Python: Real World Machine Learning

Chapter 1. The Realm of Supervised Learning

In this chapter, we will cover the following recipes:

• Preprocessing data using different techniques
• Label encoding
• Building a linear regressor
• Computing regression accuracy
• Achieving model persistence
• Building a ridge regressor
• Building a polynomial regressor
• Estimating housing prices
• Computing the relative importance of features
• Estimating bicycle demand distribution

Introduction

If you are familiar with the basics of machine learning, you will certainly know what supervised learning is all about. To give you a quick refresher, supervised learning refers to building a machine learning model that is based on labeled samples. For example, if we build a system to estimate the price of a house based on various parameters, ...