TY  - JOUR
VL  - 12
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130099158&doi=10.46338%2fijetae0522_03&partnerID=40&md5=e5a7abe963875b633f64fd41d7df5739
JF  - International Journal of Emerging Technology and Advanced Engineering
A1  - Baharun, N.
A1  - Razi, N.F.M.
A1  - Masrom, S.
A1  - Yusri, N.A.M.
A1  - Rahman, A.S.A.
Y1  - 2022///
ID  - scholars16772
N2  - 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. © 2022 Baishideng Publishing Group Inc
IS  - 5
EP  - 27
SN  - 22502459
PB  - IJETAE Publication House
SP  - 15
TI  - Auto Modellingfor Machine Learning: A Comparison Implementation between Rapid Miner and Python
N1  - cited By 5
AV  - none
ER  -