Input Preprocessing

Input preprocessing is a data preparation technique in machine learning and data science that involves cleaning, transforming, and normalizing raw input data before feeding it into a model. It aims to improve model performance, reduce noise, handle missing values, and ensure data consistency. Common steps include scaling, encoding categorical variables, handling outliers, and feature extraction.

Also known as: Data Preprocessing, Feature Engineering, Data Cleaning, Input Normalization, Preprocessing

Why learn Input Preprocessing?

Developers should learn input preprocessing to build robust machine learning models, as raw data often contains inconsistencies that degrade accuracy. It is essential in applications like natural language processing (for text tokenization), computer vision (for image normalization), and predictive analytics (for handling skewed distributions). Proper preprocessing can prevent overfitting, speed up training, and enhance interpretability.

Learning Resources

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