Data Cleaning In Data Mining | Step by Step Data Cleaning Process | Data Cleaning | Simplilearn
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Data cleaning is the process of correcting errors and inconsistencies in a dataset to enhance its quality and reliability. It involves various steps including removing duplicates, handling missing data, correcting structural errors, and validating data. Effective data cleaning is essential for obtaining reliable insights from data.
Data Cleaning In Data Mining | Step by Step Data Cleaning Process | Data Cleaning | Simplilearn
- 2. What is Data Cleaning ? It is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to improve its quality and reliability.
- 3. Why Data Cleaning is Important? Messy Missing values Unreliable Data Insights
- 4. Steps in Data Cleaning Data Import and Understanding 01
- 5. Steps in Data Cleaning Remove Duplicates 02
- 6. Steps in Data Cleaning Handle Missing Data 03
- 7. Steps in Data Cleaning Correct Structural Errors 04
- 8. Steps in Data Cleaning Standardization and Normalization 05
- 9. Steps in Data Cleaning Handling Outliers 06
- 10. Steps in Data Cleaning Filter Unnecessary Data 07
- 11. Steps in Data Cleaning Handle Inconsistent Data 08
- 12. Steps in Data Cleaning Data Validation and Integration Checks 09
- 13. Steps in Data Cleaning Export Cleaned Data 10
- 14. Tools for Data Cleaning
- 15. QUIZ Which of the following is NOT a common Data Cleaning Technique ? (A) Removing duplicate records (B) Handling missing data (D) Adding random noise to data (C) Normalizing data