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: 1978 Jonathan Walker
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: 20912 Packt Video
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 12220 Ben Rodick
Learn more about credit risk modeling with R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Hi, and welcome to the first video of the credit risk modeling course. My name is Lore, I'm a data scientist at DataCamp and I will help you master some basics of the credit risk modeling field. The area of credit risk modeling is all about the event of loan default. Now what is loan default? When a bank grants a loan to a borrower, which could be an individual or a company, the bank will usually transfer the entire amount of the loan to the borrower. The borrower will then reimburse this amount in smaller chunks, including some interest payments, over time. Usually these payments happen monthly, quarterly or yearly. Of course, there is a certain risk that a borrower will not be able to fully reimburse this loan. This results in a loss for the bank. The expected loss a bank will incur is composed of three elements. The first element is the probability of default, which is the probability that the borrower will fail to make a full repayment of the loan. The second element is the exposure at default, or EAD, which is the expected value of the loan at the time of default. You can also look at this as the amount of the loan that still needs to be repaid at the time of default. The third element is loss given default, which is the amount of the loss if there is a default, expressed as a percentage of the EAD. Multiplying these three elements leads to the formula of expected loss. In this course, we will focus on the probability of default. Banks keep information on the default behavior of past customers, which can be used to predict default for new customers. Broadly, this information can be classified in two types. The first type of information is application information. Examples of application information are income, marital status, et cetera. The second type of information, behavioral information, tracks the past behavior of customers, for example the current account balance and payment arrear history. Let's have a look at the first ten lines of our data set. This data set contains information on past loans. Each line represents one customer and his or her information, along with a loan status indicator, which equals 1 if the customer defaulted, and 0 if the customer did not default. Loan status will be used as a response variable and the explanatory variables are the amount of the loan, the interest rate, grade, employment length, home ownership status, the annual income and the age. The grade is the bureau score of the customer, where A indicates the highest class of creditworthiness and G the lowest. This bureau score reflects the credit history of the individual and is the only behavioral variable in the data set. For an overview of the data structure for categorical variables, you can use the CrossTable() function in the gmodels package. Applying this function to the home ownership variable, you get a table with each of the categories in this variable, with the number of cases and proportions. Using loan status as a second argument, you can look at the relationship between this factor variable and the response. By setting prop.r equal to TRUE and the other proportions listed here equal to FALSE, you get the row-wise proportions. Now what does this result tell you? It seems that the default rate in the home ownership group OTHER is quite a bit higher than the default rate in, for example, the home ownership group MORTGAGE, with 17.5 versus 9.8 percent of defaults in these groups, respectively. Now, let's explore other aspects of the data using R.
Views: 30558 DataCamp
Predicción de si un cliente será buen pagador o no empleando Redes Bayesianas. El sistema de Credit Scoring.
Views: 8 TODO Mining
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: 9151 Statistica France
Lenddo, world leader in social authentication and scoring technology, uses alternative data to provide credit scoring and verification to economically empower the emerging middle class around the world. We reinvent the consumer finance with life-changing services and give companies to the ability to create positive social impact. www.lenddo.com
Views: 5431 Florentin Lenoir
This video shows step by step how real-time credit scoring application can be built using machine learning and Apache Spark streaming.
Views: 3860 Mariusz Jacyno
This webinar was delivered by a Machine Learning expert and enthusiast with 17+ years of experience in analytics and related domains.
Views: 5402 IvyProSchool
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: 6685 Predictive Analytics
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: 2872 EduPristine
Credit Risk assessment aims to determine the probability of loss on a particular asset, investment or loan. The objective of assessing credit risk is to determine if an investment is worthwhile, what steps should be taken to mitigate risk, and what the return rate should be to make an investment successful. An accurate Credit Risk Model allows the financial institution to provide fair prices to customers while ensuring predictable and minimal losses. At Zopa, we use machine learning to estimate Credit Risk. In this talk, I will cover the steps involved in the creation of our Credit Risk Model, including variable pre-processing, target definition, variable selection and building and evaluation of the different machine learning models.
Views: 743 Data Science Festival
Neural Networks supervised by genetic algorithms perform better than any other machine learning algorithm for credit scoring and rating, according the AUROC metrics.
Views: 510 Carlos Antonio Campos Nogueira
SOLUTION LINK: http://libraay.com/downloads/data-mining-in-ms-excel-credit-card-default-prediction/ Data Set Information: The training data contains 22500 observations with the predictor variables as well as the response variable. The test set contains 7500 observations with the response variable removed. Task: Predict the response variable (default status) for the test data. IMPORTANT: Please include the variable "ID" in the prediction, so that model accuracy can be evaluated. Variable descriptions: This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. X2: Gender (1 = male; 2 = female). X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). X4: Marital status (1 = married; 2 = single; 3 = others). X5: Age (year). X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.
Views: 75 Libraay Downloads
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: 100 The Audiopedia
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 43 aircc journal
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: 22288 SAS Software
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: 288117 Data School
We have let algorithms define too many areas of our life from zero-tolerance in schools to relying on credit scores. Now data mining firms are using our own data against us to our detriment causing extra costs simply because some data was collected. #DataPrivacy #Algorithms #ZeroTolerance To help support our work, check out our multi-channel support page: https://tlm.li/tlm Check out our affiliates: https://tlm.li/aff Check out our support page at SwitchedtoLinux: https://tlm.li/stl Merch: https://tlm.li/stlm Support on Patreon: https://tlm.li/pat We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and affiliated sites. The following is the equipment used by Switched to Linux: Main Amazon Site - http://tlm.li/amazon Studio Light - Studio Light - http://amzn.to/2pHPuyn Logitech c920 - http://amzn.to/2nlUCXN Logitech c615 - http://amzn.to/2ohO7u6 Samson Meteor Mic - http://amzn.to/2nlVvzD Shortlinks: the shortlink urls "tlm.li" are my own self hosted application. I realize some people do not like to click on shortlinks, and for you, all information on these links can also be found by browsing these sites: https://www.switchedtolinux.com https://thinklifemedia.com
Views: 578 Switched to Linux
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: 5539 Jeromy Anglim
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: 175944 Data Analysis Videos
In this video you will learn what is Gain chart and how is it constructed. You will also learn how to use gain chart in logistic regression for model monitoring Contact [email protected]
Views: 6613 Analytics University
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
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
Views: 92075 Eduonix Learning Solutions
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: 12987 Learn Analytics
( Data Science Training - https://www.edureka.co/data-science ) This Logistic Regression Tutorial shall give you a clear understanding as to how a Logistic Regression machine learning algorithm works in R. Towards the end, in our demo we will be predicting which patients have diabetes using Logistic Regression! In this Logistic Regression Tutorial video you will understand: 1) The 5 Questions asked in Data Science 2) What is Regression? 3) Logistic Regression - What and Why? 4) How does Logistic Regression Work? 5) Demo in R: Diabetes Use Case 6) Logistic Regression: Use Cases Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 82288 edureka!
▶️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: 5157 RANDOMFIX
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
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
Views: 917 San Francisco Bay ACM
My web page: www.imperial.ac.uk/people/n.sadawi
Views: 10930 Noureddin Sadawi
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: 1306 The Audiopedia
Cars on Credit relies on Portfolio Plus Prospector, an analytics and data mining tool, to run their car loan business effectively.
Views: 506 SIT | Better Banking Software
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: 1040 MATLAB
www.learnanalytics.in demostrates use of an free and open source platform to build sophisticated predictive models. We demonstrate using R package Rattle to do data analysis without writing a line of r code. We cover hypothesis testing, descriptive statistics, linear and logistic regression with a flavor of machine learning (Random Forest, SVM etc.). Also using graphs such as ROC curves and Area under curves (AUC) to compare various models. To download the dataset and follow on your own follow http://www.learnanalytics.in/datasets/Credit_Scoring.zip
Views: 42807 Learn Analytics
http://EasilyIncreaseCreditScore.blinkweb.com/ is designed to help people increase the credit score fast. We teach people who to improve their own credit score with outspending hundreds of dollars. A credit score defined by wikipedia goes as follows. 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: 631 IncreaseCreditScore
[Streamed version. Front & back trimmed. Slide issue in beginning.] An edited version is available: https://www.youtube.com/watch?v=ANqB72b0r38 Slides: http://www.slideshare.net/gregmakowski/kamanja-driving-business-value-through-realtime-decisioning-solutions 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: 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
Views: 1166 San Francisco Bay ACM
In this movie we will talk about data preparation for scorecard development. Demo version can be requested at http://plug-n-score.com/credit-scoring-system-software-product-info-modeler.htm We will consider more issues about scorecard development process in our next videos. Follow us at social network accounts: http://www.youtube.com/user/PlugScore http://www.linkedin.com/company/plug-&-score https://twitter.com/plug_n_score https://www.facebook.com/pages/PlugScore/201377040068932 https://plus.google.com/105370001732025295734/posts http://www.slideshare.net/PlugnScore
Views: 799 Plug&Score - Credit Scoring Software
Learn the basics of Machine Learning with R. Start our Machine Learning Course for free: https://www.datacamp.com/courses/introduction-to-machine-learning-with-R First up is Classification. A *classification problem* involves predicting whether a given observation belongs to one of two or more categories. The simplest case of classification is called binary classification. It has to decide between two categories, or classes. Remember how I compared machine learning to the estimation of a function? Well, based on earlier observations of how the input maps to the output, classification tries to estimate a classifier that can generate an output for an arbitrary input, the observations. We say that the classifier labels an unseen example with a class. The possible applications of classification are very broad. For example, after a set of clinical examinations that relate vital signals to a disease, you could predict whether a new patient with an unseen set of vital signals suffers that disease and needs further treatment. Another totally different example is classifying a set of animal images into cats, dogs and horses, given that you have trained your model on a bunch of images for which you know what animal they depict. Can you think of a possible classification problem yourself? What's important here is that first off, the output is qualitative, and second, that the classes to which new observations can belong, are known beforehand. In the first example I mentioned, the classes are "sick" and "not sick". In the second examples, the classes are "cat", "dog" and "horse". In chapter 3 we will do a deeper analysis of classification and you'll get to work with some fancy classifiers! Moving on ... A **Regression problem** is a kind of Machine Learning problem that tries to predict a continuous or quantitative value for an input, based on previous information. The input variables, are called the predictors and the output the response. In some sense, regression is pretty similar to classification. You're also trying to estimate a function that maps input to output based on earlier observations, but this time you're trying to estimate an actual value, not just the class of an observation. Do you remember the example from last video, there we had a dataset on a group of people's height and weight. A valid question could be: is there a linear relationship between these two? That is, will a change in height correlate linearly with a change in weight, if so can you describe it and if we know the weight, can you predict the height of a new person given their weight ? These questions can be answered with linear regression! Together, \beta_0 and \beta_1 are known as the model coefficients or parameters. As soon as you know the coefficients beta 0 and beta 1 the function is able to convert any new input to output. This means that solving your machine learning problem is actually finding good values for beta 0 and beta 1. These are estimated based on previous input to output observations. I will not go into details on how to compute these coefficients, the function `lm()` does this for you in R. Now, I hear you asking: what can regression be useful for apart from some silly weight and height problems? Well, there are many different applications of regression, going from modeling credit scores based on past payements, finding the trend in your youtube subscriptions over time, or even estimating your chances of landing a job at your favorite company based on your college grades. All these problems have two things in common. First off, the response, or the thing you're trying to predict, is always quantitative. Second, you will always need input knowledge of previous input-output observations, in order to build your model. The fourth chapter of this course will be devoted to a more comprehensive overview of regression. Soooo.. Classification: check. Regression: check. Last but not least, there is clustering. In clustering, you're trying to group objects that are similar, while making sure the clusters themselves are dissimilar. You can think of it as classification, but without saying to which classes the observations have to belong or how many classes there are. Take the animal photo's for example. In the case of classification, you had information about the actual animals that were depicted. In the case of clustering, you don't know what animals are depicted, you would simply get a set of pictures. The clustering algorithm then simply groups similar photos in clusters. You could say that clustering is different in the sense that you don't need any knowledge about the labels. Moreover, there is no right or wrong in clustering. Different clusterings can reveal different and useful information about your objects. This makes it quite different from both classification and regression, where there always is a notion of prior expectation or knowledge of the result.
Views: 38133 DataCamp
Get the access to the full course https://bit.ly/2Afo1vZ OR Use the Code LEARN90OFF & Get 90% OFF on the same. We will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset. Video Transcript: The history of data science, machine learning, and artificial Intelligence are long, but it’s only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines - we have enough computed capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not - based on their credit history, historical loan applications, customers’ data and so on Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior. Or Amazon's recommendation engine which recommends products based on buying patterns of millions of consumers. In all these examples, machine learning is used to build models from historical data, to forecast future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud - which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity? The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations. The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python. In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset. Do you know what it takes to build sophisticated machine learning models in the cloud? How to expose these models in the form of web services? Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis? These are some of the fundamental problems data scientists and engineers struggle with on a daily basis. Stay with us: Website: https://courses.tetranoodle.com/ Facebook: https://www.facebook.com/tetranoodletech Twitter: https://twitter.com/TETRANOODLE Linkedin: https://www.linkedin.com/company/tetr... Instagram: https://www.instagram.com/tetranoodle/ Udemy: https://www.udemy.com/user/manujaggarwal YouTube: https://www.youtube.com/channel/UCAiI... #TetraNoodle #MachineLearning #AzureMachineLearning #CloudComputing #AzureTutorial #ML #Azure
Views: 289 slidenerd