Introduction to Machine Learning on IBM Cloud

Upkar Lidder

http://bit.ly/ibm-cloud-summit-2019

IBM Developer

http://bit.ly/upkar-autoai

> ulidder@us.ibm.com
> @lidderupk
> upkar.dev

Introduction to Machine Learning on IBM Cloud Upkar Lidder http://bit.ly/ibm-cloud-summit-2019 IBM Developer http://bit.ly/upkar-autoai > ulidder@us.ibm.com > @lidderupk > upkar.dev

AI / ML on IBM Cloud

By Upkar Lidder

AI / ML on IBM Cloud

5 years ago
1,092

Upkar Lidder

Upkar Lidder is a Full Stack Developer and Data Wrangler with a decade of development experience in a variety of roles. He can be seen speaking at various conferences and participating in local tech groups and meetups.

upkarlidder.com
lidderupk

	{"input_data":[{
	"fields": ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT"],
	"values": [[0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98]]
	}]}

	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

	boston = load_boston()
	X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)

	# Create a new Linear Regression Model
	LR_model = LinearRegression()

	# Train the model
	LR_model.fit(X_train, y_train)

	# store actual and predited data to draw chart
	predicted = LR_model.predict(X_test)
	actual = y_test


	# 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 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

	boston = load_boston()
	X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)

	# Create a new Linear Regression Model
	LR_model = LinearRegression()

	# Train the model
	LR_model.fit(X_train, y_train)

	# store actual and predited data to draw chart
	predicted = LR_model.predict(X_test)
	actual = y_test


	# 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 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

	boston = load_boston()
	X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)

	# Create a new Linear Regression Model
	LR_model = LinearRegression()

	# Train the model
	LR_model.fit(X_train, y_train)

	# store actual and predited data to draw chart
	predicted = LR_model.predict(X_test)
	actual = y_test


	# 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 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

	boston = load_boston()
	X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target)

	# Create a new Linear Regression Model
	LR_model = LinearRegression()

	# Train the model
	LR_model.fit(X_train, y_train)

	# store actual and predited data to draw chart
	predicted = LR_model.predict(X_test)
	actual = y_test


	# 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))

	# we will use WML to work with IBM Machine Learning Service
	from watson_machine_learning_client import WatsonMachineLearningAPIClient

	# Grab your credentials from the Watson Service section in Watson Studio or IBM Cloud Dashboard
	wml_credentials = {
	}

	# Instantiate WatsonMachineLearningAPIClient
	from watson_machine_learning_client import WatsonMachineLearningAPIClient
	client = WatsonMachineLearningAPIClient( wml_credentials )

	# 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)

	import json

	# 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")

	#get the scoring endpoint
	scoring_endpoint = client.deployments.get_scoring_url(model_deployed)
	print(scoring_endpoint)

	#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))

Introduction to Machine Learning on IBM Cloud

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