Project 1: Bank Note Authentication
Took the dataset from the UCI Machine Learning Repository.Analyzed the Data. Used Scikit-learn library. Split the Data into a Train and test. Used Random Forest Classifier to create the ML model to classify the note as Authentic or Non-Authentic. Thereafter, used Flask web-framework to build the web application and deployed the model using Flasgger. Also, used Streamlit to deploy the model. To make this more scalable, completed the deployment of this ML model pipeline using Docker and Kubernetes.
