6. Ensemble Modeling

Activity 6.01: 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:

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

   Note

   The preceding code snippet assumes that the dataset is presented in the same folder as that of the exercise notebook. However, if your dataset is present in the Datasets folder, you need to use the following code: data = pd.read_csv('../Datasets/boston_house_prices.csv')

   You will...