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Retail Credit Risk Scorecard & Probability of Default (PD) Logistic Regression in Python
Build a retail credit risk scorecard using scikit-learn Logistic Regression in Python, computing Weight of Evidence (WoE) and Information Value (IV).
#pandas#numpy#scikit-learn
Problem Statement
Retail lending banks build credit scorecards to predict borrower default risk based on bureau score, income, and debt-to-income ratio using Logistic Regression.
Formula / Core Logic
PD = 1 / (1 + exp(-(beta_0 + beta_1*Income + beta_2*Bureau_Score)))
Installation
pip install pandas numpy scikit-learn
credit-card-scoring-logistic-regression-scorecard-python.py
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
\.def train_credit_scorecard(df, feature_cols, target_col):
X = df[feature_cols]
y = df[target_col]
model = LogisticRegression()
model.fit(X, y)
probs = model.predict_proba(X)[:, 1]
# Convert probability to credit score (e.g. 300 to 900 scale)
score = 900 - (probs * 600)
return model, score
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Buy Full Bundle on Gumroad ($19) →Frequently Asked Questions
Q: What is Information Value (IV) in credit scorecard development?
Information Value measures the predictive power of each independent variable in separating good borrowers from defaulters.