Data Processing, Cleaning, correcting and preparing data so it is accurate and usable, Data Cleaning, Identifying and fixing errors in a dataset, Data Quality, A measure of how accurate, complete and reliable data is, Outlier, A value that is significantly different from the rest of the data, Extreme Value Removal, Removing impossible or unrealistic values from a dataset, Anomaly, An unusual value that does not fit the expected pattern, Noise, Random errors or unwanted values within data, Noise Removal, Eliminating incorrect values that are likely caused by system or sensor errors, Data Encoding, Converting text values into standard categories or codes, Categorical Data, Data grouped into labels or categories, Standardisation, Making data consistent by using the same format throughout, Missing Value, A blank or incomplete data field, Missing Value Imputation, Estimating and filling in missing data, Validation, Checking data meets predefined rules, Inference, Using available information to make a logical estimate, Reliable Dataset, Data that can be trusted for analysis and decision-making, Machine Learning Preparation, Processing data so it can be used effectively by AI models
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6.2 Data processing matching pairs
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