3: Data preparation
Abstract
This chapter lays a crucial foundation and delves into the intricacies of data preparation methods. Starting with data quality issues, it enables data scientists to improve the accuracy of their analysis and makes sure that false data points do not significantly affect the outcomes. The chapter covers several important topics, approaches, and techniques for dealing with missing data, noisy data, and inconsistencies–common problems in real-world datasets. It discusses on dimensionality reduction strategies. The problem of dimensionality has grown significantly as a result of the exponential rise of data. The process of dimensionality reduction is essential for streamlining data and enhancing the effectiveness of future ...
Get Fundamentals of Data Science now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
