generalization, or computational inefficiencies.

Learning Resources

• docs: Scikit-learn Preprocessing Documentation
• tutorial: Data Preprocessing in Machine Learning: 7 Easy Steps To Follow
• course: Machine Learning Data Preprocessing Course on Coursera
• book: Feature Engineering for Machine Learning - Book by Alice Zheng and Amanda Casari