Predicting credit risk using machine learning techniques.
Credit risk analysis involves evaluating the likelihood that a borrower will default on their debt obligations. This project includes data collection, preprocessing, model training, evaluation, and deployment.
The objective is to predict the likelihood of loan applicants defaulting on loans, aiding financial institutions in making informed decisions.
Data is collected from financial institutions, credit bureaus, and public statements. It includes borrower demographics, credit history, loan characteristics, and financial ratios.
We use descriptive statistics and visualizations to understand the data distribution and relationships.
Creating new features like the debt-to-income ratio and encoding categorical variables to improve model accuracy.
We use algorithms like Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Neural Networks for supervised learning.
Evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are used to assess model performance.
All processes, findings, and model performance details are documented for transparency.