This project focuses on analyzing the German Credit dataset to assess credit risk. By applying machine learning techniques, the study aims to predict the creditworthiness of individuals based on various socio-economic factors.
Data Preprocessing: Cleaned and transformed the dataset to handle missing values and encode categorical variables appropriately.
Feature Selection: Identified key predictors influencing credit risk, such as age, employment status, and credit history.
Model Development: Implemented classification algorithms, including logistic regression and decision trees, to predict creditworthiness.
Model Evaluation: Assessed model performance using metrics like accuracy, precision, recall, and the area under the ROC curve.