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Credit Default Prediction with Python: Apply & Analyze

EDUCBA via Coursera

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Overview

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Build practical skills in credit default prediction with Python by learning how to prepare data, develop classification models, and evaluate predictive performance for financial risk analysis. In this course, you will follow a structured workflow that begins with importing datasets and libraries, preprocessing data, handling missing values, encoding categorical features, scaling numerical variables, and performing exploratory data analysis (EDA) to uncover meaningful patterns. As you progress, you will build and assess logistic regression models using evaluation techniques such as confusion matrices and ROC curves. You will also optimize model performance through Grid Search and Randomized Search hyperparameter tuning. The course then expands into decision tree modeling, where you will explore splitting criteria, visualize models with Graphviz, and implement them in Python. Finally, you will apply Random Forest techniques to reduce overfitting and improve predictive accuracy for credit default prediction. Designed for learners who want to strengthen their Python-based predictive modeling skills, this course emphasizes practical implementation and model evaluation using real-world credit datasets. By the end of the course, you will be able to apply, analyze, evaluate, and construct machine learning models that support more informed decision-making in financial risk management.

Syllabus

Data Preparation & Model Foundations

In this module, learners gain a strong foundation in building a credit default prediction model using Python. The module introduces the project’s scope, outlines the workflow, and emphasizes the importance of structured data handling. Learners will explore data preprocessing techniques such as handling missing values, encoding categorical features, and scaling numerical variables. In addition, they will perform exploratory data analysis (EDA) to identify patterns, visualize distributions, and uncover key relationships within the dataset. Finally, learners will split the dataset into training and testing sets to ensure reliable evaluation of logistic regression models for predicting credit default risk.

Model Building & Advanced Techniques

In this module, learners advance beyond data preparation into the core of predictive modeling. The module introduces evaluation metrics such as the confusion matrix and ROC curve to assess classification performance in credit default prediction. Learners will then explore hyperparameter tuning methods like Grid Search and Randomized Search to optimize logistic regression models. The module further builds knowledge with decision tree theory, covering splitting criteria, visualization using Graphviz, and practical implementation in Python. Finally, learners will apply ensemble techniques with Random Forest to reduce overfitting and improve model accuracy for robust credit risk prediction.