What Is Data Cleaning?Data cleaning is the process of preparing and organizing data for analysis. It includes identifying and correcting erroneous or missing data, eliminating unnecessary data, and formatting data in a way that is usable. Data cleaning guarantees that the data is accurate and trustworthy, making it a crucial stage in the data analysis process.To make sure they are working with the best possible data, data scientists and analysts frequently perform data cleansing. The procedure entails locating and fixing data mistakes, inconsistencies, and missing numbers. Moreover, data cleaning entails converting the data into a format that is better suited for analysis.‍Benefits Of Data CleaningData cleaning is an essential part of any successful data analysis process. It is a process of organizing, standardizing, and transforming data so it can be used effectively. Data cleaning can help to improve the accuracy of data, reduce errors, and make data more useful.Different data cleaning techniquesData ProfilingData profiling involves analyzing the structure and content of data sets to identify errors, inconsistencies, and missing values. This process can be done manually or using automated software tools.Data ValidationData validation involves validating the data against a set of rules or standards.Data TransformationData transformation involves changing the format or structure of data sets.Data DeduplicationData deduplication involves identifying and removing duplicate records from data sets. This process can be done manually or using automated software tools.Data StandardizationData standardization involves transforming data into a standardized format.Automated data cleaning techniques include the following:Data CleansingData cleansing involves using software tools to identify and correct errors or inconsistencies in data sets.Data ScrubbingData scrubbing involves using software tools to identify and remove duplicate records from data sets.Data MiningData mining involves using software tools to identify patterns and trends in data sets.Data IntegrationData integration involves using software tools to combine data from multiple sources into a single data set.‍Data EnrichmentData enrichment involves using software tools to add additional information to data sets.Now let's see with a use case: