We will be using the Seaborn library, that helps in visualizing data.When regression models are being built, multicollinearity is checked for. This is because we need to understand the correlation present between all different combinations of continuous variables. If multicollinearity exists between the variables, we have to make sure that it is removed from the data. The data in real world is usually non-linear.
We need to find mechanisms to fit such non-linear data to the model. We will be using Anscombe’s dataset to visualize this data. The ‘implot’ function is used with this non-linear data.
Here’s the example −
import pandas as pd import seaborn as sb from matplotlib import pyplot as plt my_df = sb.load_dataset('anscombe') sb.lmplot(x="x", y="y", data=my_df.query("dataset == 'I'")) plt.show()