Getting ready

We will use the os package in the operating system's dependent functionality, and the pandas package for data manipulation. 

Let's now take a look at the data definitions to understand our variables. In the following code, we list the data definition for a few variables. The dataset and the complete data definitions are available on GitHub. Here is an abridged version of the data description file:

MS SubClass (Nominal): Identifies the type of dwelling involved in the sale
Lot Frontage (Continuous): Linear feet of street connected to property
Alley (Nominal): Type of alley access to property
Overall Qual (Ordinal): Rates the overall material and finish of the house
Overall Cond (Ordinal): Rates the overall condition of the house
Year Built (Discrete): Original construction date
Mas Vnr Type (Nominal): Masonry veneer type
Mas Vnr Area (Continuous): Masonry veneer area in square feet
Garage Type (Nominal): Garage location
Garage Yr Blt (Discrete): Year garage was built
Garage Finish (Ordinal): Interior finish of the garage
Garage Cars (Discrete): Size of garage in car capacity
Garage Area (Continuous): Size of garage in square feet
Garage Qual (Ordinal): Garage quality
Garage Cond (Ordinal): Garage condition
...
...
SalePrice (Continuous): Sale price $$

We will then import the os and pandas packages and set our working directory according to our requirements, as seen in the following code block:

import os
import pandas as pd

# Set working directory as per your need
os.chdir(".../.../Chapter 1")
os.getcwd()

The next step is to download the dataset from GitHub and copy it to your working directory.