Introduction to Data Science and Machine Learning

Sud, Keshav; Erdoğmuş, Pakize; Kadry, Seifedine

doi:10.5772/intechopen.77469

2020

DOI: 10.5772/intechopen.77469

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Introduction to Data Science and Machine Learning

Keshav Sud¹,

Pakize Erdoğmuş²,

Seifedine Kadry³

Abstract: Dr Sud started his research in the field of data science and AI as an undergraduate junior in 2003. He later obtained his MSc and PhD degrees developing a novel mathematical approach for forecasting performance of complex systems from the University of Illinois at Chicago. Dr Sud has worked for fortune 500 companies such as Caterpillar, Volvo, Amazon, and currently holds the role of Senior Manager of Data Science at Keurig Dr Pepper Inc., where he is leading the development of recommendation engines. Dr Sud is… Show more

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Cited by 3 publications

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An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Yalan

Tang

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Language translation is highly needed in the increasingly networked world. The writing of English essays is an essential competency for higher education students. For non-English students in China, this is especially important. They often represent large numbers of Chinese English as second language students. Therefore this paper introduced Deep Structured Learning with Natural language processing (DSL-NLP) has been proposed to convert the Chinese language to the English language for student learning. An automatic input tool for writing assistance will be beneficial in building the proposed DSL-NLP method. The relationship between textual attributes and input from human teachers and provide characteristics feedback system. This study explored natural language processing (NLP) to determine the interpretative actions required for a successful understanding of the literary text. NLP attempts to assist in achieving consistency in applied linguistics, improving classroom communication, optimizing learner attitudes and motivation, raising personality, facilitating personal development, and even changing students' perspectives toward life. Thus the experimental results show the DSL-NLP improves student learning in the English language, enhances student performance in education, and effectively increases the student career

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An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Yalan

Tang

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

Xie

Luo

2022

Risks

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The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules.

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Dirty Data between Errors and Their Handling—A Firsthand Experience in Solving Dirty Data from Within

Bukhatwa¹,

Laarfi²,

Salem³

2023

IJIS

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Managing large amounts of data is becoming part of everyday life in most organizations. Handling, analyzing, searching, and making predictions from big data is becoming the norm for many organizations of many interests. Big data provides the foundations for more benefits and higher values to be extracted from big data. As big data comes with countless benefits, it also comes with many challenges to fulfilling its expectations. Some of those problems haunting big data banks are being termed dirty data. This paper focuses on dirty data while working on an organization's natural live information system. The author was responsible for studying and analyzing a faltering information system and planning and carrying out the required solutions and fixes. The importance of the work carried out lies in the high level of dirty data observed in the system. Therefore, this paper is based on the part of dirty data-the paper focuses on how the team suffered from dirty data and how it was dealt with.

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Introduction to Data Science and Machine Learning

Cited by 3 publications

References 6 publications

An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

Dirty Data between Errors and Their Handling—A Firsthand Experience in Solving Dirty Data from Within

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