In Chapter 1, Get Closer to your Data, we manipulated and prepared the data from the HousePrices.csv file and dealt with the missing values. In this example, we're going to use the final dataset to demonstrate these sampling and resampling techniques.

You can get the prepared dataset from the GitHub.

We'll import the required libraries. We'll read the data and take a look at the dimensions of our dataset:

# import os for operating system dependent functionalities
import os

# import other required libraries
import pandas as pd
from sklearn.model_selection import train_test_split

# Set your working directory according to your requirement
os.chdir(".../Chapter 3/Resampling Methods")
os.getcwd()

Let's read our data. We'll prefix the DataFrame name with df_ to make it easier to understand:

df_housingdata = pd.read_csv("Final_HousePrices.csv")

In the next section, we'll look at how to use train_test_split() from sklean.model_selection to split our data into random training and testing subsets.