ML with Python : Zero to Hero | Video 3 | Part 2| Data Cleaning | Venkat Reddy AI Classes

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In this comprehensive video, we delve into the critical aspects of data cleaning, focusing on data validation, data sanitization, and various imputation techniques. Learn how to effectively handle outliers, missing values, and understand the importance of maintaining data integrity for accurate analysis.
Data Cleaning Fundamentals: Understand the significance of cleaning data before analysis and the potential impact of outliers and missing values.
Outlier Detection: Learn how to identify and handle outliers in your data to avoid skewed analysis results.
Data Validation: Explore techniques to validate data and ensure its accuracy and consistency.
Data Sanitization: Discover methods to sanitize data, including handling default and erroneous values.
Standalone Imputation: Learn standalone imputation techniques to fill missing values without relying on other variables.
Imputation Based on Target: Understand how to perform imputation based on the target variable to maintain data integrity and accuracy.
Who Is This For?
Aspiring Data Scientists: Perfect for beginners who want to understand the importance of data cleaning and how to perform it effectively.
Students and Professionals: Ideal for those looking to enhance their data cleaning skills and ensure high-quality data for their projects.
Tech Enthusiasts: Beneficial for anyone interested in data science and the processes involved in maintaining data integrity.
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