Demystifying Supervised Learning: Predicting Loan Default Risk
Introduction:
Supervised learning is a fundamental concept in machine learning that involves training predictive models using labeled data to make predictions on new, unlabeled data. In this blog post, we will delve into the realm of supervised learning through the lens of predicting loan default risk in a banking scenario.
Exploring Supervised Learning in Loan Prediction: Supervised machine learning enables us to develop predictive models that can forecast outcomes based on historical data. Let’s consider a practical example where we aim to build a model that predicts whether a customer will default on a loan using information about previous loans issued by a credit union.
Example Code: Let’s showcase a simplified Python implementation using scikit-learn to train a supervised machine learning model for loan default prediction based on loan features and historical outcomes:
# Import necessary libraries
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Sample data for loan features and outcomes
loan_data = np.array([[10000, 1, 50000], [15000, 2, 60000], [8000, 1, 45000], [20000, 3, 80000], [12000, 2, 55000],
[18000, 3, 65000], [7000, 1, 35000], [25000, 4, 75000], [13000, 2, 60000], [16000, 3, 70000]])
outcomes = np.array([0, 0, 1, 0, 1, 0, 1, 0, 0, 1]) # 0: No default, 1: Default
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(loan_data, outcomes, test_size=0.2, random_state=42)
# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
predictions = model.predict(X_test)
# Evaluate the model's predictive accuracy
accuracy = accuracy_score(y_test, predictions)
print("Model Accuracy:", accuracy)
Conclusion:
Supervised learning empowers us to build predictive models that can make informed decisions based on labeled data. By training models like logistic regression on historical loan data, businesses can enhance risk assessment processes and make more accurate predictions regarding loan default risk. Understanding the principles of supervised learning is crucial for developing effective machine learning solutions in various industries.