Computer - Multimedia Classes

Data cleaning, also known as data cleansing or data scrubbing, is a crucial step in the data preprocessing pipeline. It involves identifying and correcting errors or inconsistencies in datasets to improve their quality and reliability. Here are some best methods for data cleaning:

Handling Missing Data:

Identify and understand the extent of missing data.

Choose an appropriate method for handling missing values, such as imputation (mean, median, or mode), deletion of missing records, or using advanced imputation techniques.

Outlier Detection and Treatment:

Detect outliers using statistical methods or visualization tools.

Decide whether to remove outliers or transform them to be within an acceptable range.

Data Type Conversion:

Ensure that data types are consistent and appropriate for analysis.

Convert categorical variables to the correct data type (e.g., converting text categories to numerical representations).

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