Predicting Lending Rates: An Intro to AWS Machine Learning
"Machine learning for X" is a major trend in the startup space. CEOs of the largest tech companies have cited machine learning as a strategic component of their strength and future growth.
But for many organizations, machine learning feels inaccessible—the domain of data scientists with PhDs and companies with massive R&D budgets. The good news is that cloud providers like Amazon Web Services (AWS) have made machine learning more accessible than ever.
In this article, we'll walk through a practical example: using AWS machine learning services to predict lending rates. This will give you a hands-on understanding of how to approach a real-world ML problem.
The Problem: Predicting Lending Rates
Imagine you're a financial services company that wants to predict the interest rates for personal loans. You have historical data about borrowers—their credit scores, income, employment history, and the rates they received. Can you use this data to predict rates for new applicants?
This is a classic regression problem, and it's a great fit for machine learning.
Getting Started with AWS Machine Learning
AWS offers several services for machine learning, ranging from fully managed services like Amazon SageMaker to pre-built models for specific use cases. For our lending rate prediction, we'll use Amazon SageMaker, which provides a complete platform for building, training, and deploying ML models.
Step 1: Prepare Your Data
The first step in any ML project is preparing your data. For lending rate prediction, you might have a dataset with columns like:
- Credit score
- Annual income
- Debt-to-income ratio
- Employment length
- Loan amount
- Loan term
- Interest rate (our target variable)
You'll need to clean this data, handle missing values, and potentially create new features that might be predictive.
Step 2: Choose an Algorithm
For predicting a continuous variable like interest rate, you have several options:
- Linear Regression — Simple and interpretable, good for linear relationships
- XGBoost — Powerful gradient boosting algorithm that often performs well out of the box
- Neural Networks — Can capture complex patterns but require more data and tuning
For many business problems, XGBoost is a great starting point—it's powerful, relatively easy to use, and handles many data issues gracefully.
Step 3: Train Your Model
With SageMaker, training a model is straightforward:
- Upload your training data to S3
- Create a SageMaker training job specifying your algorithm and hyperparameters
- SageMaker spins up the required compute resources, trains your model, and saves it to S3
The training process finds the patterns in your historical data that best predict the interest rates.
Step 4: Evaluate and Iterate
Before deploying your model, you need to evaluate its performance. Common metrics for regression problems include:
- Mean Absolute Error (MAE) — Average difference between predicted and actual rates
- Root Mean Squared Error (RMSE) — Similar to MAE but penalizes larger errors more
- R-squared — How much of the variance in rates your model explains
Use a held-out test set to get an unbiased estimate of your model's performance. If the results aren't satisfactory, iterate on your features, algorithm, or hyperparameters.
Step 5: Deploy for Predictions
Once you're happy with your model's performance, deploy it as a SageMaker endpoint. This gives you a REST API that can make predictions in real-time:
# Example prediction request
response = runtime.invoke_endpoint(
EndpointName='lending-rate-predictor',
ContentType='text/csv',
Body='720,85000,0.25,5,15000,36'
)
predicted_rate = json.loads(response['Body'].read())
Key Considerations
Data Quality Matters Most
The single biggest factor in ML success is data quality. Garbage in, garbage out. Invest time in understanding and cleaning your data before worrying about algorithms.
Start Simple
It's tempting to jump to complex neural networks, but often simpler models perform just as well and are easier to understand and maintain. Start simple and add complexity only when needed.
Monitor in Production
ML models can degrade over time as the world changes. Monitor your model's predictions and retrain periodically with fresh data.
Explainability
Especially in regulated industries like financial services, you may need to explain why your model made a particular prediction. Consider using interpretable models or tools like SHAP values to understand feature importance.
Conclusion
Machine learning is no longer just for tech giants. With services like AWS SageMaker, organizations of all sizes can harness the power of ML to make better predictions and decisions.
The lending rate prediction example we walked through illustrates the core workflow: prepare data, train a model, evaluate performance, and deploy for predictions. This same pattern applies to countless business problems, from customer churn prediction to demand forecasting to fraud detection.
The key is to start with a clear business problem, focus on data quality, and iterate your way to a solution that delivers real value. Machine learning isn't magic—it's a powerful tool that, when applied thoughtfully, can transform how your organization operates.