why do we need to change distribution of the dataset into normal distribution and how??
Thank you for your post.
This is a topic that I haven’t worked on recently and I need to review it to improve myself on it, but I have some points that I think are important on this subject
Transforming the distribution of a dataset into a normal distribution is a common data preprocessing technique used in statistics and machine learning. The need to transform data into a normal distribution arises from the assumption of normality in many statistical tests and modeling techniques. Normal distribution (also known as Gaussian distribution) has some desirable properties that make certain analyses and modeling approaches more reliable and effective.
Here are some reasons why transforming data into a normal distribution can be beneficial:

Parametric Methods: Many statistical tests and modeling techniques, such as ttests, ANOVA, linear regression, etc., assume that the data follows a normal distribution. If the data deviates significantly from normality, the results of these tests may be biased or less accurate.

Interpretability: Normal distribution is well understood and widely used in statistics. When data is normally distributed, it becomes easier to interpret the results and draw meaningful conclusions from statistical analyses.

Central Limit Theorem: The central limit theorem states that the distribution of the sum (or average) of a large number of independent and identically distributed random variables will be approximately normally distributed, regardless of the original distribution. This theorem is the basis for many inferential statistical methods.

Stability of Variance: In some statistical models, such as linear regression, the assumption of homoscedasticity (constant variance) is important. Transforming the data can help stabilize the variance, making the model assumptions more valid.
About how to transform data into a normal distribution. There are several methods to achieve this, and the choice of method depends on the characteristics of the data and the specific context of the analysis. Here are some common techniques:

Log Transformation: Applying a logarithmic function to the data can help reduce the impact of extreme values and compress the range, making the distribution more bellshaped.

Square Root Transformation: Taking the square root of the data values can also reduce the impact of extreme values and make the distribution closer to normal.

BoxCox Transformation: The BoxCox transformation is a family of power transformations that can be used to stabilize variance and make the data more normal.
Now, the zscore suppose to be a normal distribution technique. Although the zscore standardizes the data so that it has a mean of 0 and a standard deviation of 1 it does not normalize the data distribution as many people think they do. zscore is used to standardize data that are very different from each other in terms of their unit of measurement. It is a good technique for clustering analysis for example. That assure that the data follows a common unit value to prevent that extreme values influence the clustering formation. But do a Exploratory Data Analysis is widely recommend to guarantee that there is no outliers in your data that can lead your clustering to a bias formation.
It’s important to note that while transforming data into a normal distribution can be useful in certain situations, it is not always necessary or appropriate. Before applying any data transformation, it’s crucial to understand the characteristics of the data, the assumptions of the analysis or modeling techniques being used, and the potential implications of the transformation on the interpretation of the results. Additionally, some machine learning algorithms, like decision trees and random forests, are relatively robust to nonnormality and may not require data transformation.
I hope this help.
Best regards
I assume you are asking in the context of anomaly detection.
Typically, anomaly detection systems that rely on the normal distribution perform much better when the data follows the same pattern. The reason is that the majority of the data clusters around the mean, and outliers appear at the edges/tails.
As a result, this allows the algorithm to easily identify outliers as anomalies using a threshold value.