Data cleaning refers to the process of identifying and correcting (or removing) errors and inconsistencies in a dataset so that it can be analyzed and used effectively. This may involve removing duplicates, handling missing values, converting data into a consistent format, and more. The goal of data cleaning is to make sure that the data is accurate, complete, and trustworthy. The steps in the data-cleaning process typically include: Inspection: Examine the data to identify any errors or inconsistencies. Data type conversion: Convert the data into a consistent format, such as converting strings to numbers or dates to a standard format. Handling missing values: Impute or remove missing values as appropriate. Outlier detection and treatment: Identify and correct outliers that may impact analysis. Duplicate removal: Remove duplicate records from the data Validation: Verify the accuracy and consistency of the data after cleaning. Saving the cleaned data: Save the cleaned data in a f