====== Project Summary ======

=== Model Name ===
**Loan Default Predictor**

=== Purpose ===
Predict whether a loan applicant is likely to default on a loan based on personal, financial, and credit attributes.

=== Model Type ===
Classification

=== Algorithm Source ===
AutoML (Azure Machine Learning)

---

=== What the Model Does ===
Given applicant data, the model predicts:
  * **0** → Will likely default on loan
  * **1** → Will likely not default on loan

Azure AutoML trained and selected the best performing algorithm based on metrics like AUC-weighted and accuracy.

---

=== Steps Taken ===

==== 1. Create Dataset in Azure ML ====
Used the Azure ML Studio UI to upload a CSV dataset (e.g., `loan_applicants.csv`), containing labeled data with these columns:

<code>
["ApplicantID", "Age", "MaritalStatus", "Dependents", "Education",
 "AnnualIncome", "EmploymentType", "JobSector", "CreditScore",
 "LoanAmount", "LoanTermMonths", "LoanPurpose", "InterestRate",
 "PastDefaults", "LatePayments", "BankCustomerYears", "OwnsHome", "LoanDefault"]
</code>

Where `LoanDefault` is the target label (0 or 1).

==== 2. Train with AutoML via GUI ====
Used the Azure ML Studio UI to:
  * Start a new AutoML classification run
  * Select `LoanDefault` as the target column
  * Choose compute cluster and enable explanation and metrics logging
  * Let AutoML train multiple models and select the best one

==== 3. Register the Model ====
After training:
  * Selected the best run
  * Clicked **Register Model**
  * Model is now available in the workspace under **Assets > Models**

==== 4. Deploy the Model ====
Deployed the model from Azure ML Studio:
  * Clicked **Deploy > Real-time endpoint**
  * Selected compute target
  * Endpoint URL was generated and secured with an API key

==== 5. Test the REST Endpoint ====

Access keys from Key Vault (Python):

<code python>
api_key = secret_client.get_secret("max-ml-key").value
url = secret_client.get_secret("max-ml-endpoint").value

if not api_key or not url:
    raise Exception("Missing key or URL from Key Vault")
</code>

Sample Prediction Request:

<code python>
import urllib.request
import json

data = {
  "input_data": {
    "columns": [
      "Age", "MaritalStatus", "Dependents", "Education", "AnnualIncome",
      "EmploymentType", "JobSector", "CreditScore", "LoanAmount",
      "LoanTermMonths", "LoanPurpose", "InterestRate", "PastDefaults",
      "LatePayments", "BankCustomerYears", "OwnsHome"
    ],
    "index": [0],
    "data": [[35, "Married", 2, "Graduate", 55000, "Full-time", "Private", 710,
              25000, 60, "Home", 5.2, 0, 1, 6, "Yes"]]
  }
}

body = json.dumps(data).encode("utf-8")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}

req = urllib.request.Request(url, body, headers)

try:
    with urllib.request.urlopen(req) as response:
        decoded = response.read().decode("utf-8")
        prediction = json.loads(decoded)[0]

        if prediction == 0:
            print("Prediction: Will likely default on loan")
        elif prediction == 1:
            print("Prediction: Will likely not default on loan")
        else:
            print(f"Unexpected prediction value: {prediction}")

except urllib.error.HTTPError as error:
    print("HTTP error:", error.code)
    print(error.read().decode())
</code>

---

=== Input Format Expected by Endpoint ===

<code json>
{
  "input_data": {
    "columns": [
      "Age", "MaritalStatus", "Dependents", "Education", "AnnualIncome",
      "EmploymentType", "JobSector", "CreditScore", "LoanAmount",
      "LoanTermMonths", "LoanPurpose", "InterestRate", "PastDefaults",
      "LatePayments", "BankCustomerYears", "OwnsHome"
    ],
    "index": [0],
    "data": [[35, "Married", 2, "Graduate", 55000, "Full-time", "Private", 710,
              25000, 60, "Home", 5.2, 0, 1, 6, "Yes"]]
  }
}
</code>

---

=== Outputs ===
The endpoint returns either:
  * [0] or [1]

Mapped in your script to readable text:
  * **0** → “Will likely default on loan”
  * **1** → “Will likely not default on loan”