Defining regression

In this chapter, you will discover regression. Regression is a supervised machine learning task in which a model is constructed that predicts or estimates a numerical target variable based on numerical or categorical independent variables.

The simplest type of regression model is linear regression. Let's consider a super simple example of how a linear regression could be used for regression.

Imagine that we have a dataset in which we have observations of 10 people. Based on the number of hours they study per week, we have to estimate their average grade (on a 1 to 10 scale). Of course, this is a strongly oversimplified problem.

The data looks as follows:

Code Block 7-1

import pandas as pd

nb_hrs_studies = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

avg_grade = [5.5, 5.8, 6.8, 7.2, 7.4, 7.8, 8.2, 8.8, 9.3, 9.4]

data = pd.DataFrame({'nb_hrs_studies': nb_hrs_studies, 'avg_grade': avg_grade})

data

You will obtain the following...