Chapter 5: Ensemble Modeling

Activity 14: Stacking with Standalone and Ensemble Algorithms

Solution

1. Import the relevant libraries:

   import pandas as pd
   import numpy as np
   import seaborn as sns
   
   %matplotlib inline
   import matplotlib.pyplot as plt
   
   from sklearn.model_selection import train_test_split
   from sklearn.metrics import mean_absolute_error
   from sklearn.model_selection import KFold
   
   from sklearn.linear_model import LinearRegression
   from sklearn.tree import DecisionTreeRegressor
   from sklearn.neighbors import KNeighborsRegressor
   from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor

2. Read the data and print the first five rows:

   data = pd.read_csv('house_prices.csv')
   data.head()

   The output will be as follows:

   

   Figure 5.19: The first 5 rows

3. Preprocess the dataset to remove null values and one-hot encode categorical variables to prepare the data for modeling.

   First, we remove all columns where more than 10% of the values are null. To do this, calculate the fraction of missing values...