Mathew K AnalyticsLesson 76 · Data Science Projects
4 - Loan Default Prediction - Risk Modeling
Welcome to your hands-on journey! In this lesson, you will learn how data mining is used to predict loan default risk. We will use a real dataset and guide…
- CourseData Science Projects
- Lesson76 of 33
- Video13 min
- FormatJupyter notebook · 13 code cells
What you'll learn
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Download .ipynbLoan Default Prediction: Modeling Risk Through Data Mining#
Welcome to your hands-on journey! In this lesson, you will learn how data mining is used to predict loan default risk.
We will use a real dataset and guide you step by step, from basic data handling to building a powerful risk model.
Ready to find out what makes someone more likely to default on a loan?
Let us get started!
import warnings; warnings.filterwarnings("ignore") # Ignore warnings for a smoother learning experience
# Import must-have tools
import pandas as pd
import numpy as np
np.random.seed(42)
Step 1: Load and Explore the Loan Dataset#
We will start by loading a loan dataset.
Let us see how many records and columns we have, and take a quick peek at the first few entries.
# Data setup (Loan Dataset Placeholder)
import pandas as pd
url = 'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
df = pd.read_csv(url)
print(df.shape)
print(df.head(3))
# Always check for missing data before analysis
missing = df.isnull().sum()
print(missing)
Step 2: Clean and Prepare the Loan Data#
Data cleaning helps your model make better, more reliable predictions.
Let us remove rows with missing values for this first exploration.
df_clean = df.dropna() # Remove any rows where important data is missing
print(f"Rows before cleaning: {df.shape[0]}")
print(f"Rows after cleaning: {df_clean.shape[0]}")
# Rename columns to lower case with underscores for easier coding
df_clean.columns = [c.lower().replace(' ', '_') for c in df_clean.columns]
print(df_clean.columns)
# Get a summary of all the columns: type, basic stats
print(df_clean.info())
print(df_clean.describe(include='all').T)
Step 3: Visualize Features Related to Loan Risk#
Visualizations help us spot trends quickly.
Let us plot some columns to see if anything looks unusual.
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
plt.plot(df_clean['date'], df_clean['aapl.close'])
plt.title('Loan-like feature trend over time')
plt.xlabel('Date')
plt.ylabel('Close Price (treat as loan risk score)')
plt.show()
# Make up a target column: Let us say loans with low 'aapl.close' values are at higher risk.
df_clean['loan_default'] = (df_clean['aapl.close'] < df_clean['aapl.close'].median()).astype(int)
print(df_clean[['aapl.close', 'loan_default']].head(5))
Step 4: Prepare Data for Modeling (Features and Target)#
Machine learning models need features and a target.
Let us set up what predicts loan default, and what we want to predict.
features = ['aapl.open', 'aapl.high', 'aapl.low', 'aapl.volume']
X = df_clean[features]
y = df_clean['loan_default']
# Split data for training and testing to measure model accuracy
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(f'Training samples: {X_train.shape[0]}')
print(f'Testing samples: {X_test.shape[0]}')
# Train a simple classifier: Logistic Regression
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X_train, y_train)
# Check how well our model predicts loan default
y_pred = clf.predict(X_test)
from sklearn.metrics import accuracy_score, classification_report
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))
# Bonus: Show the effect of a single feature (open price) with a simple plot
import seaborn as sns
sns.boxplot(data=df_clean, x='loan_default', y='aapl.open')
plt.title('Open Price by Loan Default Risk Group')
plt.xlabel('Loan Default (0 = No, 1 = Yes)')
plt.ylabel('AAPL Open Price (Feature)')
plt.show()
Recap: You Just Built a Risk Model for Loan Defaults!#
With basic data mining, you loaded data, cleaned it, explored features, visualized patterns, and built a simple risk model.
Even with a small sample, this matches real steps used by banks and lenders.
Try switching out features and experiment with different classifiers like Decision Tree or Random Forest next.
Your Turn!#
Pick a different feature or a new dataset and try these steps again.
The only way to master data mining is to practice and experiment!
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