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Data Cleaning Strategies.
Data Cleaning Strategies Table Of Contents: What Is Data Cleaning? Handling Missing Data. Handling Outliers. Removing Duplicate Data. Standardizing Column Names. Standardizing Data Formats Correct Data Types. Feature Selection. Feature Engineering. Addressing Class Imbalance. Dealing With Multicollinearity. Encoding Categorical Variables. Data Normalization & Standardization. Handling Text Data. Handling Time Series Data. Saving the Cleaned Data. (1) What Is Data Cleaning? In simple terms, data cleaning is the process of fixing or removing incorrect, incomplete, or irrelevant data from a dataset. It ensures that the data is accurate, consistent, and ready for analysis or use in a machine learning model. (2)
