Data cleansing and processing

Data cleaning and processing is a key step in the data science industry, where unstructured data is processed and used to improve its quality, integrity, and usability. These processes play a key role in ensuring that the data used for assessment and decision-making is accurate, precise, and dependable. This section will explore the importance of data cleansing and processing and discuss these processes’ basic concepts and techniques.

Data cleaning, also known as data cleaning or data scrubbing, refers to the process of identifying, correcting, or removing errors, inconsistencies, and anomalies from a data structure. Raw data often contain missing values, anomalies, records duplicates, inconsistent characters, or other abnormalities that are biased if not dealt with or may produce inaccurate results. Data cleansing aims to address these issues and improve data collection.

However, using the information to make the data relevant to analysis...