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.

“The World Had Forgotten about Us”: Heterotopian Resistance in Richard Flanagan’s Wanting and Lloyd Jones’s Mister Pip

This article explores how the different forms of heterotopias present in Richard Flanagan’s Wanting (2008) and Lloyd Jones’s Mister Pip (2006) articulate problematic identity politics and cultural memory. In Wanting, the collocation of Mathinna’s story with that of the lost Franklin expedition offers a form of reclaiming. This article argues that Flanagan’s novel moves from heterotopias of deviation to a crisis heterotopia, displacing and debunking the compensation function of the colonial heterotopia to highlight the crushing of Aboriginal identity. This shifting heterotopia is doubled by Mathinna’s heterotopic carceral body, that is, body as confined space, which qualifies the act of reclaiming. In Mister Pip, heterotopias concern cultural memory as the island of Bougainville, secluded from the rest of the world, turns into the repository of the villagers’ culture juxtaposed with the reading of Charles Dickens’s Great Expectations (1860–1861). This article argues that Jones’s creation of a palimpsestic heterotopia allows him to resist Eurocentric views as well as to actualize postcolonial concepts. Jones’s novel calls for a dynamic appropriation of literature. Matilda’s ‘Pacific version’ of Pip’s story reflects the cracks in the Victorian and contemporary exploitations of the island. Readers’ immersions in these heterotopias do not provide an escape from but a thoughtful commitment to the past.

Can Polyphenols Inhibit Ferroptosis?

Polyphenols, a diverse group of naturally occurring molecules commonly found in higher plants, have been heavily investigated over the last two decades due to their potent biological activities—among which the most important are their antioxidant, antimicrobial, anticancer, anti-inflammatory and neuroprotective activities. A common route of polyphenol intake in humans is through the diet. Since they are subjected to excessive metabolism in vivo it has been questioned whether their much-proven in vitro bioactivity could be translated to in vivo systems. Ferroptosis is a newly introduced, iron-dependent, regulated mode of oxidative cell death, characterized by increased lipid peroxidation and the accumulation of toxic lipid peroxides, which are considered to be toxic reactive oxygen species. There is a growing body of evidence that ferroptosis is involved in the development of almost all chronic diseases. Thus, ferroptosis is considered a new therapeutic target for offsetting many diseases, and researchers are putting great expectations on this field of research and medicine. The aim of this review is to critically analyse the potential of polyphenols to modulate ferroptosis and whether they can be considered promising compounds for the alleviation of chronic conditions.

Player/User Types for Gamification

Gamification has created great expectations for education and has become a trend in education. It is not an easy process to integrate gamification into educational environments. The design and development phases of gamification are very important. Therefore, it is necessary to follow a model that will guide the process in gamification designs. Individual differences among students are an important factor affecting their learning performance. In this context, considering student characteristics will increase the effect of gamification in education. Personalized gamification designs that meet the needs and expectations of students will be more effective than one-size-fits-all designs. It can benefit from player/user types in gamification designs to identify individual differences. This chapter aims to discuss player/user types in relation to gamification in the context of education.

Great Expectations – Sluggish Performance

Rec.: Katja Hoyer, The Rise and Fall of German Empire, Stroud: The History Press, 2021.

Field Experience from Robot Tests on Drill Floor and Pipe Deck

The objective of this paper is to present results from extensive testing of fully robotic drilling and pipe handling operations in the drilling industry, including several robots and tests on both land and offshore. Throughout the last eight years, all-electric, heavy-duty drilling and pipe handling robots of up to seven tons capacity have been tested and piloted on dedicated test facilities, land rigs and offshore rigs. The robotic equipment includes drill floor robot, pipe handler robot, robotic roughneck and pipe deck robot with the purpose of removing the people from the drill floor, derrick and the pipe deck. The testing and qualification have been conducted in phases and in a cooperation between equipment manufacturer, rig contractors and operating companies. The industry has great expectations with the introduction of robotics for red zone management and eliminating all manual operations and human exposure to heavy machinery. Expected value would be a substantial saving in rig days due to fast, precise and consistent operations and removal of people out of harm's way. In addition to improved safety, reduced OPEX, less downtime and faster installation, the robotics systems lower the noise and the carbon footprint due to higher energy efficiency and less GHG emissions. Also, the precise motion control of robots enables digitalization of the drilling process. The testing of robots in drilling applications has been done with the purpose of testing and qualifying the technology, as well as gaining experience with performance, reliability, maintainability, safety, and value to the users. This paper presents performance data from robot operations including both single robots and full system operations, such as tripping and stand-building. Reliability of electric robots in hostile environment is analyzed with regards to field experience from land rig drilling and offshore operations. Finally, the value to the users is substantiated. The paper provides unique results and experience from the longest and broadest tests of heavy-duty all-electric robots in the drilling environment. It therefore provides valuable input for decisions of future use of industrialized robots in the oil and gas upstream industry.