Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

SSRN Electronic Journal ◽

10.2139/ssrn.3711309 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Filip Ferdinand ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

Learning Methods ◽

Comprehensive Review

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

IEEE Internet of Things Magazine ◽

10.1109/iotm.0022.2000002 ◽

2020 ◽

pp. 2-6

Author(s):

Sumi Helal ◽

Flavia C. Delicato ◽

Cintia B. Margi ◽

Satyajayant Misra ◽

Markus Endler

Keyword(s):

Machine Learning ◽

Data Science ◽

Challenges And Opportunities

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Machine Learning, Optimization, and Data Science

10.1007/978-3-030-64580-9 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Data Science

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Data science skills: Building partnership for efficient school curriculum delivery in Africa

Statistical Journal of the IAOS ◽

10.3233/sji-200693 ◽

2020 ◽

Vol 36 ◽

pp. 49-62

Author(s):

Nureni Olawale Adeboye ◽

Peter Osuolale Popoola ◽

Oluwatobi Nurudeen Ogunnusi

Keyword(s):

Machine Learning ◽

Data Science ◽

Open Data ◽

School Curriculum ◽

Global Perspective ◽

Special Focus ◽

African Countries ◽

Tertiary Institutions ◽

Data Description ◽

Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

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Enabling Machine Learning Applications in Data Science

10.1007/978-981-33-6129-4 ◽

2021 ◽

Keyword(s):

Machine Learning ◽

Data Science ◽

Machine Learning Applications

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A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641034 ◽

2020 ◽

Vol 64 (1) ◽

pp. 129-133

Author(s):

Katherine Darveau ◽

Daniel Hannon ◽

Chad Foster

Keyword(s):

Machine Learning ◽

Human Factors ◽

Human Error ◽

Data Science ◽

Aircraft Engine ◽

Rule Based ◽

Root Cause ◽

Textual Data ◽

Safety Event

There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.

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