Data preprocessing prepares raw data for analysis from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Data preprocessing is a crucial step in the machine learning pipeline that involves transforming raw data into a format that is suitable for analysis. This process is essential because raw data is often messy, incomplete, or inconsistent, which can lead to inaccurate results if not addressed. By performing data preprocessing, we can clean and prepare the data so that it can be effectively used for training machine learning models.
One common task in data preprocessing is handling missing values. Missing values can arise due to various reasons, such as data collection errors or incomplete records. It is important to address missing values before proceeding with the analysis, as they can have a significant impact on the results. There are several strategies for dealing with missing values, such as imputation or deletion, depending on the nature of the data.
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