Implemented a logistic regression model for credit data classification using Python and scikit-learn. The model was trained and evaluated on a dataset containing features such as income, age, and loan information to predict whether a customer will default or not. Achieved an accuracy score of 91.5%, indicating the model's effectiveness in predicting credit defaults.
The model used 1500 data-points(70%) for training and used 600 data-points for testing Used Cross validation for more accurate predition of model. This project demonstrates proficiency in data preprocessing, model training, evaluation, and utilizing scikit-learn's machine learning algorithms for classification tasks.