https://github.com/gbroques/naive-bayes

A Python implementation of Naive Bayes from scratch.
https://github.com/gbroques/naive-bayes

classification data-mining data-mining-algorithms laplace-smoothing log-likelihood maximum-a-posteriori-estimation maximum-likelihood-estimation naive naive-algorithm naive-bayes naive-bayes-algorithm naive-bayes-classification naive-bayes-classifier naive-bayes-implementation naive-bayes-tutorial naivebayes python python3

Last synced: about 2 months ago
JSON representation

A Python implementation of Naive Bayes from scratch.

• Host: GitHub
• URL: https://github.com/gbroques/naive-bayes
• Owner: gbroques
• License: mit
• Created: 2018-02-01T13:54:57.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-03-27T14:56:31.000Z (over 7 years ago)
• Last Synced: 2023-03-01T09:56:27.077Z (over 2 years ago)
• Topics: classification, data-mining, data-mining-algorithms, laplace-smoothing, log-likelihood, maximum-a-posteriori-estimation, maximum-likelihood-estimation, naive, naive-algorithm, naive-bayes, naive-bayes-algorithm, naive-bayes-classification, naive-bayes-classifier, naive-bayes-implementation, naive-bayes-tutorial, naivebayes, python, python3
• Language: Python
• Homepage:
• Size: 61.5 KB
• Stars: 31
• Watchers: 3
• Forks: 27
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Naive Bayes from Scratch in Python

[![Build Status](https://travis-ci.org/gbroques/naive-bayes.svg?branch=master)](https://travis-ci.org/gbroques/naive-bayes)

A custom implementation of a Naive Bayes Classifier written from scratch in Python 3.

[![Bayes Theorem](bayes-theorem.png)](http://www.saedsayad.com/naive_bayesian.htm)

From [Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier):

> In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

## Dataset

#### Loan Defaulters

| Home Owner | Marital Status | Annual Income | Defaulted Borrower |

| ---------- | -------------- | ------------- | ------------------ |

| Yes        | Single         | $125,000      | No                 |

| No         | Married        | $100,000      | No                 |

| No         | Single         | $70,000       | No                 |

| Yes        | Married        | $120,000      | No                 |

| No         | Divorced       | $95,000       | Yes                |

| No         | Married        | $60,000       | No                 |

| Yes        | Divorced       | $220,000      | No                 |

| No         | Single         | $85,000       | Yes                |

| No         | Married        | $75,000       | No                 |

| No         | Single         | $90,000       | Yes                |

**Source:** *Introduction to Data Mining* (1st Edition) by Pang-Ning Tan

Figure 5.9, Page 230

## How to Run

Please run with Python 3 or greater.

`python main`