The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.

In recent years, the collection and sharing of individuals' private data has become commonplace in many industries. Local differential privacy (LDP) is a rigorous approach which uses a randomized algorithm to preserve privacy even from the database administrator, unlike the more standard central differential privacy. For LDP, when applying noise directly to high-dimensional data, the level of noise required all but entirely destroys data utility. In this paper we introduce a novel, application-agnostic privatization mechanism that leverages representation learning to overcome the prohibitive noise requirements of direct methods, while maintaining the strict guarantees of LDP. We further demonstrate that this privatization mechanism can be used to train machine learning algorithms across a range of applications, including private data collection, private novel-class classification, and the augmentation of clean datasets with additional privatized features. We achieve significant gains in performance on downstream classification tasks relative to benchmarks that noise the data directly, which are state-of-the-art in the context of application-agnostic LDP mechanisms for high-dimensional data. The collection of personal data is ubiquitous, and unavoidable for many in everyday life. While this has undeniably improved the quality and user experience of many products and services, evidence of data misuse and data breaches (Sweeney, 1997; Jolly, 2020) have brought the concept of data privacy into sharp focus, fueling both regulatory changes as well as a shift in personal preferences. The onus has now fallen on organizations to determine if they are willing and able to collect personal data under these changing expectations.

These venture bets on startups that "returned the fund," making firms and careers, were the result of research, strong convictions, and patient follow-through. Here are the stories behind the biggest VC home runs of all time. In venture capital, returns follow the Pareto principle -- 80% of the wins come from 20% of the deals. Great venture capitalists invest knowing they're going to take a lot of losses in order to hit those wins. Chris Dixon of top venture firm Andreessen Horowitz has referred to this as the "Babe Ruth effect," in reference to the legendary 1920s-era baseball player. Babe Ruth would strike out a lot, but also made slugging records. Likewise, VCs swing hard, and occasionally hit a home run. Those wins often make up for all the losses and then some -- they "return the fund." "If you do the math around our goal of returning the fund with our high impact companies, you will notice that we need these companies to exit at a billion dollars or more," he wrote.

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology. There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

In October 2016, two leading P2P platforms in the U.S. -- Lending Club and Prosper -- announced a new increase in interest rates for lower-grade loans. The decision was made in order to sustain investor demand, as the model platforms are operating under challenges faced during the last months due to compliance issues with Lending Club and the general turbulence of the P2P lending industry. However, keeping the investor demand stable is not the only reason for recent changes -- as Lending Club announced, delinquencies are growing, especially when it comes to high-risk loans. October 2016 was not the first time P2P platforms changed their interest rates that year. Initially, the decision was caused by the Federal Reserve to raise interest rates in response to signs of a stable and strengthening economy in December 2015, following a period of low rates designed to promote quicker economic growth in the U.S. and worldwide after the financial crisis.

Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. The science behind machine learning is interesting and application-oriented. Many startups have disrupted the FinTech ecosystem with machine learning as their key technology. There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications.

There is a rising debate about recent Artificial Intelligence (AI) improvements -- is it a peril for humanity?

Cleaning and preparing data is a critical first step in any machine learning project. In this blog post, Dataquest student Daniel Osei's takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning. This post is based on a Dataquest'Monthly Challenge', where our students are given a free-form task to complete. After first reading about Machine Learning on Quora in 2015, Daniel became excited at the prospect of an area that could combine his love of Mathematics and Programming. After reading this article on how to learn data science, Daniel started following the steps, eventually joining Dataquest to learn Data Science with us in in April 2016. We'd like to thank Daniel for his hard work, and generously letting us publish this post.

Jean took NYC Data Science Academy 12 week full time Data Science Bootcamp pr... between Sept 23 to Dec 18, 2015. The post was based on his first class project(due at 2nd week of the program). Check out the full report here! You will find all the details of the code behind the analysis and the visualisations. For this project, we wish to present and explore the data provided by Lending Club.

Random forest model using Lending Club public dataset shows opportunity to improve adjusted return by 2.75% Arimo recently performed a study using a public dataset provided by Lending Club with the goal of showing how machine learning could improve investor returns. To do this we used the PredictiveEngine component of our Data Intelligence Platform, which provides the ability to easily build a variety of predictive machine learning models which scale transparently when deployed on distributed parallel computing platforms. Lending Club is an online peer-to-peer lending company that connects borrowers with investors who have capital to lend. When a loan application is submitted by a borrower, Lending Club reviews and decides whether to offer a loan at a risk-adjusted rate or to reject the application. As of the 3rd quarter of 2015, more than 12 billion in loans have been issued through Lending Club.