Page Comparison

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1. Log into the ML UI if you are not already logged in. 
2. Click the You have X projects link as shown below.
   
3. Click on the Breast_Cancer_data_analytics_project project to expand it.
4. Enter breast_cancer_analysis_1 as the analysis name and click CREATE ANALYSIS. The following page will appear displaying the summary statistics.
    
5. Click Next without making any changes to the summary statistics.
   
   The Explore view will open. You will notice that Parallel Sets and Trellis Chart visualisations are enabled, and Scatter Plot and Cluster Diagram visualisations are disabled. This is determined by the feature types of the dataset. Select and clear the checkboxes for categorical features as follows.
    
   
   

6. Click Next. The Algorithms view will be displayed. Enter values as shown below.
   

   Parameter	Value
   Algorithm name	LOGISTIC REGRESSION L_BFGS
   Response variable	Class
   Train data fraction	0.7

7. Click Next. The Parameters view will appear.Enter L2 as the reg type.
    
8. Click Next. The Model view will appear. Select Breast_Cancer_Dataset-1.0.0 as the dataset version.
    
9. Click Run. RUN to perform the analysis.
   Image AddedThe analysis will be created and displayed for the project as shown below.
   

Step 4: Predict

1. Log into the ML UI if you are not already logged in. 
2. Click the You have X projects link as shown below to open the Projects window.
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3. Click MODELS for the breast_cancer_analysis_1  analysis.
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4. Click Predict on the model displayed.
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5. Enter values in the Predict page as shown below.
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   Parameter Name	Value
   Prediction Source	Feature values
   SampleCodeNumber	1018561
   ClumpThickness	2
   UniformityOfCellSize	1
   UniformityOfCellShape	1
   MarginalAdhesion	1
   SingleEpithelialCellSize	2
   BareNuclei	1
   BlandChromati	1
   NormalNucleoli	1
   Mitoses	5

6. Click Predict. The prediction will be displayed as follows.
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