<span lang="EN-US">Automated machine learning is a promising approach widely used to solve classification and prediction problems, which currently receives much attention for modification and improvement. One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm.  As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. This paper presents the effect of different pairs of mutation and crossover rates on the automated machine learning performances that tested on different types of datasets. The finding can be used to support the theory that higher crossover rates used to improve the algorithm accuracy score while lower crossover rates may cause the algorithm to converge at earlier stage.</span>

No abstract

Probability is a study of the rules that offers the foundational statistics. This sets out the investigation where students' understanding of counting rules and its probability were explored using 20 items was developed and administered Statistics course. Data were captured through an interactive e Edmodo.com and analyzed using Winsteps 3.81.0. The results from the Wright map showed that 83.8% of the students have while 16.2% of the students need to be given more attention on the topic. The study was also able to show that the items can be replicated in other sample

Increasing housing prices in Perak has made it difficult for homebuyers to own affordable housing. Housing affordability ensures that housing provided is affordable for every income group, especially the low and middle-income groups. It has brought the government and housing developers’ attention the issue of housing affordability by supplying public low-cost housing schemes.

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