Prediction of Credit Card Approval

Credit risk as the board in banks basically centers around deciding the probability of a customer's default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.

Engagement during Pandemic Teaching: Report of The Eaai-21 Panel on Teaching Online and Blended AI Courses

Three panelists, Ashok Goel, Ansaf Salleb-Aouissi and Mehran Sahami explain some of the tools and techniques they used to keep their students engaged during virtual instruction. The techniques include the desire to take one’s passion for the learning materials to the virtual classroom, to ensure teacher presence, provide for cognitive engagement with the subject and facilitate social interactions. Finally, we learn about tools used to manage a large online course so as to move the many active learning exercises to the virtual classroom.

Effect of Computer Assisted Audit Tools on Corporate Sustainability

The literature is fertile in studies that examine the determinants of internal and external auditors’ adoption of computer-assisted audit tools and techniques (CAATs), often ignoring their practical effects on audit quality and organizational performance. This study provides novel evidence on the type of CAATs used by internal auditors, tests the effect of their adoption on corporate sustainability, and explores the moderating effect of organizational characteristics. In this paper, we used data from Portuguese internal auditors collected through a survey, whose research hypotheses were analyzed by the partial least squares–structural equation modeling technique. We found that internal auditors use CAATs moderately in the exercise of their tasks. The results of our study show that there is a strong and positive effect of the use of CAATs by internal auditors on fraud detection in the purchase-to-pay business process, and that the intensity of this relationship is not influenced by the type and size of the entity. This study complements previous research and provides support to practitioners’ decisions that can boost the use of CAATs in internal auditing to make organizations more sustainable.

Potential Cyber Threats, Vulnerabilities, and Protections of Unmanned Vehicles

This study seeks to contribute to the literature by presenting a discussion of potential cyber risks and precautionary measures concerning unmanned vehicles as a whole. In this study, Global Navigation Satellite System (GNSS) spoofing, jamming, password cracking, Denial-of-Service (DoS), injecting malware, and modification of firmware are identified as potential cyberattack methods against unmanned vehicles. Potential deterrents against the aforementioned cyberattack methods are suggested as well. Illustrations of such safeguards include creating an architecture of the multi-agent system, using solid-state storage components, applying distributed programming tools and techniques, implementing sophisticated encryption techniques for data storage and transmission, deploying additional sensors and systems, and comparing the data received from different sensors.

Big Data Analytics in Undergraduate Advertising Curricula

The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.

Big Data Visualization Tools and Techniques

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.