Overview
Data preprocessing is a critical step in the machine learning pipeline that involves preparing raw data for analysis. This process includes cleaning the data to remove inaccuracies, transforming it to enhance its usability, and ensuring that it is in a suitable format for machine learning algorithms...
Key Terms
Example: Removing duplicate entries in a customer database.
Example: Transforming income data to a scale of 0 to 1.
Example: A person with an income of $1,000,000 in a dataset of average incomes.
Example: Converting 'red', 'blue', 'green' into three binary columns.
Example: Scaling height and weight to a common scale.
Example: Using 80% of the dataset to train a model.
Related Topics
Feature Engineering
The process of using domain knowledge to create features that make machine learning algorithms work.
intermediateModel Evaluation
Techniques to assess the performance of machine learning models.
intermediateData Visualization
The graphical representation of information and data to understand trends and patterns.
intermediateKey Concepts
Data CleaningData TransformationFeature ScalingData Encoding