Train/test distributions

In a machine learning interview, you will most certainly work with training data and test data. As discussed earlier, poor model performance can result if the distributions of training and test datasets differ.

In this exercise, you'll use functions from sklearn.model_selection as well as seaborn and matplotlib.pyplot to split loan_data into a training set and a test set, as well as visualize their distributions to spot any discrepancies.

Note that seaborn and matplotlib.pyplot have already been imported to your workspace and aliased as sns and plt, respectively.

The pipeline now includes Train/Test split:

Machine learning pipeline

This exercise is part of the course

Practicing Machine Learning Interview Questions in Python

View Course

Exercise instructions

Subset loan_data to only the Credit Score and Annual Income features, and the target variable Loan Status in that order.
Create an 80/20 split of loan_data and assign it to loan_data_subset.
Create pairplots of trainingSet and testSet (in that order) setting the hue argument to the target variable Loan Status.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create `loan_data` subset: loan_data_subset
loan_data_subset = ____[['____','____','____']]

# Create train and test sets
trainingSet, testSet = ____(____, ____=___, random_state=123)

# Examine pairplots
plt.figure()
sns.____(____, hue='____', palette='RdBu')
plt.show()

plt.figure()
sns.____(____, hue='____', palette='RdBu')
plt.show()

Edit and Run Code