The Naive Bayes Algorithm is based on the Bayes Theoram. Bayes’ theoram describes the probablitiy of an event, based on prior knowledge of conditions that might be related to the event.

 Data is usually distributed in different ways with a bias to the left or to the right
or it can all be jumbled up.
However, there are chances that data is distributed around a central value
without any bias to the left or right and reaches normal distribution in the form
of a bell-shaped curve.
The random variables are distributed in the form of a symmetrical bell-shaped
curve.
Properties of Nornal Distribution:
1. Unimodal -one mode
2. Symmetrical -left and right halves are mirror images
3. Bell-shaped -maximum height (mode) at the mean
4. Mean, Mode, and Median are all located in the center
5. Asymptotic



 

It is a statistical hypothesis testing for a randomized experiment with two variables A and B.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads
An example of this could be identifying the click-through rate for a banner ad.

In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.
In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.