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Discerption:

Handling Missing Values:
Missing data can distort analysis results and lead to biased conclusions. Dealing with missing values involves strategies like imputation, where missing values are estimated based on other available data points, or elimination, where rows with missing values are removed.

2. Addressing Outliers:
Outliers are extreme values that can skew statistical analyses and models. Detecting outliers helps determine whether they're genuine data points or errors. Depending on the situation, outliers can be removed, transformed, or treated separately.

#CleanData #MissingValueFix #OutlierTreatment #DataTransformation #Standardization #CategoricalHandling #DataIntegration #DuplicateRemoval #DataQuality #weddingdocumentation

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