Estimating PD model using Logistic Regression in Python

This repository contains a simple implementation of Probability of Default (PD) model estimation. The data was prepared in advance. Dataset contains only categorical variables. The process was prepared in Python.

Analytical process carried out:

• transformation of variables according to the Weight of Evidence measure
• filtering variables using Information Value
• model estimation using Logistic Regression
• evaluation of model fit quality using ROC and PRC
• scorecard development
• performing the scoring process on the test set

Used technology

• Python version 3.8.3
• Anaconda Distribution
• Jupyter Lab

Used libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns 
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score,roc_curve,auc,precision_recall_curve