I got error at while I am using watson machine learning client in IBM cloud deplyement

  1  import numpy as np
  2  import matplotlib.pyplot as plt
  3  import pandas as pd
  4  
  5  #Insert your dataset here
  6  
  7  dataset = pd.read_csv('https://github.com/gdabhishek/WML-Deploy/blob/master/Social_Network_Ads.csv')
  8  
  9  User ID	Gender	Age	EstimatedSalary	Purchased
 10  0	15624510	Male	19	19000	0
 11  1	15810944	Male	35	20000	0
 12  2	15668575	Female	26	43000	0
 13  3	15603246	Female	27	57000	0
 14  4	15804002	Male	19	76000	0
 15  
 16  
 17  
 18  #Check Missing Values
 19  dataset.isnull().any()
 20  
 21  #Spilt Dependent and Independent Variables
 22  
 23  X = dataset.iloc[:, [2, 3]].values
 24  y = dataset.iloc[:, 4].values
 25  
 26  # Splitting the dataset into the Training set and Test set
 27  
 28  from sklearn.model_selection import train_test_split
 29  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.25, random_state = 0)
 30  
 31  # Fitting Decision Tree Classification to the Training set
 32  
 33  from sklearn.tree import DecisionTreeClassifier
 34  classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
 35  classifier.fit(X_train, y_train)
 36  
 37  # Predicting the Test set results
 38  y_pred = classifier.predict(X_test)
 39  
 40  #Finding the accuracy score
 41  
 42  from sklearn.metrics import accuracy_score
 43  print("Accuracy Score: ",accuracy_score(y_test,y_pred)*100,"%")
 44  
 45  from watson_machine_learning_client import WatsonMachineLearningAPIClient
 46  
 47  #
 48  ### here i got error
 49  ##
 50  
 51  wml={
 52      
 53  }
 54  
 55  client = WatsonMachineLearningAPIClient(wml)
 56  
 57  model_props = {client.repository.ModelMetaNames.AUTHOR_NAME: "", 
 58                 client.repository.ModelMetaNames.AUTHOR_EMAIL: "", 
 59                 client.repository.ModelMetaNames.NAME: "MyModel"}
 60  
 61  model_artifact =client.repository.store_model(classifier, meta_props=model_props)
 62  
 63  client.repository.list()
 64  
 65  published_model_uid = client.repository.get_model_uid(model_artifact)
 66  created_deployment = client.deployments.create(published_model_uid, name="MyDeployment")
 67  
 68  
 69  scoring_endpoint = client.deployments.get_scoring_url(created_deployment)
 70  
 71  
 72  print(scoring_endpoint)
 73  
 74  scoring_payload = {"fields": ["Age","Salary"],"values": [[25,50000]]}
 75  predictions = client.deployments.score(scoring_endpoint, scoring_payload)
 76  print(predictions)
 77  
 78  client.deployments.list("de8eebf1-7c57-429d-9831-21c5fd4912a3")