The first concept that comes to mind of any data science professional when solving any regression problem is to construct a linear regression model. Linear regression is one of the oldest algorithms, but it's still very efficient. We will build a linear regression model in Python using a sample dataset. This dataset is available in scikit-learn as a sample dataset called the Boston housing prices dataset. We will use the sklearn library to load the dataset and build the actual model. Let's start by loading and understanding the data:
- Let's begin by importing all the necessary libraries and creating our dataframe:
# Importing the necessary librariesimport pandas as pdimport numpy as npimport matplotlib.pyplot ...
