This tutorial creates two machine learning models with IBM Watson Studio. The first is created in the UI and the second in Jupyter Notebook using scikit-learn. We will save and deploy bother models on IBM Cloud.

1. Introduction to MachineIntroduction to Machine
Learning on IBM WatsonLearning on IBM Watson
StudioStudio
Upkar Lidder
Developer Advocate, IBM
> ulidder@us.ibm.com
> @lidderupk
> upkar.dev http://bit.ly/waston-ml-sign

2. PrerequisitesPrerequisites
@lidderupkIBM Developer
1. Create IBM Cloud Account using THIS URL
3. If you already have an account, use the above URL to sign into your
IBM Cloud account.
2. Check your email and activate your account. Once activated, log back
into your IBM Cloud account using the link above.
http://bit.ly/waston-ml-sign

3. Watson Studio & Watson Machine LearningWatson Studio & Watson Machine Learning
@lidderupkIBM Developer

4. Watson StudioWatson Studio

5. IBM Watson Studio IBM Watson Studio
@lidderupkIBM Developer

6. IBM Watson Studio IBM Watson Studio
@lidderupkIBM Developer

7. Workshop -Workshop - GoalsGoals
@lidderupkIBM Developer
Successfully Create, Store and
Deploy a Linear Regression Model
on IBM Cloud using Watson Studio
and Watson Machine Learning
Services.

8. Question -Question - predict median house price for Boston areapredict median house price for Boston area
@lidderupkIBM Developer

9. Linear regressionLinear regression - Simple- Simple
@lidderupkIBM Developer
Median
HousePrice
Another Variable
Y = ⍺ + βx

10. StepsSteps
@lidderupkIBM Developer
1. Sign up / Log into IBM Cloud - http://bit.ly/waston-ml-sign
2. Create Watson Studio Service.
3. Sign into Watson Studio and create a new Data Science
Project. It also creates a Cloud Object Store for you.
4. Associate a Machine Learning Service with your project.
5. Upload csv data to your project.
6. Add a new Machine Learning Model to your project.
7. Create a Linear Regression Model and save it to IBM Cloud.
8. Create a new deployment on IBM Cloud.
9. Test your model !

11. Step 1 -Step 1 - sign up/ log into IBM Cloudsign up/ log into IBM Cloud
@lidderupkIBM Developer
http://bit.ly/waston-ml-sign

12. Step 2 -Step 2 - locate Watson Studio in Cataloglocate Watson Studio in Catalog
@lidderupkIBM Developer

13. Step 3 -Step 3 - create Watson Studio instancecreate Watson Studio instance
@lidderupkIBM Developer

14. Step 4 -Step 4 - launch Watson Studiolaunch Watson Studio
@lidderupkIBM Developer

15. Step 5 -Step 5 - create a new projectcreate a new project
@lidderupkIBM Developer

16. Step 6 -Step 6 - pick Data Science starterpick Data Science starter
@lidderupkIBM Developer

17. Step 7 -Step 7 - give the project a name and assign COSgive the project a name and assign COS
@lidderupkIBM Developer

18. Step 8 -Step 8 - open asset tabopen asset tab
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19. Step 9 -Step 9 - drag and drop data file into Load Assetsdrag and drop data file into Load Assets
@lidderupkIBM Developer
http://bit.ly/boston-house-csv

20. Step 10 -Step 10 - add Machine Learning model to the projectadd Machine Learning model to the project
@lidderupkIBM Developer

21. Step 11 - associate a ML instanceStep 11 - associate a ML instance
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22. Step 12a -Step 12a - create ML instance if you don't have onecreate ML instance if you don't have one
@lidderupkIBM Developer

23. Step 12b -Step 12b - create a new lite ML instancecreate a new lite ML instance
@lidderupkIBM Developer

24. @lidderupkIBM Developer
Step 12c -Step 12c - create a new lite ML instancecreate a new lite ML instance

25. Step 12d -Step 12d - reload the ML service sectionreload the ML service section
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26. Step 13 -Step 13 - pick new service and create a model manuallypick new service and create a model manually
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27. Step 14 -Step 14 - pick the Boston data file aspick the Boston data file as data assetdata asset
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28. @lidderupkIBM Developer
Step 15 -Step 15 - pick target, input features and model typepick target, input features and model type

29. @lidderupkIBM Developer
Step 16 -Step 16 - add estimator(s)add estimator(s)

30. @lidderupkIBM Developer
Step 17 -Step 17 - change hyperparameters (optional)change hyperparameters (optional)

31. @lidderupkIBM Developer
Step 18 -Step 18 - save/store the trained model on IBM Cloudsave/store the trained model on IBM Cloud

32. @lidderupkIBM Developer
Step 19 -Step 19 - add a new deploymentadd a new deployment

33. @lidderupkIBM Developer
1
2
Step 20 -Step 20 - ensure that deployment is successfulensure that deployment is successful

34. @lidderupkIBM Developer
Step 21a -Step 21a - implementation / test the deployed modelimplementation / test the deployed model

35. @lidderupkIBM Developer
{
"fields": ["CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "B", "LSTAT"],
"values": [
[0.00632, 18, 2.31, 0, 0.538, 6.575, 66, 4.09, 1, 296, 15.3, 396.9, 4.99]
]
}
1
2
3
4
5
6
Step 21b -Step 21b - implementation / test the deployed modelimplementation / test the deployed model

36. @lidderupkIBM Developer
Watson Studio - Binary ClassificationWatson Studio - Binary Classification

37. @lidderupkIBM Developer
Watson Studio - Multiclass ClassificationWatson Studio - Multiclass Classification

38. @lidderupkIBM Developer
Watson Studio - RegressionWatson Studio - Regression

39. Watson Machine LearningWatson Machine Learning

40. WML -WML - Supported Frameworks as of 06.21.19Supported Frameworks as of 06.21.19
@lidderupkIBM Developer

41. IBM Watson Machine Learning IBM Watson Machine Learning
@lidderupkIBM Developer

42. IBM Watson Machine Learning Client IBM Watson Machine Learning Client
@lidderupkIBM Developer http://wml-api-pyclient.mybluemix.net/index.html

43. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
Output

44. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
7
8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
Output

45. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
7
8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Create a new Linear Regression Model
LR_model = LinearRegression()
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
10
11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
Output

46. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
7
8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Create a new Linear Regression Model
LR_model = LinearRegression()
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
10
11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Train the model
LR_model.fit(X_train, y_train)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
13
14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
Output

47. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
7
8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Create a new Linear Regression Model
LR_model = LinearRegression()
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
10
11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Train the model
LR_model.fit(X_train, y_train)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
13
14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# store actual and predited data to draw chart
predicted = LR_model.predict(X_test)
actual = y_test
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
16
17
18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
Output

48. WML -WML - create scikit-learn linear regression modelcreate scikit-learn linear regression model
@lidderupkIBM Developer
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
1
2
3
4
5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
7
8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Create a new Linear Regression Model
LR_model = LinearRegression()
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
10
11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# Train the model
LR_model.fit(X_train, y_train)
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
13
14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# store actual and predited data to draw chart
predicted = LR_model.predict(X_test)
actual = y_test
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
16
17
18
19
20
# The coefficients21
print('Coefficients: n', LR_model.coef_)22
# The mean squared error23
print("Mean squared error: %.2f"24
% mean_squared_error(actual, predicted))25
# Explained variance score: 1 is perfect prediction26
print('Variance score: %.2f' % r2_score(actual, predicted))27
# The coefficients
print('Coefficients: n', LR_model.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% mean_squared_error(actual, predicted))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % r2_score(actual, predicted))
from sklearn.linear_model import LinearRegression1
from sklearn.datasets import load_boston2
import matplotlib.pyplot as plt3
from sklearn.model_selection import train_test_split4
from sklearn.metrics import mean_squared_error, r2_score5
6
boston = load_boston()7
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)8
9
# Create a new Linear Regression Model10
LR_model = LinearRegression()11
12
# Train the model13
LR_model.fit(X_train, y_train)14
15
# store actual and predited data to draw chart16
predicted = LR_model.predict(X_test)17
actual = y_test18
19
20
21
22
23
24
25
26
27
Output

49. WML -WML - evaluation metricsevaluation metrics
@lidderupkIBM Developer

50. WML -WML - get Machine Learning service credentialsget Machine Learning service credentials
@lidderupkIBM Developer

51. WML -WML - save scikit-learn linear regression modelsave scikit-learn linear regression model
@lidderupkIBM Developer
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15

52. WML -WML - save scikit-learn linear regression modelsave scikit-learn linear regression model
@lidderupkIBM Developer
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard
wml_credentials = {
}
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
4
5
6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15

53. WML -WML - save scikit-learn linear regression modelsave scikit-learn linear regression model
@lidderupkIBM Developer
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard
wml_credentials = {
}
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
4
5
6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# Instantiate WatsonMachineLearningAPIClient
from watson_machine_learning_client import WatsonMachineLearningAPIClient
client = WatsonMachineLearningAPIClient( wml_credentials )
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
8
9
10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15

54. WML -WML - save scikit-learn linear regression modelsave scikit-learn linear regression model
@lidderupkIBM Developer
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard
wml_credentials = {
}
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
4
5
6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# Instantiate WatsonMachineLearningAPIClient
from watson_machine_learning_client import WatsonMachineLearningAPIClient
client = WatsonMachineLearningAPIClient( wml_credentials )
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
8
9
10
11
# store the model12
published_model = client.repository.store_model(model=LR_model,13
meta_props={'name':'upkar-housing-linear-reg'},14
training_data=X_train, training_target=y_train)15
# store the model
published_model = client.repository.store_model(model=LR_model,
meta_props={'name':'upkar-housing-linear-reg'},
training_data=X_train, training_target=y_train)
# we will use WML to work with IBM Machine Learning Service1
from watson_machine_learning_client import WatsonMachineLearningAPIClient2
3
# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard4
wml_credentials = {5
}6
7
# Instantiate WatsonMachineLearningAPIClient8
from watson_machine_learning_client import WatsonMachineLearningAPIClient9
client = WatsonMachineLearningAPIClient( wml_credentials )10
11
12
13
14
15

55. WML -WML - deploy scikit-learn linear regression modeldeploy scikit-learn linear regression model
@lidderupkIBM Developer
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16

56. WML -WML - deploy scikit-learn linear regression modeldeploy scikit-learn linear regression model
@lidderupkIBM Developer
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
# grab the model from IBM Cloud
published_model_uid = client.repository.get_model_uid(published_model)
# create a new deployment for the model
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")
import json1
2
3
4
5
6
7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16

57. WML -WML - deploy scikit-learn linear regression modeldeploy scikit-learn linear regression model
@lidderupkIBM Developer
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
# grab the model from IBM Cloud
published_model_uid = client.repository.get_model_uid(published_model)
# create a new deployment for the model
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")
import json1
2
3
4
5
6
7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
#get the scoring endpoint
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)
print(scoring_endpoint)
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
9
10
11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16

58. WML -WML - deploy scikit-learn linear regression modeldeploy scikit-learn linear regression model
@lidderupkIBM Developer
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
# grab the model from IBM Cloud
published_model_uid = client.repository.get_model_uid(published_model)
# create a new deployment for the model
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")
import json1
2
3
4
5
6
7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
#get the scoring endpoint
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)
print(scoring_endpoint)
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
9
10
11
12
#use the scoring endpoint to predict house median price some test data13
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}14
predictions = client.deployments.score(scoring_endpoint, scoring_payload)15
print(json.dumps(predictions, indent=2))16
#use the scoring endpoint to predict house median price some test data
scoring_payload = {"values": [list(X_test[0]), list(X_test[1])]}
predictions = client.deployments.score(scoring_endpoint, scoring_payload)
print(json.dumps(predictions, indent=2))
import json1
2
# grab the model from IBM Cloud3
published_model_uid = client.repository.get_model_uid(published_model)4
5
# create a new deployment for the model6
model_deployed = client.deployments.create(published_model_uid, "Deployment of scikit model")7
8
#get the scoring endpoint9
scoring_endpoint = client.deployments.get_scoring_url(model_deployed)10
print(scoring_endpoint)11
12
13
14
15
16

59. WML -WML - deploy scikit-learn linear regression modeldeploy scikit-learn linear regression model
@lidderupkIBM Developer

60. WML -WML - try it out on your own !try it out on your own !
@lidderupkIBM Developer
http://bit.ly/waston-ml-sign

61. @lidderupkIBM Developer
WML -WML - create a new notebook from URLcreate a new notebook from URL
Grab the FULL URL from : http://bit.ly/boston-house-notebook

62. Thank youThank you
Let's chat !Let's chat !
@lidderupkIBM Developer
Upkar Lidder, IBM
@lidderupk
https://github.com/lidderupk/
ulidder@us.ibm.com