A Web-Based User Interface for Machine Learning Analysis

The objective of this thesis is to develop a user friendly web application that will be used to analyse data sets using various machine learning algorithms. The application design follows human computer interaction design guidelines and principles to make a user friendly interface [Shn03]. It uses Linear Regression, Logistic Regression, Backpropagation machine learning algorithms for prediction. This application is built using Java, Play framework, Bootstrap and IntelliJ IDE. Java is used in the backend to create a model that maps the input and output data based on any of the above given learning algorithms while Play Framework and Bootstrap are used to display content in frontend. Play framework is used because it is based on web-friendly architecture. As a result it uses predictable, minimal resources (CPU, memory, threads) for highly scalable applications. It is also developer friendly where changes can be made in the code and hitting the refresh button in browser will update the interface. Bootstrap is used to style the web application and it adds responsiveness to the interface with added feature of cross-browser compatible designs. As a result, the website is responsive and fits the screen size of computer. Using this web application users can predict features, category of the entity in the data sets. User needs to submit data set where each row in the data set must represent attributes of the entity. Once data is submitted the application builds a model using user selected machine learning algorithm logistic regression, linear regression or backpropagation. After the model is developed in second stage of the application user can submit attributes of the entity whose category needs to predicted. The predicted category will be displayed on screen in third stage of the application. The interface of the application shows its current active stage. These models are built using 80% of submitted dataset and remaining 20% is used to test the accuracy of the application. In this thesis, prediction accuracy of each algorithm is tested using UCI breast cancer data sets. When tested on breast cancer data with 10 attributes both Logistic Regression and Backpropagation gave 98.5% accuracy. And when tested on breast cancer data with 31 attributes Logistic Regression gave 92.85% accuracy and Backpropagation gave 94.64%.