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Author: Aaron England

Chapter 2/Exercises/Exercise_04.py

@@ -1,6 +1,6 @@
 # Exercise 4: Fitting a multiple linear regression model and determining the intercept and coefficient
 
-# continuing from Exercise 1:
+# continuing from Exercise 3:
 
 # instantiate linear regression model
 from sklearn.linear_model import LinearRegression

Chapter 2/Exercises/Exercise_05.py

@@ -1,5 +1,7 @@
 # Exercise 5: Fitting a logistic regression model and determining the intercept and coefficient
 
+# clear environment prior to running this code
+
 # import data
 import pandas as pd
 df = pd.read_csv('weather.csv')

Chapter 2/Exercises/Exercise_08.py

@@ -1,5 +1,7 @@
 # Exercise 8: Preparing data for support vector classifier
 
+# clear environment prior to running this code
+
 # import data
 import pandas as pd
 df = pd.read_csv('weather.csv')

Chapter 2/Exercises/Exercise_10.py

@@ -1,5 +1,7 @@
 # Exercise 10: Tuning decision tree classifier using grid search in pipeline
 
+# continuing from Activity 4:
+
 # Set up the steps for a pipeline
 from sklearn.preprocessing import StandardScaler
 from sklearn.tree import DecisionTreeClassifier

Chapter 2/Exercises/Exercise_11.py

@@ -36,3 +36,8 @@
 plt.legend(loc=4)
 plt.show()
 
+
+
+
+
+

Chapter 2/Exercises/Exercise_12.py

@@ -1,5 +1,7 @@
 # Exercise 12: Preparing data for random forest regressor pipeline
 
+# clear environment prior to running this code
+
 # import data
 import pandas as pd
 df = pd.read_csv('weather.csv')

Chapter 2/Exercises/Exercise_13.py

@@ -1,5 +1,7 @@
 # Exercise 13: Programmatically extracting tuned hyperparameters and determining feature importance from random forest regressor grid search model
 
+# continuing from Activity 6
+
 # instantiate model
 from sklearn.ensemble import RandomForestRegressor
 model = RandomForestRegressor(criterion=best_parameters['Forest__criterion'],