#_____WHAT IS NAIVE BAYES ALGORITHM?________
It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
Equation which is derived from Bayes Theroem is:
P(A/B) = (P(B/A).P(A))/P(B)
P(A/B) is the posterior probability of class. P(B) is the prior probability of class. P(B/A) is the Observation Density. P(A) is the prior probability of predictor.
#___Principal Component Analysis__
PCA:- Principal component analysis (PCA) is a technique used for identification of a smaller number of uncorrelated variables known as principal components from a larger set of data. The technique is widely used to emphasize variation and capture strong patterns in a data set.
In this project PCA is also used to improve the performance of Naive Bayes for bring out only that features which have important effect in the prediction of testing values