Null Values
In data science, null values refer to missing or undefined data points in a dataset. These values can occur due to various reasons such as data entry errors, incomplete data, or data corruption during transmission. Null values can have a significant impact on data analysis and machine learning models as they can lead to biased or inaccurate results. Therefore, it is essential to handle null values appropriately by either removing them from the dataset or imputing them with appropriate values. There are various techniques for handling null values, such as mean imputation, mode imputation, and regression imputation. However, the choice of technique depends on the nature of the data and the analysis being performed.
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