Project that integrates Web Development, DB and Machine Learning.
The purpose of this project was to make inferences using machine learning models with data entered manually through a Web App, and that these are stored in a database in order to show them as a register.
Since the main goal was not to create a complicated model, the Iris dataset was used as a demo. For the Web App, the Flask library was chosen, due to its simplicity; and a little of Boostrap for design. The database used was PostgresSQL, from which it was connected using SQLAlchemy. Finally, a Decision Tree model was previously trained, whose weight is found in resources (res/model.pkl)
The Web App has an inference page which has a list of registers which have been added from this interfece. It has also a button to edit some register and get an updated predicted class, as well as a button to delete any register.
