What is the process of cleansing a large dataset to extract meaningful information?

Question

Accepted Answer

Data cleansing involves identifying and correcting errors in a dataset to ensure accuracy and reliability for analysis.

1. Data Profiling: Analyze the dataset to understand its structure, types, and quality. For example, check for missing values or outliers.
2. Removing Duplicates: Identify and eliminate duplicate records to ensure each entry is unique. For instance, if customer data has multiple entries for the same person, keep only one.
3. Handling Missing Values: Decide how to deal with missing data, either by removing records, imputing values, or using algorithms that can handle missing data. For example, replacing missing age values with the average age.
4. Standardization: Ensure consistency in data formats, such as date formats (MM/DD/YYYY vs. DD/MM/YYYY) or text casing (all lowercase).
5. Validation: Check data against predefined rules or constraints to ensure accuracy. For example, validating that a phone number contains only digits.
6. Transformation: Convert data into a suitable format for analysis, such as normalizing numerical values or encoding categorical variables.

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