Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

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

How to do it…

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

    from sklearn import preprocessing
    from sklearn.naive_bayes import GaussianNB 
  2. Let's load the dataset:

    input_file = 'path/to/'
    # Reading the data
    X = []
    y = []
    count_lessthan50k = 0
    count_morethan50k = 0