Data Skewness: 11 Transformative Techniques for Enhanced Data Distribution

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Dealing with data skewness is an important step in data preprocessing, especially when working with machine learning models or statistical analyses. Data skewness refers to the asymmetry of the data distribution, where some values are more concentrated on one side of the mean than the other. Skewed data can impact the performance of many algorithms. Here’s how to address data skewness:

1. Identify Data Skewness:

• Before addressing skewness, it’s essential to identify it. You can use statistical measures like skewness or create visualizations, such as histograms or density plots, to visualize the distribution of your data.

a. Statistical Measures:

• Use skewness as a statistical measure to quantify the asymmetry in the distribution of the data.
• Skewness is calculated as the third standardized moment and can be positive, negative, or zero.
• Positive skewness indicates a right-skewed distribution (tail on the right), while negative skewness indicates a left-skewed distribution.

# Calculate skewness
skewness = pd.Series(data).skew()

b. Visualizations:

• Create histograms to…