Python: Real World Machine Learning

Estimating the income bracket

We will build a classifier to estimate the income bracket of a person based on 14 attributes. The possible output classes are higher than 50K or lower than or equal to 50K. There is a slight twist in this dataset in the sense that each datapoint is a mixture of numbers and strings. Numerical data is valuable, and we cannot use a label encoder in these situations. We need to design a system that can deal with numerical and non-numerical data at the same time. We will use the census income dataset available at https://archive.ics.uci.edu/ml/datasets/Census+Income.

How to do it…

1. We will use the income.py file already provided to you as a reference. We will use a Naive Bayes classifier to achieve this. Let's import a couple ...