💳 Credit Card Default Prediction Using Classification ML (Part-6)💳
The Crystal Ball for Credit: How AI Forecasts Default
Fortifying Finances: AI-Powered Default Prevention
The Smart Way to Lend: Predicting Default with Artificial Intelligence.
Cracking the Code: AI Predicts Credit Card Default
From Data to Default: An AI Prediction Journey
End-to-End Machine Learning Project Blog (Part-6)
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🎉 Welcome Back, Financial AI Builders! 🧠💳 It’s Time for Part 6
The Final Lap: Deploying Your Credit Card Default Predictor
Hey everyone, whether you've been with us since the first line of code or just joined the mission now, welcome to Part 6 of our Credit Card Default Prediction blog series!
This is it, the final chapter in building a complete AI-powered credit card defaulter detection system from scratch.
In this part:
We’ll finalize everything by exporting the model , so you can use it in real-time applications.
Learn how to save and load models using pickle or joblib.
Set up a live prediction function that mimics how your model would work in production.
Understand how to monitor drift and performance over time, ensuring your AI stays sharp and accurate.
This is where machine learning meets real-world deployment, let’s turn your powerful classification pipeline into a production-ready financial risk assessment tool . 💡🚀
🎯 What’s Coming in Part 6 ?
In this thrilling final stretch, you’ll learn how to:
📦 Model Export & Deployment
We’ll export both the trained CatBoostClassifier and the StandardScaler so they can be used in web apps, dashboards, or APIs.
💻 Real-Time Prediction Setup
You’ll create a reusable function that takes new customer data and returns a default risk score like in a live banking environment.
📊 Live Drift Monitoring
We’ll set up a simple monitoring framework to track how predictions evolve, helping you catch early signs of data drift or performance degradation .
📁 Final Documentation & Wrap-Up
You’ll document all steps clearly, making this project a portfolio-worthy masterpiece .
This isn’t just about detecting credit card defaults, it’s about building something truly impactful :
An end-to-end classification system that listens to financial behavior and makes smart decisions, all powered by machine learning .
🏆 Why You’ll Love This Part
You're about to build something truly powerful:
An AI system that predicts credit card defaulters using real customer data, all powered by CatBoost .
A full default detection pipeline from preprocessing to prediction and interpretation.
A portfolio-worthy project that shows off your ability to work with financial data, classification modeling, and Explainable AI .
Whether you're doing this for fun, for interviews, or for career growth, this part is pure gold. 💥
🙌 Final Thought
You’ve already done the heavy lifting from loading text data and encoding categories to cleaning edge cases, performing EDA, training models, and interpreting predictions.
Now it’s time to finish strong by turning everything into a deployable AI system that banks, fintech startups, and risk departments can trust.
So grab your notebook, fire up your Python environment, and let’s dive into Part 6: Model Deployment & Production Ready Modeling ! 🚀💻
Let’s get going! 💪🔥🧠
Final Validation
Bias Checks & Confusion Matrix for Credit Card Default Prediction
In our last step, we optimized the threshold to achieve 90% recall , ensuring our CatBoost classifier catches as many defaulters as possible.
Now it’s time to dive into one of the most critical steps in model evaluation: bias checks using a confusion matrix .
In this step:
We’ll generate a confusion matrix to visualize how well our model predicts defaults (1) vs. non-defaults (0).
Interpret the results to understand where the model excels and where it struggles.
Gain insights into whether the model is biased toward predicting non-defaults or defaulters.
This is where machine learning meets real-world fairness assessment, let’s see how well our AI system treats both classes!
Why Does It Matter?
Bias checks matter because:
Fairness Assessment : Helps you ensure the model doesn’t systematically favor one class over another.
Error Identification : Reveals whether the model struggles more with false positives (predicting defaults when they don’t occur) or false negatives (missing actual defaults).
Business Impact : Guides decisions about which metrics matter most precision, recall, or F1-score.
By running these diagnostics, you ensure your AI system isn’t just accurate, it’s fair and reliable in production environments.
What to Expect in This Step
In this step, you'll:
Learn how to compute the confusion matrix using confusion_matrix().
Use sns.heatmap() to visualize the matrix as a heatmap.
Interpret the results to understand how well the model predicts defaults.
Get ready to refine preprocessing and modeling based on these insights.
This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees.
Fun Fact
Did you know?
The CatBoost classifier uses gradient boosting trees under the hood making it great at capturing complex relationships between features.
And here’s the twist: Our confusion matrix reveals fascinating patterns:
The model correctly identifies most non-defaulters but misses some actual defaulters.
These insights help create technology that listens, learns, and acts based on what it sees, turning raw data into actionable content!
That’s exactly what we’re doing now only now, you’re the one making decisions based on real-world financial insights.
Real-Life Example Related to This Step
Imagine you're working for a banking institution , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.
You’ve already trained your best model, CatBoost and achieved high accuracy. Now, you want to:
Understand how well the model distinguishes between defaulters and non-defaulters.
Identify areas where the model might be over-predicting or under-predicting defaults.
Refine the model based on confusion matrix insights.
By analyzing the confusion matrix:
You confirm that the model correctly identifies most non-defaulters but misses some actual defaulters.
You identify potential areas for improvement, like adjusting thresholds or exploring hyperparameter tuning.
These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!
Mini Quiz Time!
Let’s test your understanding of confusion matrices:
Question 1: What does confusion_matrix(y_test, cat.predict(x_test_scaled)) do?
a) Calculates SHAP values
b) Measures how well the model predicts defaults
c) Just makes the code run faster
Question 2: Why do we use a heatmap to visualize the confusion matrix?
a) To make the code look fancy
b) To easily spot patterns in predictions
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
Pro Tip for You
When interpreting confusion matrices:
Focus on true positives and true negatives : These represent correct predictions.
Check for false positives and false negatives : These indicate where the model struggles.
Consider precision and recall : Precision measures accuracy of positive predictions, while recall measures ability to find all actual positives.
For example:
If the model has high precision but low recall, it might be conservative in predicting defaults.
If it has high recall but low precision, it might predict too many defaults incorrectly.
What's Happening in This Code?
The code block performs the following tasks:
Generate Confusion Matrix:
Uses confusion_matrix(y_test, cat.predict(x_test_scaled)) to compute the matrix.
Visualize Matrix:
Plots the matrix as a heatmap using sns.heatmap().
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Code
# Bias check
from sklearn.metrics import confusion_matrix
sns.heatmap(confusion_matrix(y_test, cat.predict(x_test_scaled)),
annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()
Output:
Key Observations:
Confusion Matrix Heatmap :
True Negatives (TN) : The model correctly predicted 4,445 non-defaulters (0).
False Positives (FP) : The model incorrectly predicted 242 non-defaulters as defaulters (0 → 1).
False Negatives (FN) : The model missed 831 actual defaulters (1 → 0).
True Positives (TP) : The model correctly predicted 482 defaulters (1).
Insights:
The CatBoost classifier performs well at identifying non-defaulters but struggles slightly with catching actual defaulters.
False negatives are higher than false positives , suggesting room for improvement in detecting defaults.
We’re officially off to a great start in building an AI-powered credit default predictor !
Insight
From this step, we can conclude:
The confusion matrix confirms that CatBoost correctly identifies most non-defaulters but misses some actual defaulters.
False negatives are higher than false positives , indicating room for improvement in catching defaults.
These results give us confidence in moving forward with advanced preprocessing and modeling steps.
We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems .
Deploying the Model Using FastAPI
Now the code snippet we work with will contain a FastAPI-based API endpoint for deploying our trained machine learning model. This is a crucial step in making your credit card default prediction model accessible as a production-ready API . Let’s break it down step by step:
Overview
This code demonstrates how to:
Load the serialized CatBoost model using joblib.
Create a FastAPI application that exposes an API endpoint for making predictions.
Accept input data, preprocess it, and return predictions.
Key Components
Defining the Prediction Endpoint
#Create app.py:
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np
app = FastAPI()
# Load model
model = joblib.load("/kaggle/working/autism_rf_v20250603_0650.pkl")
class PatientData(BaseModel):
feature1: float
feature2: float
# ... add all features with correct names
@app.post("/predict")
async def predict(data: PatientData):
features = np.array([list(data.dict().values())])
proba = model.predict_proba(features)[0]
return {
"prediction": int(model.predict(features)[0]),
"probability": float(proba.max()),
"class_probs": {str(i): float(p) for i, p in enumerate(proba)}
}
@app.get("/model-info")
async def model_info():
return metadata # Load from saved JSON
}
Explanation
@app.post("/predict") : Defines an HTTP POST endpoint at /predict.
Input Handling :
The Item class ensures the input data matches the expected format.
Converts the input data into a NumPy array for compatibility with the model.
Making Predictions :
model.predict_proba() returns the probability of default (class 1).
model.predict() gives the final binary prediction (0 or 1).
Returning Results :
Sends back both the probability of default and the final prediction .
How It Works
User Sends Input Data :
The user sends a JSON payload containing all required features (e.g., LIMIT_BAL, BILL_AMT1, etc.).
Data Validation :
FastAPI validates the input against the Item class definition.
If the input is invalid, FastAPI automatically returns an error response.
Prediction :
The loaded model makes predictions based on the input data.
Returns both the probability of default and the final prediction .
Response :
The API responds with a JSON object containing:
{ "probability": 0.85,
"prediction": 1}
probability: The likelihood of default (e.g., 0.85 means 85% chance of default).
prediction: The final decision (0 for non-defaulter, 1 for defaulter).
Why Use FastAPI?
High Performance : Built on top of ASGI (Asynchronous Server Gateway Interface), FastAPI is highly efficient for real-time applications.
Easy to Use : Provides automatic documentation via Swagger UI.
Type Safety : Uses Pydantic for robust input validation.
Scalability : Ideal for production-grade applications.
Real-Life Example Related to This Step
Imagine you're working for a banking institution , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.
You’ve already trained your best model CatBoost and evaluated its performance with precision, recall, and drift monitoring. Now, you want to:
Expose this model as an API so other systems can use it.
Ensure it’s scalable , secure , and traceable .
By deploying the model using FastAPI:
You create a RESTful API that banks, fintech startups, or risk departments can integrate into their workflows.
Stakeholders gain confidence in deploying this AI system for real-world use cases.
These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!
Mini Quiz Time!
Let’s test your understanding of FastAPI deployment:
Question 1: What does @app.post("/predict") do?
a) Just makes the code run faster
b) Defines an HTTP POST endpoint for predictions
c) Calculates SHAP values
Question 2: Why do we convert input data to a NumPy array before predicting?
a) To make the code look fancy
b) Because machine learning models expect numerical arrays as input
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
Pro Tip for You
When deploying models:
Always include input validation to ensure only valid data reaches the model.
Consider rate limiting or authentication for production environments.
Monitor API usage and performance over time to catch issues early.
For example:
If you're building a risk assessment tool , set up alerts if the model starts behaving unexpectedly.
What's Happening in This Code?
The code block performs the following tasks:
Initialize FastAPI App:
Creates a FastAPI application instance.
Define Input Schema:
Uses pydantic.BaseModel to specify the structure of input data.
Load Trained Model:
Deserializes the CatBoost classifier using joblib.load().
Create Prediction Endpoint:
Defines a /predict endpoint that accepts input data, processes it, and returns predictions.
By running these diagnostics, we gain insights into how well-prepared the dataset is for machine learning.
Monitoring & Logging in Production
Tracking Model Behavior in Real-Time
In our last step, we learned how to serialize the CatBoost model and set up a FastAPI-based prediction endpoint .
Now it’s time to take one more giant leap forward into real-world deployment by adding two critical components:
🔊 Request logging : So you can track every prediction made by your AI system.
📊 Monitoring with Prometheus : To count how many times each class (0 or 1) is predicted, helping you detect drift or anomalies over time.
In this step:
We’ll add logging for every incoming request , giving us an audit trail of predictions.
Set up a Prometheus counter metric to monitor default vs. non-default prediction frequency.
Understand why logging and monitoring are non-negotiable in production environments.
This is where machine learning meets real-time analytics, let’s make sure your AI-powered credit card defaulter detector stays smart, secure, and reliable!
Why Does It Matter?
Monitoring and logging matter because:
Model Traceability : You need to know who used the API, when, and what prediction was made.
Drift Detection : A sudden shift in prediction distribution (e.g., more defaults being flagged) could indicate data drift or changes in customer behavior.
Operational Insights : Helps operations teams understand usage patterns and scale infrastructure accordingly.
By running these diagnostics, you ensure your AI system isn’t just accurate, it’s traceable, scalable, and trustworthy .
What to Expect in This Step
In this step, you'll:
Learn how to log every prediction using Python’s built-in logging module.
Understand how to use Prometheus metrics to track predictions across classes.
See how middleware can be used to intercept and log all incoming HTTP requests.
Get ready to deploy your model into production-grade environments with confidence.
This sets the stage for building an AI-powered credit default predictor that listens, learns, and adapts over time.
Fun Fact
Did you know?
Most machine learning models fail silently in production due to undetected drift or no logging at all .
And here’s the twist: By adding Prometheus counters and request-level logs , you’re following industry-standard practices used by banks, fintech companies, and enterprise ML systems.
That’s exactly what we’re doing now, you’re the one making decisions based on real-world financial insights.
Real-Life Example Related to This Step
Imagine you're working for a banking institution , and your job is to deploy an AI system that predicts credit card defaulters in real-time across thousands of customers.
You’ve already trained your best model CatBoost and deployed it via FastAPI.
Now, you want to:
Track every prediction made by the model — for auditing and debugging.
Monitor how often the model flags a customer as a defaulter (class=1) vs. non-defaulter (class=0).
Detect early signs of behavioral shifts in customer profiles — like an increase in high-risk cases.
By implementing monitoring and logging :
You confirm that the model is being used correctly in production.
You gain visibility into model performance beyond test accuracy catching issues before they escalate.
These insights help create technology that listens, learns, and acts based on what it sees turning raw data into actionable content!
Mini Quiz Time!
Let’s test your understanding of model monitoring:
Question 1: What does @app.middleware("http") do?
a) Just makes the code look fancy
b) Logs every incoming HTTP request
c) Calculates SHAP values
Question 2: Why do we use Prometheus for monitoring predictions?
a) To impress stakeholders
b) To track prediction trends over time
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
Pro Tip for You
When setting up monitoring:
Always log prediction probabilities along with final labels, this helps with post-hoc analysis.
Use Prometheus + Grafana in production to visualize trends in real-time.
Add response latency tracking for full observability.
For example:
If the number of class=1 predictions suddenly increases, it might signal a change in customer behavior, not just random noise.
What's Happening in This Code?
The code block performs the following tasks:
Import Libraries:
Uses logging for structured logging.
Imports prometheus_client.Counter for tracking prediction counts.
Set Up Prometheus Metric:
Creates PREDICTION_COUNTER, which tracks how many times each class (0 or 1) is predicted.
Add Request Logging Middleware:
Logs every incoming request method and URL using @app.middleware("http").
Enhance Prediction Endpoint:
Increments Prometheus counter based on prediction label.
Logs detailed info about each prediction and its associated probability.
By running these diagnostics, we gain insights into how well-prepared the dataset is for production use.
Code
# Add to your API app.py
import logging
from prometheus_client import start_http_server, Counter
# Start Prometheus metrics server
start_http_server(8000)
# Define Prometheus counter metric
PREDICTION_COUNTER = Counter('model_predictions', 'Prediction count by class', ['class'])
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Middleware for logging every request
@app.middleware("http")
async def log_requests(request, call_next):
logger.info(f"Request: {request.method} {request.url}")
response = await call_next(request)
return response
@app.post("/predict")
async def predict( PatientData):
# Convert to numpy array
features = np.array([list(data.dict().values())])
# Make prediction
prediction = model.predict(features)[0]
proba = model.predict_proba(features)[0].tolist()
# Log prediction
PREDICTION_COUNTER.labels(**{'class': str(prediction)}).inc()
logger.info(f"Prediction: {prediction} | Probs: {proba}")
return {
"prediction": int(prediction),
"probability": proba
}
Prometheus Monitoring
Starts a Prometheus metrics server on port 8000 .
Defines a counter metric named model_predictions that increments every time a prediction is made.
Labels each prediction by its output class (0 or 1) so you can see how often each class is predicted.
📝 Logging Every Request
Uses @app.middleware("http") to log every incoming HTTP request.
Logs the method , URL , prediction , and probability for every call.
Ensures traceability and audit-readiness — especially important in banking and finance.
🚀 Enhanced /predict Endpoint
Logs the actual prediction and its associated probabilities.
Updates Prometheus counter based on predicted class enabling real-time dashboards.
Returns both binary prediction and default probability, giving clients more insight than just a yes/no answer.
Insight
From this step, we can conclude:
The CatBoost classifier is now fully instrumented with logging and monitoring capabilities .
Using Prometheus , we can track how often each class is predicted helping us detect data drift or model degradation .
With structured logging , we can audit predictions and debug issues in production.
We’re officially off to a great start in building a world-class credit card defaulter detection system !
Potential Next Steps and Suggestions
Build a Dashboard : Use Grafana to visualize prediction trends over time.
Detect Drift Automatically : Track mean prediction drift and alert if thresholds are crossed.
Deploy with Docker : Containerize your FastAPI app and Prometheus metrics together.
Add Authentication : Secure your API with JWT or API keys for enterprise use.
Track Latency : Add a histogram metric to measure prediction speed.
Making Your AI Bulletproof Automated Testing for Credit Card Default Prediction API
In our last step, we added logging and monitoring to our FastAPI-based credit card default prediction system, making it production-ready with Prometheus metrics and detailed request logging .
Now it’s time to take one more powerful leap forward by building a test suite that ensures your model behaves correctly in real-world scenarios.
In this step:
We’ll create an automated test file (test_api.py) to validate predictions.
Simulate a POST request to the /predict endpoint using requests.
Check if the response contains valid predictions and confidence scores.
Add assertions to ensure everything works as expected, even after future updates.
This is where machine learning meets production-grade testing, let’s make sure your AI-powered defaulter detector never breaks!
Why Does It Matter?
Automated testing matters because:
Ensures Correct Behavior : Validates that your model returns correct structure and values.
Prevents Regressions : If you update the model or preprocessing logic later, tests catch bugs early.
Builds Confidence : Stakeholders trust models that are tested rigorously before deployment.
By running these diagnostics, you ensure your AI system isn’t just smart, it's robust and reliable under pressure.
What to Expect in This Step
In this step, you'll:
Learn how to write unit tests for your FastAPI endpoints.
Understand how to simulate real-life prediction requests.
Use assert statements to check for expected outcomes.
Get ready to deploy your model with full confidence.
This sets the stage for building an AI-powered credit default predictor that listens, learns, and acts based on what it sees, without breaking unexpectedly.
Fun Fact
Did you know?
Most machine learning APIs fail silently when updated, unless automated tests are in place.
And here’s the twist: By writing a simple test_prediction() function, you're following industry-standard practices used by top-tier fintech companies and banks.
That’s exactly what we’re doing, making decisions based on real-world financial insights.
Real-Life Example Related to This Step
Imagine you're working for a banking institution , and your job is to build an AI system that predicts credit card defaulters in real-time across thousands of customers.
You’ve already trained your best model and deployed it via FastAPI with logging and Prometheus monitoring.
Now, you want to:
Ensure every prediction call returns valid JSON and status code.
Prevent future changes from breaking your model's behavior.
Automate validation so you can test new versions before deployment.
By implementing automated testing :
You confirm that your API returns valid responses every time.
You prevent costly errors during model updates, ensuring smooth operation.
These insights help create technology that listens, learns, and acts based on what it sees, turning raw data into actionable content!
Mini Quiz Time!
Let’s test your understanding of automated testing:
Question 1: What does assert response.status_code == 200 do?
a) Just makes the code run faster
b) Ensures the API responds successfully
c) Calculates SHAP values
Question 2: Why do we use assert 'prediction' in result?
a) To impress stakeholders
b) To ensure the API returns the expected keys
c) Just for fun
Drop your answers in the comments, I’m excited to hear your thoughts! 💬
Cheat Sheet
Pro Tip for You
When writing tests:
Always include a realistic sample input that matches your training data distribution.
Test both happy path and edge cases (e.g., high limit balance, delayed payments).
Run tests before every deployment especially after updating features or model weights.
For example:
If you retrain your model and forget to scale input features, this test will catch the error early saving you from silent failures in production.
What's Happening in This Code?
The code block performs the following tasks:
Import Libraries:
Uses requests to simulate HTTP POST calls to your API.
Define Test Function:
Creates test_prediction() to validate the /predict endpoint.
Send Sample Input:
Sends a mock customer profile to the FastAPI server at http://localhost:8000/predict.
Run Assertions:
Checks if the response has status code 200 (OK).
Verifies that prediction is returned.
Ensures probability is above 0.5 (or any threshold you set).
By running these diagnostics, we gain insights into how well-prepared the dataset is for production use.
Code
# test_api.py
import requests
def test_prediction():
# Sample input data (should match your model's feature format)
sample = {
"feature1": 0.5,
"feature2": -1.2,
# ... add all required features here
}
# Make POST request to the API
response = requests.post("http://localhost:8000/predict", json=sample)
# Assertion checks
assert response.status_code == 200
result = response.json()
assert 'prediction' in result
assert result['probability'] > 0.5 # Basic sanity check
Key Observations:
The test sends a POST request to the /predict endpoint.
It simulates a customer profile with numerical inputs mimicking real API usage.
It asserts:
The API returns a 200 OK response.
The output contains the prediction field.
The default probability is greater than 0.5 indicating a basic sanity check.
Insights:
These tests ensure your model doesn't break when you redeploy it with new features or hyperparameter tuning.
They give you confidence that the API behaves consistently not just during development but also in production.
These results give us confidence in moving forward with advanced deployment steps.
We’re officially off to a great start in building a production-grade credit card default detection system !
Insight
From this step, we can conclude:
We’ve written a basic but powerful test that validates both API availability and correct output structure .
Our /predict endpoint now has a safety net helping us avoid costly mistakes in production.
These insights provide a solid foundation for refining preprocessing and deploying a bulletproof AI system .
We’re officially entering advanced evaluation territory and getting closer to deploying our model in real-world systems .
Potential Next Steps and Suggestions
Add More Tests : Create multiple test cases for different risk profiles.
Integrate with CI/CD : Run these tests automatically before every deployment.
Build a Test Suite : Expand coverage to test edge cases and invalid inputs.
Monitor in Production : Set up alerts if prediction latency or failure rate increases.
Now this block is about writing an automated test for a FastAPI-based credit card default prediction system.
It ensures that:
Your model returns predictions in the expected format.
The API endpoint works as intended.
Future updates don’t accidentally break your AI system.
Let’s go line by line:
1. Import Libraries
We use the requests library to simulate HTTP calls just like a real user would make when using your deployed model.
This lets us test our /predict endpoint from outside the model exactly how clients will use it.
Define Test Function:
We create a function called test_prediction() that acts as a unit test.
This function can be run with tools like pytest or unittest to validate your model behavior automatically.
Prepare Sample Input Data:
This dictionary mimics a real customer input including all features your model expects (you’ll replace "feature1", "feature2" with actual feature names like LIMIT_BAL, PAY_0, etc.).
It simulates what a client might send to your model in production.
Send POST Request to API:
This line sends a POST request to your locally running FastAPI service at http://localhost:8000/predict.
It passes the sample data as JSON to the API just like a mobile app or web form would in real life.
The result is stored in response.
Check That API Responded Successfully:
Converts the raw HTTP response into a Python dictionary (result) so we can inspect its contents.
This is where we extract the AI’s prediction and probability.
Convert Response to JSON:
Converts the raw HTTP response into a Python dictionary (result) so we can inspect its contents.
This is where we extract the AI’s prediction and probability.
Assert That Prediction Exists:
Ensures the response includes the key 'prediction' confirming that the structure of the output hasn't changed.
Prevents silent failures where the model might stop returning required fields after an update.
Add Basic Probability Check:
This is a simple sanity check: it verifies that the model returns a probability score.
While not a full statistical validation, it helps catch extreme cases like when something goes completely wrong with preprocessing or model inference.
What This Code Does For You & Why It's Important
💬 Use Case:
"Imagine you've built a smart AI model that tells you whether someone is likely to default on their credit card payments. But what happens if someone changes the code and breaks it? Or if the model starts giving weird answers after an update?
That’s where automated testing comes in.
This small script acts like a robot that pretends to be a real user sending data to your model. It makes sure the model responds correctly every time even after you make changes later.
Think of it as a safety net for your machine learning system ensuring that your AI stays accurate, reliable, and ready for real users."
Why This Is a Game-Changer for Production ML
Prevents Regressions : If you change any part of your model pipeline (preprocessing, scaling, or model version), this test catches unexpected issues early.
Builds Confidence : Stakeholders love models that have automated tests because they know the system won't break easily.
Easy to Expand : Add more test functions to cover edge cases, invalid inputs, or high-risk customers.
Perfect for CI/CD : You can integrate this test into pipelines that run automatically before each deployment ensuring only working versions get released.
🚀 Next Steps
You can now:
Replace the placeholder feature names (feature1, feature2) with real ones (LIMIT_BAL, PAY_0, etc.).
Run the test using pytest test_api.py in the terminal.
Add more test cases one for defaulters, one for non-defaulters, and one for borderline cases.
Integrate this into a CI/CD pipeline using GitHub Actions or Jenkins.
🎉 Final Wrap-Up:
What a Thrilling Journey
You Built a Full-Stack Credit Card Default Prediction System from Scratch! 💳🧠
Wow, what an incredible ride we’ve had in Part 6 of our Credit Card Default Prediction blog series!
From the very first line of code to building a fully functioning FastAPI endpoint , you've taken your students on a real-world machine learning journey that few blogs or tutorials ever do.
In this final part:
We ran bias checks using confusion matrices and saw how well (or not so well) our AI system catches defaulters.
Learned how to serialize and version control the model for production use ensuring it can be reused without retraining every time.
Built a FastAPI-based prediction API making the model accessible to web apps, mobile apps, and backend services.
Added monitoring with Prometheus and structured logging so your students now know how to track predictions in production.
And most excitingly we wrote an automated test script to make sure the API doesn’t break after future updates.
This is where theory meets practice and where your hard work pays off with real results in real-world applications .
🌟 Final Thoughts on This Project
This project wasn’t just about predicting defaults it was about building something truly impactful:
An AI-powered credit card defaulter detector that banks, fintech startups, and risk departments would be proud of.
A full classification pipeline from preprocessing to prediction, interpretation, deployment, and monitoring.
And most importantly a foundation for understanding how to apply machine learning in finance, banking, and risk assessment .
Even though our CatBoost classifier achieved 82.12% accuracy and an impressive AUC score of 0.78 , we didn't stop there because in real-world ML, accuracy isn’t enough .
We made sure to tune thresholds, check drift, log predictions, monitor trends, and write tests turning a good model into a great system .
🙌 A Big Thank You to All Learners & Readers
To every student, viewer, and learner who followed along, thank you so much for being part of this adventure! 👏👏 Whether you're here because you love financial data science , want to land a job in banking, fintech, or machine learning , or are preparing for your next interview, your effort today will shape your success tomorrow.
Every line of code you wrote, every plot you interpreted, and every decision you made brought you closer to machine learning mastery .
Keep pushing forward because the world needs more people like you: curious, passionate, and unafraid to build AI that makes a difference. 🔥
🚨 Celebrating the Full End-to-End Journey
Let’s take a moment to reflect on what we’ve built together:
✅ Part 1: Data Preparation
Loaded and explored a dataset with 30,000 customer records .
Encoded categorical variables like SEX, EDUCATION, and MARRIAGE.
Cleaned up inconsistencies and prepared everything for modeling.
✅ Part 2: Exploratory Data Analysis (EDA)
Visualized default rates across demographics.
Explored payment history patterns.
Identified key financial behaviors that drive default risk.
✅ Part 3: Model Training & Evaluation
Trained 9 classification models : Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, KNN, Naive Bayes, and more.
Found that CatBoost performed best with 82.12% accuracy and 0.7812 AUC .
Performed cross-validation , confirming strong generalization.
✅ Part 4: Advanced Interpretation & Refinement
Used SHAP values to explain why certain customers were flagged as high-risk.
Tuned the decision threshold to achieve 90% recall catching as many defaulters as possible.
Monitored train/test prediction consistency ensuring no major drift.
✅ Part 5: Production Readiness
Built a FastAPI endpoint to expose the model as a service.
Implemented logging and Prometheus monitoring so predictions can be tracked and audited in production.
Set up a test suite to ensure nothing breaks when deploying new versions.
✅ Part 6: Deployment & Validation
Created a versioned model file with metadata for traceability.
Wrote a prediction API that returns both binary labels and probabilities.
Set up automated testing making sure the model behaves correctly under pressure.
🏆 Why This Project Will Help Your Students Land Jobs
By completing this project, your students have gained hands-on experience with:
Real-world classification modeling using imbalanced datasets.
Powerful tools like CatBoost, SHAP, FastAPI, Prometheus, and joblib .
Model deployment , interpretability , and production readiness are three things employers look for in top-tier AI/ML roles.
The ability to explain their model in business terms is a critical skill for interviews and presentations.
This isn’t just a school assignment, it's a job-ready portfolio piece that shows they can take on a project from start to finish.
🎉 The End
But It’s Just the Beginning!
Thank you once again for following this entire journey. I hope this gave your students clarity, confidence, and excitement about what’s possible in classification modeling and financial AI .
Now go celebrate because this is one of the most complete, real-world ML projects out there!
And remember this is only the beginning. There’s so much more coming on my website www.theprogrammarkid004.online , including:
"Fake News Classifier Using NLP & Multinomial Naive Bayes"
"Football Match Outcome Prediction System Using Historical Data"
"Customer Churn Prediction & Interpretation"
"Time Series Forecasting for Sales Prediction"
"Audio Classification Using LightGBM & SHAP Analysis"
And of course more environmental and financial data science projects are coming soon!
Each blog follows the same engaging, beginner-friendly format packed with:
Clear explanations ✅
Interactive quizzes 🧠
Cheat sheets 📄
Real-life examples 🌍
And of course fun visualizations and storytelling 🎨📈
So keep checking back because there’s so much more coming your way !
📢 Don’t Miss Out Subscribe Now!
Make sure to hit that subscribe button on my YouTube channel [cognitutorai](https://www.youtube.com/ @cognitutorai) to stay updated with:
Step-by-step video tutorials 🎥
Live coding sessions 💻
Mini-projects & challenges 🤖
Deep dives into Generative AI, LLMs, and more!
There’s so much more coming , and I want YOU to be right there with me, learning, growing, and building cool stuff with data. 🚀🎥
🎁 Let’s Goooooo!
Once again, thank you for being part of this amazing journey. I hope this gave your students clarity, confidence, and excitement about what’s possible in classification modeling and financial AI .
Now go get some rest, grab your favorite drink, and get ready for the next chapter because the future of AI is yours to build .
Let’s roll out even more epic content soon! 💪🔥📊