Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms

Abstract

The use of machine learning algorithms in last decade has been enormously increased. It has opened the door to several opportunities in various fields of research and business. However, identifying the appropriate algorithm for a particular program has always been an enigma, and that necessitates to be solved ere the development of any machine learning system. Let’s take the example of the weather forecasting prediction system, it is used to identify the future weather prediction of a particular country or continent. Now, it is a daunting task to find the right algorithm or model for such a purpose that can predict accurate values. There are several other systems such as recommendation systems, sales prediction of a mega-store, stock prediction systems, or predicting what are the chances of a driver meeting an accident based on his past records and the road they’ve taken. These problem statements require to be resolved using the most suitable algorithm and identifying them is a necessary task. The objective of proposed system is to develop an interface that can be used to display the result matrix of different machine learning algorithms after being exposed to different datasets with different features. The system compares a set of machine learning algorithms while determining the appropriate algorithm for the selected predictive system using the required data sets. Stock market, earth and sales forecasting data is used for analysis. For experimental performance analysis several technologies and tools are used including Python, Django, Jupyter Notebook, Machine Learning, Data Science methodologies, etc. The comparative performance analysis of best known five time series forecasting machine learning algorithms viz. linear regression, K—nearest neighbor, Auto ARIMA, Prophet, and Support Vector Machine is done.