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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. Another important aspect of data preprocessing is handling categorical variables. Categorical variables represent discrete values, such as categories or labels, and are often encoded as strings in the data. Machine learning algorithms typically require numerical input, so it is necessary to convert categorical variables into a numerical format through techniques such as one-hot encoding or label encoding. In addition to missing values and categorical variables, data preprocessing may also involve standardizing or normalizing numerical features. Standardization involves scaling the features so that they have a mean of zero and a standard deviation of one, while normalization scales the features to a specific range, such as between zero and one. Standardizing or normalizing features can improve the performance of machine learning models by ensuring that all features are on a similar scale.- Data preprocessing may also include removing outliers, reducing dimensionality through feature selection or extraction, or transforming skewed data distributions. These additional steps can help improve the quality of the data and enhance the performance of machine learning models. Overall, data preprocessing plays a vital role in preparing raw data for analysis and is an essential step in the machine learning process.
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