A custom implementation of a Naive Bayes Classifier written from scratch in Python 3.
From Wikipedia:
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.
| 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
Please run with Python 3 or greater.
python main
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