Open AccessAuto Modelling for Machine Learning: A Comparison Implementation between RapidMiner and Python
Author(s) -
Norhayati Baharun,
Nor Faezah Mohamad Razi,
Suraya Masrom,
Nor Ain Mohamad Yusri,
Abdullah Sani Abd Rahman
Publication year - 2022
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae0522_03
Subject(s) - python (programming language) , artificial intelligence , machine learning , computer science , hospitality , software deployment , random forest , business intelligence , data science , software engineering , data mining , tourism , programming language , political science , law
Recently, business intelligence is creating many changes and challenges to the business models of many industries globally. While a bigger impact has been reported on business intelligence models, there has been very little effort that investigates the deployment of business intelligence models based on auto modelling approaches of machine learning. Design and implement a machine learning business intelligence model involved a series of hassle tasks and was mostly time-consuming for an inexpert data scientist. Therefore, this paper presents different approaches to auto modelling machine learning provided by RapidMiner and Python machine learning software tools. To compare the results of modelling from the different approaches, the Airbnb hospitality dataset has been used as a case study for predicting the hospitality prices. The results show that Random Forest Regressors have been very promising to produce a high percentage of accuracy score with all the auto modelling machine learning Keywords— Machine Learning, Auto Modelling, Price Prediction, TPOT Python, RapidMinerja