2. Data Exploration with Jupyter

Activity 2.01: Building a Third-Order Polynomial Model

Solution:

1. Load the necessary libraries and the dataset from scikit-learn, as follows:

   import pandas as pd
   import matplotlib.pyplot as plt
   import numpy as np
   from sklearn import datasets
   boston = datasets.load_boston()
   df = pd.DataFrame(data=boston['data'], \
                     columns=boston['feature_names'],)
   df['MEDV'] = boston['target']

2. First, we will pull out our dependent feature and target variable from df, as follows:

   y = df['MEDV'].values
   x = df['LSTAT'].values.reshape(-1,1)

   This is identical to what we did earlier for the linear model.

3. Verify what x looks like by executing the following code:

   x[:3]

   The output is as follows:

   array([[4.98],
          [9.14],
          ...