NaivePy

This Module Is No Longer Maintained

v1.1 is the last release.

Naive Bayes :

About Naive Bayes :

Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.
It is mainly used in text classification that includes a high-dimensional training dataset.
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.
It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.

Formula Of Naive Bayes :

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.

Documentation:

Read the Docs Here

Installation :

To Install the module

pip install naivepy

About Module:

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