Loan Approval Classification App with MLflow

• Built a Streamlit-based loan approval app achieving 95% accuracy using Bagging, with MLflow for experiment

tracking.

• Applied SMOTE to handle imbalanced data and compared models including Deep Neural Networks (DNN, 88%

accuracy).

• Tools Used: Python, NumPy, Pandas, Matplotlib, Streamlit, MLflow, Scikit-learn, TensorFlow/Keras