# NaivePy
</br>
# This Module Is No Longer Maintained
v1.1 is the last release.
# Naive Bayes :
## About Naive Bayes :
<p align="Justify">
Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.</br>
It is mainly used in text classification that includes a high-dimensional training dataset.</br>
Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.</br>
It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.</br>
Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.
</p>
## Formula Of Naive Bayes :
<p align="justify">
Bayes' theorem is also known as Bayes' Rule or Bayes' law, which is used to determine the probability of a hypothesis with prior knowledge. It depends on the conditional probability.
The formula for Bayes' theorem is given as:
Naïve Bayes Classifier Algorithm
Where,
**$P(A|B)$ = ${P(B|A)P(A)} \over P(B)$**
P(A|B) is Posterior probability: Probability of hypothesis A on the observed event B.
P(B|A) is Likelihood probability: Probability of the evidence given that the probability of a hypothesis is true.
P(A) is Prior Probability: Probability of hypothesis before observing the evidence.
P(B) is Marginal Probability: Probability of Evidence.
</p>
# Documentation:
Read the [Docs Here](https://naivepy.readthedocs.io/en/latest/#)
# Installation :
To Install the module
```
pip install naivepy
```
# About Module:
<p align="justify">
Naivepy module is built using python and pandas. It is and machine learning algorithm. This Module can take the target column and classifies it.
**Note** : The Target Column must have 2 Types of values other wise MaxTargetColumnException will be occured.
# Examples :
**Code** :
```
from naivepy import Naive
n=Naive(filename="data.csv",sample_list=["red","suv","domestic"],target_column="stolen")
print(n.getans)
print(n.getdata)
print(n.getlabel)
```
**Output** :
```
Color Type Origin Stolen
0 Red Sports Domestic Yes
1 Red Sports Domestic No
2 Red Sports Domestic Yes
3 Yellow Sports Domestic No
4 Yellow Sports Imported Yes
5 Yellow SUV Imported No
6 Yellow SUV Imported Yes
7 Yellow SUV Domestic No
8 Red SUV Imported No
9 Red Sports Imported Yes
No
0.072
```
# Author : Prathamesh Dhande
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