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Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 18641 Packt Video
Data Mining - Credit Scoring empleando Redes Bayesianas
 
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Predicción de si un cliente será buen pagador o no empleando Redes Bayesianas. El sistema de Credit Scoring.
Views: 7 TODO Mining
Credit Card Default - Data Mining
 
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A data mining project as part of requirements for Applied Data Mining at Rockhurst University. This presentation explores the mining of data utilizing R programming. Methods used are Decision Tree and Linear Regression models to predict the outcome of whether a customer will default on their next monthly credit card payment.
Views: 1939 Jonathan Walker
Can credit scores for the creditless be determined by cellphone data? | Eric Mibuari | TED Institute
 
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70% of African households own cellphones but less than 35% have access to credit. Mining non-traditional forms of credit using available technologies, Eric Mibuari asks if this "mobile continent" can use cellphones to access what they need most - loans. [email protected] was a TED-curated event showcasing speakers from across the IBM community. Produced in partnership with IBM, the event brought to light the often-overlooked. Examining social structures and sustainable approaches, [email protected] explored the necessity of invention to build a better world. Visual Credits 4:36 Photo of man: adapted from Emmanuel R Lacoste / Shutterstock.com Photo of woman: adapted from bikeriderlondon / Shutterstock.com 5:33 Photo: Poverty Weighed in a Can / IBM Research - Africa / Flickr 7:21 Photo: Poverty Weighed in a Can / IBM Research - Africa / Flickr About the TED Institute: We know that innovative ideas and fresh approaches to challenging problems can be discovered inside visionary companies around the world. The TED Institute helps surface and share these insights. Every year, TED works with a group of select companies and foundations to identify internal ideators, inventors, connectors, and creators. Drawing on the same rigorous regimen that has prepared speakers for the TED main stage, TED Institute works closely with each partner, overseeing curation and providing intensive one-on-one talk development to sharpen and fine tune ideas. Learn more at http://www.ted.com/ted-institute Follow TED Institute on Twitter @TEDPartners Subscribe to our channel: https://www.youtube.com/user/TEDInstitute
Views: 3804 TED Institute
Step by step guide how to build a real-time credit scoring system using Apache Spark Streaming
 
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This video shows step by step how real-time credit scoring application can be built using machine learning and Apache Spark streaming.
Views: 3806 Mariusz Jacyno
FRM: Credit Scoring Model Development
 
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Download FRM Question Bank: http://www.edupristine.com/ca/courses/frm-program/ Learn how to develop a model by follow cyclic process given in above video. About EduPristine: Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading Training provider for Finance Certifications like CFA, PRM, FRM, Financial Modeling etc. EduPristine strives to be the trainer of choice for anybody looking for Finance Training Program across the world. Subscribe to our YouTube Channel: http://www.youtube.com/subscription_center?add_user=edupristine Visit our webpage: http://www.edupristine.com/ca
Views: 2839 EduPristine
Next Generation Credit Decisions: Where big data outperforms the credit bureaus
 
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Erki Kert, CEO & Big Data Scientist presents at the Auto Captives Summit. Erki shows that the predictive power of big data scoring offers a 30% improvement in scoring accuracy over positive credit information.
Views: 1694 White Clarke Group
What is CREDIT SCORE? What does CREDIT SCORE mean? CREDIT SCORE meaning & explanation
 
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What is CREDIT SCORE? What does CREDIT SCORE mean? CREDIT SCORE meaning - CREDIT SCORE definition - CREDIT SCORE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of the person. A credit score is primarily based on a credit report information typically sourced from credit bureaus. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. Lenders also use credit scores to determine which customers are likely to bring in the most revenue. The use of credit or identity scoring prior to authorizing access or granting credit is an implementation of a trusted system. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Credit scoring also has much overlap with data mining, which uses many similar techniques. These techniques combine thousands of factors but are similar or identical.
Views: 94 The Audiopedia
Machine Learning - Simple Overview & How it used in Credit Risk Modeling in a Bank
 
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This webinar was delivered by a Machine Learning expert and enthusiast with 17+ years of experience in analytics and related domains.
Views: 5126 IvyProSchool
SVM & Logistic Reg CaseStudy Credit Risk Default Python
 
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This is a detailed Case Study on SVM & Logistic Regression in Python. Missing Value imputation, finding important variables (variable importance) is coverd with clear expalnation. C and Gamma Values in SVM is also discussed. How to arrive at the best C & Gamma Values using Grid search
▶️$168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH
 
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▶️Learn to Leverage your credit and make your credit it work for you. $168,361 How To Get Higher Limit Credit Cards, Learn the SECRET Learn About CARDMATCH Check out CreditCards.com for CARDMATCH How to Remove Negative Credit Items / Collections + Credit Inquiries + Sample Letters PROVIDED, FREE DYI CREDIT REPAIR Link to Free Federal Credit Reports www.annualcreditreport.com Credit Repair Letter Provided by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlM3RISnFKMEJXaG8/view?usp=sharing Credit Inquiry Removal by RandomFix https://drive.google.com/file/d/0B8YhYO1fFwFlYU5BU2JFSzRJMVU/view?usp=sharing Cool information about credit score A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of an individual. A credit score is primarily based on a credit report information typically sourced from credit bureaus. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. Lenders also use credit scores to determine which customers are likely to bring in the most revenue. The use of credit or identity scoring prior to authorizing access or granting credit is an implementation of a trusted system. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, landlords, and government departments employ the same techniques. Digital finance companies such as online lenders also use alternative data sources to calculate the creditworthiness of borrowers. Credit scoring also has much overlap with data mining, which uses many similar techniques. These techniques combine thousands of factors but are similar or identical. Give the Gift of Prime https://goo.gl/YJTEMn Thanks for your support. God Bless -RandomFIX www.RandomFIXWorld.com **If the video was helpful, remember to give it a and consider subscribing. New videos every Monday** How to get high limit credit cards fast good credit equal high credit limit cards
Views: 4921 RANDOMFIX
Credit Scorecard Modeling Using the Binning Explorer App - MATLAB Video
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Learn more about MATLAB: http://goo.gl/YKadxi Use the Binning Explorer app, a new app in Risk Management Toolbox™ to perform data binning and modeling. Use the built-in functions in MATLAB® together with Financial Toolbox™ to perform other processes in credit score modeling, allowing you to analyze consumer credit risk in an efficient manner.
Views: 1019 MATLAB
Portfolio Plus Prospector | Data Mining Analytics for Loan Business
 
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Cars on Credit relies on Portfolio Plus Prospector, an analytics and data mining tool, to run their car loan business effectively.
How Do Credit Card Issuers Detect Fraud? - Credit Card Insider
 
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Today's question is: How do credit card issuers detect fraud? Ask us your credit questions in the comments and find your next card at https://www.creditcardinsider.com/ Have you ever used your credit card out of town and received a phone call or email from your card issuer, asking about your recent purchases? This is part of the issuer's fraud detection system, which tries to find unusual patterns in your purchase record. Every week, John Ulzheimer answers YOUR credit questions. Email us, give us a call, or ask on live chat, and we may answer your question on YouTube! To learn about credit scores, credit reports, managing debt, and how credit cards work, check out our learn section at https://www.creditcardinsider.com/learn/ If you're looking for a credit card, start your search at https://www.creditcardinsider.com/credit-cards/ Join our community! https://plus.google.com/+Creditcardinsider https://twitter.com/cardinsider https://www.facebook.com/CreditCardInsider https://www.creditcardinsider.com
Views: 14303 Credit Card Insider
RapidMiner Tutorial - Modeling and Scoring  (Data Mining and Predictive Analytics System)
 
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A tutorial discussing modeling and scoring in RapidMiner. RapidMiner is an open source system for data mining, predictive analytics, machine learning, and artificial intelligence applications. For more information: http://rapid-i.com/ Brought to you by Rapid Progress Marketing and Modeling, LLC (RPM Squared) http://www.RPMSquared.com/
Views: 6652 Predictive Analytics
What is Credit Score?
 
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A short briefing on introduction of Credit Score. Special thanks to our lecturer, Dr. Jastini Mohd Jamil References: Credit score. (n.d.). In Wikipedia. Retrieved March 13, 2014, from http://en.wikipedia.org/wiki/Credit_score Credit score definition. (n.d.). In Investopedia. Retrieved March 13, from, http://www.investopedia.com/terms/c/credit_score.asp Credit Score Example. (n.d.). Retrieved from http://www.creditprofile.transunion.ca/popup/scoreExample.jsp?popup=true Credit scoring. (n.d.). Retrieved March 13, 2014, from http://epic.org/privacy/creditscoring/ Koh, H.C., Tan, W.C., & Goh, C.P. (2006). A two-step method to construct credit scoring models with data mining techniques. International Journal of Business and Information, 1(1), 96-118. Retrieved March 13, 2014, from http://www.knowledgetaiwan.org/ojs/files/Vol1No1/Paper_5.pdf Koh, H. C., Tan, W. C., & Goh, C. P. (2006). A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques. International Journal of Business and Information, Volume 1(Number 1), 96-118. Mester, L. J. (1997, September/ October ). What's the Point of Credit Scoring? Business Review, 3-16. What's in my FICO Score. (n.d.). Retrieved March 13, 2014, from http://www.myfico.com/crediteducation/whatsinyourscore.aspx
Views: 122 Stella Khaw
Demo on Logistic Regression using IBM SPSS : Credit Scoring
 
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Logistic Regression Tutorials - https://goo.gl/kCjMpW Credit scoring using logistic regression on IBM SPSS. We demonstrate how to maximize profits by intelligently deciding who gets a loan and who gets rejected. To download the dataset http://www.learnanalytics.in/datasets/Credit_Scoring.zip For more information, check out our website www.learnanalytics.in and our blog section on www.learnanalytics.in/blog , or drop us an email at [email protected]
Views: 12932 Learn Analytics
Repair Credit Score In 37 Days
 
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A credit score is a numerical expression based on a statistical analysis of a person's credit files, to represent the creditworthiness of that person. A credit score is primarily based on credit report information, typically sourced from credit bureaus. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. The use of credit or identity scoring prior to authorizing access or granting credit is an implementation of a trusted system. Credit scoring is not limited to banks. Other organizations, such as mobile phone companies, insurance companies, employers, landlords, and government departments employ the same techniques. Credit scoring also has a lot of overlap with data mining, which uses many similar techniques.
Views: 127 IncreaseCreditScore
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
 
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Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners. Apply coupon code "YOUTUBE10" to get this course for $10 http://bit.ly/2Mi5IuP Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Kamanja: An Open Source Real Time System for Scoring Data Mining Models, Greg Makowski 20150727
 
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Greg Makowski, Director of Data Science, LigaDATA This talk will start with a number of complex data real-time use cases, such as a) complex event processing, b) supporting the modeling of a data mining department and c) developing enterprise applications on Apache big-data systems. While Hadoop and big data has been around for a while, banks and healthcare companies tend not to be early IT adopters. What are some of the security or roadblocks in Apache big data systems for such industries with high requirements? Data mining models can be trained in dozens of packages, but what can simplify the deployment of models regardless of where they were trained or with what algorithm? Predictive Modeling Markup Language (PMML), is a type of XML with specific support for 15 families of data mining algorithms. Data mining software such as R, KNIME, Knowledge Studio, SAS Enterprise Miner are PMML producers. The new open-source product, Kamanja, is the first open-source, real-time PMML consumer (scoring system). One advantage of PMML systems is that it can reduce time to deploy production models from 1-2 months to 1-2 days - a pain point that may be less obvious if your data mining exposure is competitions or MOOCs. Kamanja is free on Github, supports Kafka, MQ, Spark, HBase and Cassandra among other things. Being a new open-source product, initially, Kamanja supports rules, trees and regression. I will cover an architecture of a sample application using multiple real-time open source data, such as social network campaigns and tracking sentiment for the bank client and its competitors. Other real-time architectures cover credit card fraud detection. A brief demo will be given of the social network analysis application, with text mining. An overview of products in the space will include popular Apache big data systems, real-time systems and PMML systems. For more details: Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions http://kamanja.org/ http://www.meetup.com/SF-Bay-ACM/events/223615901/ http://www.sfbayacm.org/event/kamanja-new-open-source-real-time-system-scoring-data-mining-models Venue sponsored by eBay, Food and live streaming sponsored by LigaDATA, San Jose, CA, July 27, 2015 Chapter Chair Bill Bruns Data Science SIG Program Chair Greg Makowski Vice Chair Ashish Antal Volunteer Coordinator Liana Ye Volunteers Joan Hoenow, Stephen McInerney, Derek Hao, Vinay Muttineni Camera Tom Moran Production Alex Sokolsky Copyright © 2015 ACM San Francisco Bay Area Professional Chapter
Le Data Mining en 35 Leçons - Session 3 : Introduction à des données de risque de crédit
 
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Cette troisième session de notre série de didacticiels "Le Data Mining en 35 Leçons avec STATISTICA" présente un jeu de données de risque de crédit qui sera utilisé dans un grand nombre de sessions ultérieures. Il est fortement conseillé de visionner ce tutoriel afin de bien comprendre la problématique et le contexte de ce projet de Crédit Scoring qui sera traité à l'aide de différents outils graphiques, de gestion des données, et algorithmes de data mining proposés dans le logiciel STATISTICA Data Miner.
Views: 9075 Statistica France
Getting Started with SAS Enterprise Miner: Scoring New Data
 
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http://support.sas.com/software/products/miner/index.html Chip Robie of SAS presents the sixth in a series of six "Getting Started with SAS Enterprise Miner 13.2" videos. This sixth video demonstrates scoring new data with SAS Enterprise Miner. For more information regarding SAS Enterprise Miner, please visit http://support.sas.com/software/products/miner/index.html SAS ENTERPRISE MINER SAS Enterprise Miner streamlines the data mining process so you can create accurate predictive and descriptive analytical models using vast amounts of data. Our customers use this software to detect fraud, minimize risk, anticipate resource demands, reduce asset downtime, increase response rates for marketing campaigns and curb customer attrition. LEARN MORE ABOUT SAS ENTERPRISE MINER http://www.sas.com/en_us/software/analytics/enterprise-miner.html SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNEL http://www.youtube.com/subscription_center?add_user=sassoftware ABOUT SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 75,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world The Power to Know.® VISIT SAS http://www.sas.com CONNECT WITH SAS SAS ► http://www.sas.com SAS Customer Support ► http://support.sas.com SAS Communities ► http://communities.sas.com Facebook ► https://www.facebook.com/SASsoftware Twitter ► https://www.twitter.com/SASsoftware LinkedIn ► http://www.linkedin.com/company/sas Google+ ► https://plus.google.com/+sassoftware Blogs ► http://blogs.sas.com RSS ►http://www.sas.com/rss
Views: 22075 SAS Software
Predicting Default Payments of Credit Card Clients in Taiwan by Zhiying Xie&Yongxin Zhu
 
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Machine Learning 2017 final project: Predicting Default Payments of Credit Card Clients in Taiwan by Zhiying Xie & Yongxin Zhu
Credit Scoring Nivel 1
 
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Objetivos y beneficios del curso.
Views: 2715 Fermac Risk
Credit Classification using Decision Trees
 
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Training on Credit Classification using Decision Trees by Vamsidhar Ambatipudi
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning
 
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 1244 The Audiopedia
Scoring a datase with Excel data mining add-ins
 
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Describes how to score a dataset using excel add ins
Views: 1092 Monica Tremblay
Credit Scoring & R: Reject inference, nested conditional models, & joint scores
 
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Credit scoring tends to involve the balancing of mutually contradictory objectives spiced with a liberal dash of methodological conservatism. This talk emphasises the craft of credit scoring, focusing on combining technical components with some less common analytical techniques. The talk describes an analytical project which R helped to make relatively straight forward. Ross Gayler describes himself as a recovered psychologist who studied rats and stats (minus the rats) a very long time ago. Since then he has mostly worked in credit scoring (predictive modelling of risk-related customer behaviour in retail finance) and has forgotten most of the statistics he ever knew. Credit scoring involves counterfactual reasoning. Lenders want to set policies based on historical experience, but what they really want to know is what would have happened if their historical policies had been different. The statistical consequence of this is that we are required to build statistical models of structure that is not explicitly present in the available data and that the available data is systematically censored. The simplest example of this is that the applicants who are estimated to have the highest risk are declined credit and consequently, we do not have explicit knowledge of how they would have performed if they had been accepted. Overcoming this problem is known as 'reject inference' in credit scoring. Reject inference is typically discussed as a single-level phenomenon, but in reality there can be multiple levels of censoring. For example, an applicant who has been accepted by the lender may withdraw their application with the consequence that we don't know whether they would have successfully repaid the loan had they taken up the offer. Independently of reject inference, it is standard to summarise all the available predictive information as a single score that predicts a behaviour of interest. In reality, there may be multiple behaviours that need to be simultaneously considered in decision making. These may be predicted by multiple scores and in general there will be interactions between the scores -- so they need to be considered jointly in decision making. The standard technique for implementing this is to divide each score into a small number of discrete levels and consider the cross-tabulation of both scores. This is simple but limited because it does not make optimal use of the data, raises problems of data sparsity, and makes it difficult to achieve a fine level of control. This talk covers a project that dealt with multiple, nested reject inference problems in the context of two scores to be considered jointly. It involved multivariate smoothing spline regression and some general R carpentry to plug all the pieces together.
Views: 5529 Jeromy Anglim
Artificial Intelligence Meets Credit Scoring
 
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Our Jaywing experts explore the challenges and opportunities that Artificial Intelligence presents for the credit scoring industry in this webinar recording
Views: 57 Jaywing plc
ROC Curves and Area Under the Curve (AUC) Explained
 
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An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 279072 Data School
How Facebook could ruin your credit score with credit companies spying on social media - TomoNews
 
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CHICAGO — Credit rating companies like FICO are mining social media profiles like Facebook to decide how worthy people are of credit cards and loans, the Chicago Tribune reported. This is just the latest in a variety of schemes that big data is using to keep an eye on digital activity and judge customer's financial habits. "If you look at how many times a person says 'wasted' in their (Facebook) profile, it has some value in predicting whether they're going to repay their debt," FICO chief executive Will Lansing told the Financial Times. Also included in the scoring are payment histories of everything from credit cards to movie rentals, and apparently moving house a lot is a bad sign because it might mean the person wasn't able to pay their rent. Jim Wehmann, executive vice-president for scores at FICO told the Financial Times: "We can now score the previously un-scoreable." So the moral of the story is pay your bills, and don't post status updates to Facebook about how wasted you were on payday when they turned off your utilities. ----------------------------------------­--------------------- Welcome to TomoNews, where we animate the most entertaining news on the internets. Come here for an animated look at viral headlines, US news, celebrity gossip, salacious scandals, dumb criminals and much more! Subscribe now for daily news animations that will knock your socks off. Visit our official website for all the latest, uncensored videos: http://us.tomonews.net Check out our Android app: http://bit.ly/1rddhCj Check out our iOS app: http://bit.ly/1gO3z1f Get top stories delivered to your inbox everyday: http://bit.ly/tomo-newsletter Stay connected with us here: Facebook http://www.facebook.com/TomoNewsUS Twitter @tomonewsus http://www.twitter.com/TomoNewsUS Google+ http://plus.google.com/+TomoNewsUS/ Instagram @tomonewsus http://instagram.com/tomonewsus -~-~~-~~~-~~-~- Please watch: "Crying dog breaks the internet’s heart — but this sad dog story has a happy ending" https://www.youtube.com/watch?v=4prKTN9bYQc -~-~~-~~~-~~-~-
Views: 6374 TomoNews US
Evaluating Classifiers: Kolmogorov-Smirnov Chart (K-S Chart)
 
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My web page: www.imperial.ac.uk/people/n.sadawi
Views: 10725 Noureddin Sadawi
SPSS Credit Scoring Model
 
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http://www.clearinsight.ca
Views: 3745 Clear Insight FPM
Data Mining
 
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Group 18
Views: 1421 supergroup18
BizViz Predictive Analysis - Credit Card Scoring Model
 
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Response Modeling/Credit Scoring/Credit Rating using Machine Learning Techniques on German Credit Data.
Views: 284 BDB
Predicting Peer-to-Peer Loan Default Using Data Mining Techniques - Callum Stevens
 
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Access a shiny web app at: https://callumstevens.shinyapps.io/logisticregression/ View full slideshow presentation at: https://goo.gl/mGMkXI Abstract: Loans made via Peer-to-Peer Lending (P2PL) Platforms are becoming ever more popular among investors and borrowers. This is due to the current economic environment where cash deposits earn very little interest, whilst borrowers can face high interest rates on credit cards and short term loans. Investors seeking yielding assets are looking towards P2PL, however most lack prior lending experience. Lenders face the problem of knowing which loans are most likely to be repaid. Thus this project evaluates popular Data Mining classification algorithms to predict if a loan outcome is likely to be 'Fully Repaid‘ or 'Charged Off‘. Several approaches have been used in this project, with the aim of increasing predictive accuracy of models. Several external datasets have been blended to introduce relevant economic data, derivative columns have been created to gain meaning between different attributes. Filter attribute evaluation methods have been used to discover appropriate attribute subsets based on several criteria. Synthetic Minority Over-sampling Technique (SMOTE) has been used to address the imbalanced nature of credit datasets, by creating synthetic 'Charged Off‘ loans to ensure a more even class distribution. Tuning of parameters has been performed, showing how each algorithm‘s performance can vary as a result of changes. Data pre-processing methods have been discussed in detail, which previous research lacked discussion on. The author has documented each Data Mining phase to allow researchers to repeat tests. Selected models have been deployed as Web Applications, providing researchers with accuracy metrics upon which to evaluate them. Possible approaches to improve accuracy further have been discussed, with the hope of stimulating research into this area.
Views: 620 Callum Stevens
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 198273 Last moment tuitions
Logistic Regression Using Excel
 
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Predict who survives the Titanic disaster using Excel. Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?" Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate. How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.
Views: 172562 Data Analysis Videos
ALERT! There Is a Coup D’etat on Americans! And You Won’t Believe Who Started It!
 
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Subscribe To Justus Knight Youtube: https://www.youtube.com/channel/UCO46mZ07U0sP8QKJafyxa2w GET STARTED WITH NOBLEGOLD: https://get.noblegoldinvestments.com/gold-ira-guide/?offer_type=gold&affiliate_source=affiliate_lisa_haven CALL NOBLE: 1-888-596-7916 Get the NobleGold Free E-Book: http://go.noblegoldinvestments.com/government-lies-exposed-ebook-part-ii?affiliate_source=affiliate_lisa_ GET TRUMP COIN: https://trumpcoin2020.com/ (Get $5 Off Using Code: lisa) GET involved in the Trade Genius Academy: https://tradegenius.co/ Get Food Storage: https://www.PrepareWithLisa.com Or Call My Patriot Supply at 1-888-204-0144 DONATE To Lisa Haven Via Patreon: https://www.patreon.com/lisahaven DONATE Via-Bitcoin: 1KCxsVFgpoSV5v3n4yYaFC5XPegXNBWgVB DONATE Via-Litecoin: LNDZVf2Ysp55j3Ra89aM74F22S4baqBEet Subscribe to My Website at: http://lisahaven.news/ Subscribe to My Backup Channel: https://www.youtube.com/channel/UCqrfQtkbIsQ6p3hC8oKH99w Like Me on Bitchute/Real.Video/Minds: https://www.bitchute.com/channel/qbnhKthU5W7Z/ https://www.real.video/channel/lisahaven https://www.minds.com/LisaHaven Like Me On Facebook/Twitter: https://www.facebook.com/pages/Lisa-Haven/194854627280186?ref=hl https://twitter.com/Lisa_Haven For More Information See: https://www.scmp.com/news/china/society/article/2157883/drones-facial-recognition-and-social-credit-system-10-ways-china Facebook: https://www.inc.com/minda-zetlin/facebook-patents-spying-smartphone-camera-microphone-privacy.html Patent For Phone: http://pdfaiw.uspto.gov/.aiw?docid=20180167677&PageNum=2&IDKey=A6D3461E5DFE&HomeUrl=http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2%2526Sect2=HITOFF%2526p=1%2526u=%25252Fnetahtml%25252FPTO%25252Fsearch-bool.html%2526r=1%2526f=G%2526l=50%2526co1=AND%2526d=PG01%2526s1=%252522376,515%252522%2526OS=%252522376,515%252522%2526RS=%252522376,515%252522 China: https://www.cnet.com/news/china-launches-high-tech-bird-drones-to-watch-over-its-citizens/ https://www.wsj.com/articles/facebook-really-is-spying-on-you-just-not-through-your-phones-mic-1520448644 China Surveillance State: https://www.youtube.com/watch?v=uReVvICTrCM China: https://www.nytimes.com/2018/03/02/technology/china-technology-censorship-borders-expansion.html Facebook: https://www.scmp.com/week-asia/economics/article/2164191/why-facebook-bet-us1-billion-singapore-data-centre China Wealthy: https://www.bloomberg.com/news/articles/2018-02-06/for-china-s-wealthy-singapore-is-the-new-hong-kong https://theintercept.com/2018/09/21/google-suppresses-memo-revealing-plans-to-closely-track-search-users-in-china/ China: https://www.vox.com/2018/2/27/17058074/china-banned-words-jinping Zuckerberg: https://gizmodo.com/here-s-everything-that-s-banned-on-facebook-all-on-one-1825495383 https://www.thewrap.com/facebook-cant-become-ministry-of-truth-after-alex-jones-says-former-head-of-security/ https://www.rfa.org/english/news/china/china-has-41-journalists-behind-bars-amid-ever-widening-media-controls-12142017103536.html Social Media Platforms: http://time.com/money/5227844/facebook-reviews-private-messages/ https://www.wsj.com/articles/facebook-really-is-spying-on-you-just-not-through-your-phones-mic-1520448644 https://www.cnet.com/news/facebook-cambridge-analytica-data-mining-and-trump-what-you-need-to-know/ https://techcrunch.com/2018/08/21/facebook-score/ Facebook: https://nationalinterest.org/blog/buzz/why-facebook%E2%80%99s-new-%E2%80%98reputation-scores%E2%80%99-could-be-freedom-killer-29752 China: https://www.thenational.ae/arts-culture/comment/big-brother-china-s-data-driven-social-credit-system-sounds-like-a-sci-fi-dystopia-1.774372 https://www.washingtonpost.com/technology/2018/08/21/facebook-is-rating-trustworthiness-its-users-scale-zero-one/?utm_term=.1931d6243f21 https://techcrunch.com/2018/08/21/facebook-score/ https://www.wsj.com/articles/facebook-really-is-spying-on-you-just-not-through-your-phones-mic-1520448644 Facebook: http://www.mybusinesspresence.com/how-to-avoid-facebook-jail-or-facebook-time-out-for-your-company/ China: https://www.worldwatchmonitor.org/2018/02/china-100-christians-sent-re-education-camps-xinjiang/ China: http://www.foxnews.com/world/2018/09/10/chinese-officials-burn-bibles-close-churches-force-christian-to-denounce-faith-amid-escalating-crackdown.html Facebook: https://www.aim.org/on-target-blog/national-religious-broadcasters-censorship/ Facebook: https://www.usatoday.com/story/tech/news/2018/04/19/facebook-growing-use-facial-recognition-raises-privacy-concerns/526937002/
Views: 42492 Lisa Haven
Developments and Challenges in Credit Scoring
 
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Developments and Challenges in Credit Scoring, Dan Kellett, Director of Decision Sciences, Capital One UK
Views: 137 The OR Society
Big Data Analytics Lectures | Jaccard distance  with Solved Example in hindi
 
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Video credit : Atharva hello friends, In this video we will be learning Jaccard distance and Jaccard similarity concept. It is used to calculate the similarity or disimilarity between 2 sets. and It is also profoundly used in Data mining and machine learning. AND also please have a look at the distance measures video before watching this Before watching this it is ALL the Best and Have a nice day. visit our website for full course www.lastmomenttuitions.com NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 5122 Last moment tuitions
Scoring and retraining ML models using managed data pipelines   Final
 
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Machine Learning models take time and effort to get right and implement. Once they are up and running, models may need to be periodically retrained to keep up with the pace of new information. In this session, we will use descriptive scenarios like predictive inventory management, energy consumption and capacity planning, and condition based equipment maintenance to show how to set up managed data pipelines to: 1. Score large-scale data as batch executions 2. Execute model retraining with and without model evaluation 3. Schedule all executions as recurring data pipelines We will demonstrate this in the context of the service integration between Azure Machine Learning and Azure Data Factory. Speaker: Sonia Carlson is a Principal Program Manager with the Azure Data Factory (ADF) team. She previously worked in the Biotech and Pharmaceutical industry on laboratory and data analytics platforms for genetics and gene expression analysis. Lynn Gasch is a Sr. SDE and currently develops the ADF integration with the Azure Machine Learning service. She previously worked in AI research focusing on data mining, information retrieval and management, and network and computer security.
IS 640 R Data Mining Project Solutions
 
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SOLUTION LINK: http://libraay.com/downloads/is-640-r-data-mining-project-solutions/ Use Random Forests, Neural Networks and Support Vector Machines to predict loan status (default or not). Understand the difference between in-sample fitting and out-of-sample predictive performance. Use two cross-validation methods to assess analytic model performance. Save this file on your desktop as yourlastname_640DM.docx. Load the Loan.csv data set into R. It lists the outcome of 850 loans. The data variables include loan status, credit grade (from excellent to poor), loan amount, loan age (in months), borrower’s interest rate and the debt to income ratio. Code loan status as a binary outcome (0 for current loans, 1 for late or default loans). Display the column names from the loan data set. Fit the loan data set using random forest function. Copy the trained random forest model and the confusion matrix from R and paste it below. [10 points] Randomly select 750 out of 850 loans as your training sample. Use the remaining 100 loans as your test set. Train the 2nd random forest model using the training set. Apply the 2nd model to the test set to predict loan status. Compare your predictions to the true loan statuses (using table function). Display the confusion matrix below. Based on this confusion matrix, what’s the overall misclassification rate? [10 points] Fit the loan data set using an artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare in-sample predictions to the true loan statuses. Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build the 2nd artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build a model of support vector machine. Use table function to compare the SVM’s out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Randomly shuffle the loan data set. Run 10-fold cross-validation to evaluate the out-of-sample performance of Random Forest, ANN and SVM. Based on your cross-validation results, which model has the best out-of-sample performance? Please briefly explain why. [30 points] Run leave-one-out cross-validation to evaluate the performance of random forest algorithm in predicting loan status. Why does it take much longer to run leave-one-out cross-validation than to run ten-fold cross-validation? Based on the result of your leave-one-out cross-validation, how many loans are misclassified by the random forest model?[20 points] Please save your word file as a pdf file named yourlastname_640DM.pdf. Submit the pdf file through the drop box in your Canvas account.
Views: 110 Libraay Downloads

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