What are the best practices for data reduction?

What are the best practices for data reduction?

Data reduction involves techniques to minimize the amount of data while preserving its integrity. Key practices include data compression, feature selection, and data aggregation.

Can you explain data compression and how it works?

Data compression reduces the size of data files by encoding information more efficiently. This can be done through lossless methods, which preserve all original data, or lossy methods, which remove some data for greater size reduction.

What is feature selection?

Feature selection is the process of identifying and selecting a subset of relevant features for use in model construction. It helps improve model performance and reduces overfitting.

How does data aggregation help in data reduction?

Data aggregation combines multiple data points into a single summary, reducing the overall data volume while retaining essential information, which is useful for analysis.