Mean encoding

Mean encoding transforms categorical variables into numerical values by replacing each category with the mean of the target variable for that category. This technique establishes a direct relationship between the encoded variable and the target, making it particularly useful in linear models. Let’s implement it:

1. First, import pandas and train_test_split from scikit-learn to prepare for applying the encoding. Import matplotlib.pyplot, which you will use to make graphs showing the relationship between the encoded categorical variables and the target variable:

   import pandas as pd
   from sklearn.model_selection import train_test_split
   import matplotlib.pyplot as plt

2. Next, set up the sample data and split it into train and test datasets using train_test_split. This sample dataset has two categorical columns, Neighborhood and Exterior1st, and a target column, SalePrice:

   # Sample data
   data = {
       'Neighborhood': ['OldTown&apos...