Build interactive web applications with Streamlit and Python
Train Logistic Regression, Random Forest, and Support Vector Classifiers using scikit-learn
Plot evaluation metrics for binary classification algorithms
Using Python and Streamlit to build beautiful and interactive ML web apps.
Load, explore, visualize and interact with data, and generate dashboards.
Web application will allows users to choose what classification algorithm
they want to use and let them interactively set hyper-parameter values.