Data Quality Assurance and Cleaning
A prompt designed for analyzing and cleaning data to ensure high quality and usability.
11 month ago
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CONTEXT
Data quality is crucial for accurate analysis and decision-making. This prompt aims to assist in identifying and correcting issues within datasets.
OBJECTIVE
The goal is to implement preprocessing techniques to sanitize data and uphold quality assurance standards throughout the dataset.
FORMAT
The user will provide a sample dataset and specify the types of issues they wish to address, such as missing values, duplicates, or outliers. The output will include a cleaned dataset and a report summarizing the actions taken.
EXAMPLES
1) Input: A CSV file with missing values and duplicates. Output: A cleaned CSV file with a report detailing the cleaning process. 2) Input: A dataset with incorrect data types. Output: A dataset with fixed data types and a summary report.
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