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How can you handle data skewness in preprocessing?

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Data skewness is a measure of how asymmetric a distribution of values is. It can affect the performance and accuracy of many data science models, especially those that assume normality or use mean-based metrics. Therefore, it is important to handle data skewness in preprocessing, before applying any modeling techniques. In this article, you will learn what data skewness is, how to detect it, and how to transform it using different methods.

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Raghu Etukuru, Ph.D.

AI Scientist | Author of Four Books
SWAKSHAR MUKHERJEE

Facilities Security Management Managerial at Bengal Silicon Valley,linkedin & PM Institute, Inc. certified Six Sigma:…
? Ammar A.

Data Visualization | Data analytics/Analyst | Data scientist | HRIS | HR analytics | People coordinator/analytics |…

1 What is data skewness?

Data skewness is a property of a distribution that indicates how much it deviates from a symmetrical shape. A symmetrical distribution, such as a normal or bell-shaped curve, has a skewness of zero. A positively skewed distribution has a long tail to the right, meaning that most values are clustered to the left of the mean, and some extreme values are much higher than the mean. A negatively skewed distribution has a long tail to the left, meaning that most values are clustered to the right of the mean, and some extreme values are much lower than the mean. The degree of skewness can be quantified by a numerical value, such as the Pearson's skewness coefficient or the Fisher's skewness coefficient.

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Gaurav Shivhare

Data Scientist | Generative AI | NLP | AI/ML | LLM | Product
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Left skewness, like a tail stretching left, means more data are bunched at the high end. Picture income in a neighbourhood where most are wealthy, but a few are not. Right skewness is the opposite: a long tail on the right. Imagine a class test where most students score low, but a few score very high.

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SWAKSHAR MUKHERJEE

Facilities Security Management Managerial at Bengal Silicon Valley,linkedin & PM Institute, Inc. certified Six Sigma: Green & Black Belt, Data Scientist. Noida International University & IIRIS Certified Risk Leader.
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Data skewness is a measure of the asymmetry or lack of symmetry in a set of data distribution. If the data is skewed, it means that the distribution is not centered and one tail is longer or heavier than the other. Positive skewness indicates a longer right tail, while negative skewness indicates a longer left tail. Skewness helps in understanding the shape of the data distribution.

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2 Why is data skewness a problem?

Data skewness can pose a problem for many data science models and methods, because it can distort the statistical properties and assumptions of the data. For example, data skewness can affect the mean, median, mode, standard deviation, variance, and confidence intervals of a distribution. It can also make the data less representative of the underlying population or process, and more sensitive to outliers and noise. Data skewness can also cause issues for models that rely on normality or linearity assumptions, such as linear regression, ANOVA, t-test, and many others. Data skewness can also affect the performance of models that use mean-based metrics, such as mean squared error or mean absolute error, as they can be biased by extreme values.

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SWAKSHAR MUKHERJEE

Facilities Security Management Managerial at Bengal Silicon Valley,linkedin & PM Institute, Inc. certified Six Sigma: Green & Black Belt, Data Scientist. Noida International University & IIRIS Certified Risk Leader.
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Data skewness can be a problem because it can affect the performance and accuracy of certain statistical analyses and machine learning models. Many statistical methods assume a normal distribution, and skewed data deviates from this assumption. This can lead to biased results and incorrect inferences. Skewed data may also impact the performance of machine learning models, as they might struggle to generalize well when the training data has a significant skew. Addressing data skewness through techniques like transformation or using models robust to skewed data can help mitigate these issues and improve the reliability of analyses and predictions.

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3 How to detect data skewness?

There are several ways to detect data skewness, both visually and numerically. Visually, you can use plots such as histograms, boxplots, or density plots to inspect the shape and symmetry of the distribution. You can also use a Q-Q plot to compare the distribution to a normal distribution, and look for deviations from the diagonal line. Numerically, you can use skewness coefficients, such as the Pearson's or the Fisher's, to measure the degree and direction of skewness. A rule of thumb is that if the absolute value of the skewness coefficient is greater than 0.5, the distribution is moderately skewed, and if it is greater than 1, the distribution is highly skewed.

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Raghu Etukuru, Ph.D.

AI Scientist | Author of Four Books
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Skewness in the data can be detected by visualizing it, but measuring it using numerical methods is adequate if we are developing applications and embracing automation. Visual detection: Histograms give a visual representation of the data's distribution. Skewness is indicated by a longer tail on one side of the peak. Boxplots plots display the data's quartiles and can visually indicate skewness through the asymmetry of the 'box' or the length of the 'whiskers'. Numerically, Pearson's Coefficient and Fisher's Skewness are common techniques. A skewness value near 0 indicates a symmetrical distribution, not skewed. Absolute skewness value > 0.5: Moderate skewness. Absolute skewness value > 1: High skewness.

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? Ammar A.

Data Visualization | Data analytics/Analyst | Data scientist | HRIS | HR analytics | People coordinator/analytics | Analytics sales
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Detecting data skewness is like spotting flavor imbalances in a dish ??. Visualize it with histograms, boxplots, or density plots—think of them as taste tests for your data! A Q-Q plot is your culinary guide, helping you compare your data's flavor profile to the "normal dish" standard. Numerically, use skewness coefficients like a seasoning scale. Pearson's or Fisher's coefficients are your taste testers, quantifying the skewness degree. If the absolute value is > 0.5, it's a hint of skewness; > 1, and your data's flavor is dancing to its own spicy beat! ?????

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SWAKSHAR MUKHERJEE

Facilities Security Management Managerial at Bengal Silicon Valley,linkedin & PM Institute, Inc. certified Six Sigma: Green & Black Belt, Data Scientist. Noida International University & IIRIS Certified Risk Leader.
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You can detect data skewness using statistical measures. Here’s a simplified explanation: 1. Skewness Coefficient: 2. Visual Inspection: ? Create a histogram or a box plot to visually inspect the data distribution. Skewed data often shows a noticeable tilt to one side.3. Quantile-Quantile (Q-Q) Plot: ? Q-Q plots compare the quantiles of your data to the quantiles of a theoretical distribution (e.g., normal distribution). Deviations from a straight line in the plot can indicate skewness.4. Summary Statistics: If the mean is significantly different from the median, it suggests skewness. Remember, these methods provide indications of skewness, but they might not pinpoint the exact cause or degree of skewness in more complex distributions.

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4 How to transform data skewness?

Data skewness can be transformed in various ways, depending on the type and degree of skewness, and the goal of the transformation. Logarithmic transformation is often used as it applies the natural logarithm function to the data, reducing positive skewness. However, it cannot handle zero or negative values. Square root transformation is also popular, as it applies the square root function to the data to reduce positive skewness, and can handle zero values. Cube root transformation is useful for data that follows a skewed normal distribution, as it compresses both positive and negative skewness. Box-Cox transformation and Yeo-Johnson transformation are also viable options, applying a power function to minimize skewness and handle zero, positive, and negative values respectively. Parameter estimation is required for both of these methods, which can be done through maximum likelihood or cross-validation.

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SWAKSHAR MUKHERJEE

Facilities Security Management Managerial at Bengal Silicon Valley,linkedin & PM Institute, Inc. certified Six Sigma: Green & Black Belt, Data Scientist. Noida International University & IIRIS Certified Risk Leader.
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Transforming skewed data often involves applying mathematical functions to the data to make its distribution more symmetrical. Common transformations include: 1. Log Transformation: ? Useful for reducing right skewness. 2. Square Root Transformation: 3. Box-Cox Transformation: 4. Reciprocal Transformation:. 5. Exponential Transformation: The choice of transformation depends on the characteristics of your data. It’s important to note that these transformations might not be suitable for all types of data, and their effectiveness should be assessed based on the specific context and analysis requirements. After transformation, it’s advisable to check the skewness again to ensure it meets the desired characteristics.

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5 How to apply data skewness transformation in Python?

To apply data skewness transformation in Python, you can use libraries such as numpy, scipy, or sklearn. For example, to apply a logarithmic transformation, you can use the numpy.log function:

import numpy as np
data = np.array([1, 2, 4, 8, 16, 32, 64, 128, 256])
log_data = np.log(data)

To apply a Box-Cox transformation, you can use the scipy.stats.boxcox function:

import scipy.stats as stats
data = np.array([1, 2, 4, 8, 16, 32, 64, 128, 256])
boxcox_data, lambda_ = stats.boxcox(data)

To apply a Yeo-Johnson transformation, you can use the sklearn.preprocessing.PowerTransformer class:

from sklearn.preprocessing import PowerTransformer
data = np.array([1, 2, 4, 8, 16, 32, 64, 128, -256])
pt = PowerTransformer(method='yeo-johnson')
yeojohnson_data = pt.fit_transform(data.reshape(-1, 1))

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you handle data skewness in preprocessing?

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1 What is data skewness?

2 Why is data skewness a problem?

3 How to detect data skewness?

4 How to transform data skewness?

5 How to apply data skewness transformation in Python?

6 Here’s what else to consider

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How can you handle data skewness in preprocessing?

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1 What is data skewness?

2 Why is data skewness a problem?

3 How to detect data skewness?

4 How to transform data skewness?

5 How to apply data skewness transformation in Python?

6 Here’s what else to consider

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