Predict lung cancer malignancy, using Azure AI ML studio, using Deep learning

Click on Create to proceed:

Fill out the screen as follows; do select Type as Tabular:

Select Source as web files in the next screen:

This will take you to the following screen:

I took the data from the following Github URL repo:

https://raw.githubusercontent.com/rohitinu6/Lung_Cancer_Prediction_Using_Machine_Learning/refs/heads/main/Dataset/survey%20lung%20cancer.csv

Hence I am pasting the same out here:

Click on next; in the next screen it will give you the columns involved. You can include/exclude any cloumn as per you need, however please do not change anything, as each of these components are pretty useful in prediciting maliganancy:

And in the next step, click on Create to create the data step:

 Once the data gets created, you would come back to the screen, from where you started it all:

Select the dataset you just create and Click on Next. 
And in the following screen, we are selecting the column we need for prediction:

Also do enable Deep learning. For additional setitngs, click on Addtional configuration settings: and select the algorithms which you do not want to get executed here:


I have exluded them purposely, as they are not really related to classification algorithms. excluding them, would just save some cost.
Click on Save.
Click on Validation Type as: K-Cross validation, and test data as 30% of data, as a result 70% of the data would be kept for training and 30% as testing:



Click Next to continue. Here you can select Computer cluster (they are cheaper and quite cost saving: able to scale down when not in need). I created one before hand and am using the same here:
 
Once done, now you can put it for training job:



 Here you need to wait, till the time the algo completes the training, by going through the model again and again (called as Epochs).
Step 2: Create and endpoint

Once the training is complete, it looks like:



Click on Model+Child Jobs, to see the list of associated jobs created along with.


Select the one with AUC weight that is close to 1 >> Click Deploy to continue:


Choose Real time time endpoint. The following scteen will appear:

Choose the necessary Virtual machine and click on Deploy button to continue. This will initialise the deployment and will consequently turn you back to the following screen:

Click on Endpoints and it will show you the endppoint which you just deployed. Will be under provisioning state:

It will be shown as Creating state:


Once done, teh deployment looks like this:




Uh, now it's ready -- let us test it.
Step 3: testing the endpoint --
Click on Test tab: 


I tested with the following JSON:
{
  "input_data": {
    "columns": [
      "GENDER",
      "AGE",
      "SMOKING",
      "YELLOW_FINGERS",
      "ANXIETY",
      "PEER_PRESSURE",
      "CHRONIC DISEASE",
      "FATIGUE",
      "ALLERGY",
      "WHEEZING",
      "ALCOHOL CONSUMING",
      "COUGHING",
      "SHORTNESS OF BREATH",
      "SWALLOWING DIFFICULTY",
      "CHEST PAIN"
    ],
    "index": [1],
    "data": ["M",69,1,2,2,1,1,2,1,2,2,2,2,2,2]
  }
}
And the answer came: YES
You can automate the testing by calling from a web service/API/Azure function directly -- like the example given in my previous post.