Hi @Umairnsr87, apologies for the late reply.
First I’ll try to explain what a normal distribution is and why it is important.
A normal distribution of a variable takes the shape of a bell curve where most values tend towards the center of the distribution and the distribution’s mean, median and mode are equal.
This is important because a lot of machine learning algorithms assumes that data a normal distribution before fitting the data. This is also supported by the Central Limit Theorem which states that a sample taken from the distribution will follow a normal distribution and the mean will be equal to the original distribution.
In order to determine if your distribution is normal, you can apply statistical methods such as plotting the probability density function of a variable and calculating its skewness.
A normal distribution is symmetric on either side of its mean/center. To best describe symmetry, skewness is used a metric. The distribution can be left skewed or right skewed and a normal distribution has a skewness of 0. Negative values indicate data that is left skewed and positive values indicate data that are right skewed.
Depending on the skewness and type of data, you can apply the various types of normalization techniques. This will help in transforming your data into a normal distribution and ensure your algorithm fits data without variation.
You do not need to normalize your data if it already fits a normal distribution.