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ATP Tennis stands for the “The Association of Tennis Professionals” which is the primary governing body for male tennis players. ATP was formed in Sep 1972 for professional tennis players. A study has been done on tennis players’ datasets to implement supervised machine learning techniques to illustrate match data and make predictions. An appropriate dataset has been chosen, data cleaning has been implemented to extract anomalies, data is visualized via plotting methods in R language and supervised machine learning models applied. The main models applied are linear regression and decision tree. Results and predictions have been extracted from the applied models. In the linear regression model, the correlation is calculated to find the relation between dependent and independent variables, furthermore the results and prediction are extracted from the linear regression model. Also, three hypotheses are applied for multiple linear regression model. The decision tree modeled the best of 3 or best of 5 sets of matches and predicted which set of matches would be considered best. Keywords: Machine Learning, supervised learning, linear regression, decision tree, R language, Tennis, ATP.

happiness is a dream goal to be achieved by governments and individuals and it can be considered as a proper measure of social development progress. The purpose of this paper is to conduct a study on World happiness report dataset, to classify the most critical variables regarding the life happiness score. The strong evidence of the identified main features classified from the outcomes of applying the supervised machine learning approaches using the Neural Network training model and the OneR models in classifications and feature selection. The trained model used in predictions revealed the insights derived from applying the data analysis, where the study found out that the GDP per capita is the critical indicator of life happiness score as well as the health life expectancy is the second primary feature. Findings from study evaluated using different performance metrics such as accuracy and confusion matrix to prove the insights gained from the data. Keywords: world happiness, machine learning, Neural Network.

In this article, we address a new model for the scaling law heat conduction problem by using the scaling law vector calculus associated with the Korcak scaling law. The scaling law heat conduction equations are discussed in detail. The scaling law vector calculus formulas are proposed as an efficiently mathematical tool to describe the Korcak scaling -law phenomena in heat transport system.

Web scraping or web crawling refers to the procedure of automatic extraction of data from websites using software. It is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where data isn’t provided in machine readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry. Based on the result it has been concluded that Web scraping is a highly useful tool in the information age, and an essential one in the modern fields. Multiple technologies are required to implement web scrapping properly such as spidering and pattern matching which are discussed. This paper is looking into what web scraping is, how it works, web scraping stages, technologies, how it relates to Business Intelligence, artificial intelligence, data science, big data, cyber securityو how it can be done with the Python language, some of the main benefits of web scraping, and what the future of web scraping may look like, and a special degree of emphasis is placed on highlighting the ethical and legal issues. Keywords: Web Scraping, Web Crawling, Python Language, Business Intelligence, Data Science, Artificial Intelligence, Big Data, Cloud Computing, Cybersecurity, legal, ethical.