What is the main purpose of data preprocessing?

The main purpose of data preprocessing is to clean and transform raw data. This step is critical in the data science process because raw data often comes from various sources and may contain inaccuracies, inconsistencies, duplicate entries, and missing values that can lead to unreliable results if not addressed.

During data preprocessing, tasks such as removing duplicates, filling in missing values, and correcting data types are performed to create a tidy dataset that can be reliably analyzed. Additionally, transformation processes like normalization or scaling may be applied to adjust the data for better performance in modeling.

Well-processed data sets ensure that subsequent analyses, whether they be statistical analysis or machine learning applications, are based on accurate and relevant inputs, ultimately improving the quality and reliability of the results generated.

Other options, while related to data analysis, do not capture the essence of preprocessing. Analyzing data effectively and visualizing data are dependent on having clean data, but they are not the primary focus of preprocessing. Generating new data samples is also not a function of preprocessing; rather, it pertains to techniques like data augmentation or synthetic data generation, which occurs later in the data handling process.