Predictive Modeling and Big Data
|
|
|
- Chad Barnett
- 8 years ago
- Views:
Transcription
1 Predictive Modeling and Presented by Eileen Burns, FSA, MAAA Milliman Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 2 1 June 16, 2014 [Enter presentation title in footer] Copyright 2007
2 Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 3 Predictive Modeling 2010 SOA call for papers on predictive modeling for life insurers: involves "mining" datasets and performing statistical analysis that may uncover unexpected relationships about the underlying risks that may indicate the likelihood of future outcomes for an insurer. 4 2 June 16, 2014 [Enter presentation title in footer] Copyright 2007
3 Types of Predictive Models Ordinary Least Squares Generalized Linear Models Generalized Additive Models Bayesian Models CART Boosted trees Random forests K-means clustering Linear programming 5 Nonlinear programming Simulated annealing Genetic algorithms Agent-based models Neural networks MARS Robust regression Semiparametric regression Ordinary Least Squares and Generalized Linear Models response 㐲 0response 1 p predictor predictor predictor 6 3 June 16, 2014 [Enter presentation title in footer] Copyright 2007
4 CART and Random Forests 7 K-means Clustering 8 4 June 16, 2014 [Enter presentation title in footer] Copyright 2007
5 Optimization models, and Simulated Annealing 9 Agent-based Models 10 5 June 16, 2014 [Enter presentation title in footer] Copyright 2007
6 Agenda Current uses of predictive modeling in the life insurance industry Potential applications of Dimensions of data 12 Volume Velocity Variety Implications for modeling with Imposes restrictions on data cleaning feasibility Requires new methods for analysis Shifts focus to correlation rather than causation Implications for users of Why? Precision June 16, 2014 [Enter presentation title in footer] Copyright 2007
7 Types of Personal 13 Credit Consumer spending Health Social network Fit bit Grouped Regional Employer Healthcare provider Demographic Some restrictions apply! Agenda Current uses of predictive modeling in the life insurance industry Potential applications of q x 14 7 June 16, 2014 [Enter presentation title in footer] Copyright 2007
8 Current uses of predictive modeling in the life insurance industry Underwriting Pricing Policyholder behavior q x Agent performance Product design 15 Agenda Current uses of predictive modeling in the life insurance industry Potential applications of /14 John Smith 16 8 June 16, 2014 [Enter presentation title in footer] Copyright 2007
9 Property & Casualty Insurance Car insurance Homeowners Workers comp Health Credit Bureaus /14 John Smith 17 Agenda Current uses of predictive modeling in the life insurance industry Potential applications of q x 18 9 June 16, 2014 [Enter presentation title in footer] Copyright 2007
10 Potential Applications of Directional marketing Scoring algorithms for underwriting Further refine experience analysis Base and dynamic lapse Guarantee utilization Annuitization q x 19 Q&A Eileen Burns, FSA, MAAA eileen.burns@milliman.com /14 John Smith q x June 16, 2014 [Enter presentation title in footer] Copyright 2007
Predictive Modeling Techniques in Insurance
Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics
2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012. Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA
2012 3 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012 Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA Predictive Modeling: An Overview November 8, 2012 Syed M. Mehmud
Predictive modelling around the world 28.11.13
Predictive modelling around the world 28.11.13 Agenda Why this presentation is really interesting Introduction to predictive modelling Case studies Conclusions Why this presentation is really interesting
Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA
Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA Presenters: Jean-Frederic Breton David A. Moore, FSA, MAAA Session 62:
Health Spring Meeting May 2008 Session # 42: Dental Insurance What's New, What's Important
Health Spring Meeting May 2008 Session # 42: Dental Insurance What's New, What's Important Floyd Ray Martin, FSA, MAAA Thomas A. McInteer, FSA, MAAA Jonathan P. Polon, FSA Dental Insurance Fraud Detection
An Introduction to Data Mining
An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail
COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions
SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0
Principles of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
SOA 2013 Life & Annuity Symposium May 6-7, 2013. Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting
SOA 2013 Life & Annuity Symposium May 6-7, 2013 Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting Moderator: Barry D. Senensky, FSA, FCIA, MAAA Presenters: Jonathan
An Introduction to Advanced Analytics and Data Mining
An Introduction to Advanced Analytics and Data Mining Dr Barry Leventhal Henry Stewart Briefing on Marketing Analytics 19 th November 2010 Agenda What are Advanced Analytics and Data Mining? The toolkit
Learning outcomes. Knowledge and understanding. Competence and skills
Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges
BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376
Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.
Data Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
A Deeper Look Inside Generalized Linear Models
A Deeper Look Inside Generalized Linear Models University of Minnesota February 3 rd, 2012 Nathan Hubbell, FCAS Agenda Property & Casualty (P&C Insurance) in one slide The Actuarial Profession Travelers
Predictive Modeling in Workers Compensation 2008 CAS Ratemaking Seminar
Predictive Modeling in Workers Compensation 2008 CAS Ratemaking Seminar Prepared by Louise Francis, FCAS, MAAA Francis Analytics and Actuarial Data Mining, Inc. www.data-mines.com Louise.francis@data-mines.cm
Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA
Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA Presenters: Guillaume Briere-Giroux, FSA, MAAA Eileen Sheila
DATA MINING TECHNIQUES AND APPLICATIONS
DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,
A Basic Guide to Modeling Techniques for All Direct Marketing Challenges
A Basic Guide to Modeling Techniques for All Direct Marketing Challenges Allison Cornia Database Marketing Manager Microsoft Corporation C. Olivia Rud Executive Vice President Data Square, LLC Overview
Model Validation Techniques
Model Validation Techniques Kevin Mahoney, FCAS kmahoney@ travelers.com CAS RPM Seminar March 17, 2010 Uses of Statistical Models in P/C Insurance Examples of Applications Determine expected loss cost
Table of Contents. June 2010
June 2010 From: StatSoft Analytics White Papers To: Internal release Re: Performance comparison of STATISTICA Version 9 on multi-core 64-bit machines with current 64-bit releases of SAS (Version 9.2) and
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets
Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification
PREDICTIVE MODELS IN LIFE INSURANCE
PREDICTIVE MODELS IN LIFE INSURANCE Date: 17 June 2010 Philip L. Adams, ASA, MAAA Agenda 1. Predictive Models Defined 2. Predictive models past and present 3. Actuarial perspective 4. Application to Life
An Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
Comparison of Data Mining Techniques used for Financial Data Analysis
Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract
Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes
Knowledge Discovery and Data Mining Lecture 19 - Bagging Tom Kelsey School of Computer Science University of St Andrews http://tom.host.cs.st-andrews.ac.uk twk@st-andrews.ac.uk Tom Kelsey ID5059-19-B &
Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA
Welcome Xindong Wu Data Mining: Updates in Technologies Dept of Math and Computer Science Colorado School of Mines Golden, Colorado 80401, USA Email: xwu@ mines.edu Home Page: http://kais.mines.edu/~xwu/
Practical applications of Predictive Modelling Overview of the process, the techniques and the applications
Practical applications of Predictive Modelling Overview of the process, the techniques and the applications Jean-Yves Rioux CIA 2014 Annual Meeting, June 19, 2014 Agenda What it is and why do it 2 Process
New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction
Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.
Using Predictive Analytics to Detect Fraudulent Claims
Using Predictive Analytics to Detect Fraudulent Claims May 17, 211 Roosevelt C. Mosley, Jr., FCAS, MAAA CAS Spring Meeting Palm Beach, FL Experience the Pinnacle Difference! Predictive Analysis for Fraud
Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
In this presentation, you will be introduced to data mining and the relationship with meaningful use.
In this presentation, you will be introduced to data mining and the relationship with meaningful use. Data mining refers to the art and science of intelligent data analysis. It is the application of machine
Data Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification
MS1b Statistical Data Mining
MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to
ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis
ElegantJ BI White Paper The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis Integrated Business Intelligence and Reporting for Performance Management, Operational
WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat
Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise
Data Mining. Nonlinear Classification
Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15
Azure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD
Predictive Analytics Techniques: What to Use For Your Big Data March 26, 2014 Fern Halper, PhD Presenter Proven Performance Since 1995 TDWI helps business and IT professionals gain insight about data warehousing,
Index Contents Page No. Introduction . Data Mining & Knowledge Discovery
Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.
EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.
EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models
HT2015: SC4 Statistical Data Mining and Machine Learning
HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric
Using Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
Personal Auto Predictive Modeling Update: What s Next? Roosevelt Mosley, FCAS, MAAA CAS Predictive Modeling Seminar October 6 7, 2008 San Diego, CA
Personal Auto Predictive Modeling Update: What s Next? Roosevelt Mosley, FCAS, MAAA CAS Predictive Modeling Seminar October 6 7, 2008 San Diego, CA You ve Heard Where predictive modeling for auto has been
from Larson Text By Susan Miertschin
Decision Tree Data Mining Example from Larson Text By Susan Miertschin 1 Problem The Maximum Miniatures Marketing Department wants to do a targeted mailing gpromoting the Mythic World line of figurines.
The Predictive Data Mining Revolution in Scorecards:
January 13, 2013 StatSoft White Paper The Predictive Data Mining Revolution in Scorecards: Accurate Risk Scoring via Ensemble Models Summary Predictive modeling methods, based on machine learning algorithms
SEIZE THE DATA. 2015 SEIZE THE DATA. 2015
1 Copyright 2015 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. BIG DATA CONFERENCE 2015 Boston August 10-13 Predicting and reducing deforestation
Predictive Analytics: Achieving Greater Decision Accuracy, Better Risk Segmentation, and Greater Profitability
Predictive Analytics: Achieving Greater Decision Accuracy, Better Risk Segmentation, and Greater Profitability Lamont D. Boyd, CPCU, AIM Insurance Market Director FICO Scoring Solutions LamontBoyd@FICO.com
How To Understand Data Mining In R And Rattle
http: // togaware. com Copyright 2014, Graham.Williams@togaware.com 1/40 Data Analytics and Business Intelligence (8696/8697) Introducing Data Science with R and Rattle Graham.Williams@togaware.com Chief
Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.
Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví Pavel Kříž Seminář z aktuárských věd MFF 4. dubna 2014 Summary 1. Application areas of Insurance Analytics 2. Insurance Analytics
How To Make A Credit Risk Model For A Bank Account
TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions
TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science
TNS EX A MINE BehaviourForecast Predictive Analytics for CRM 1 TNS BehaviourForecast Why is BehaviourForecast relevant for you? The concept of analytical Relationship Management (acrm) becomes more and
Session 54 PD, Credibility and Pooling for Group Life and Disability Insurance Moderator: Paul Luis Correia, FSA, CERA, MAAA
Session 54 PD, Credibility and Pooling for Group Life and Disability Insurance Moderator: Paul Luis Correia, FSA, CERA, MAAA Presenters: Paul Luis Correia, FSA, CERA, MAAA Brian N. Dunham, FSA, MAAA Credibility
Data Mining. Dr. Saed Sayad. University of Toronto 2010 saed.sayad@utoronto.ca. http://chem-eng.utoronto.ca/~datamining/
Data Mining Dr. Saed Sayad University of Toronto 2010 saed.sayad@utoronto.ca http://chem-eng.utoronto.ca/~datamining/ 1 Data Mining Data mining is about explaining the past and predicting the future by
Big Data and Marketing
Big Data and Marketing Professor Venky Shankar Coleman Chair in Marketing Director, Center for Retailing Studies Mays Business School Texas A&M University http://www.venkyshankar.com venky@venkyshankar.com
Machine Learning and Data Mining. Fundamentals, robotics, recognition
Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,
Cleaned Data. Recommendations
Call Center Data Analysis Megaputer Case Study in Text Mining Merete Hvalshagen www.megaputer.com Megaputer Intelligence, Inc. 120 West Seventh Street, Suite 10 Bloomington, IN 47404, USA +1 812-0-0110
Data Mining and Pattern Recognition for Large-Scale Scientific Data
Data Mining and Pattern Recognition for Large-Scale Scientific Data Chandrika Kamath Center for Applied Scientific Computing Lawrence Livermore National Laboratory October 15, 1998 We need an effective
Session 61 L, Applications of Data Analytics in Health Insurance. Moderator/Presenter: Henning Chiv, FSA, MAAA
Session 61 L, Applications of Data Analytics in Health Insurance Moderator/Presenter: Henning Chiv, FSA, MAAA Session 61: Applications of Data Analytics in Health Insurance Henning Chiv, FSA, MAAA June
MSCA 31000 Introduction to Statistical Concepts
MSCA 31000 Introduction to Statistical Concepts This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced
Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.
Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation
Let the data speak to you. Look Who s Peeking at Your Paycheck. Big Data. What is Big Data? The Artemis project: Saving preemies using Big Data
CS535 Big Data W1.A.1 CS535 BIG DATA W1.A.2 Let the data speak to you Medication Adherence Score How likely people are to take their medication, based on: How long people have lived at the same address
Using Adaptive Random Trees (ART) for optimal scorecard segmentation
A FAIR ISAAC WHITE PAPER Using Adaptive Random Trees (ART) for optimal scorecard segmentation By Chris Ralph Analytic Science Director April 2006 Summary Segmented systems of models are widely recognized
Insurance Fraud Detection: MARS versus Neural Networks?
Insurance Fraud Detection: MARS versus Neural Networks? Louise A Francis FCAS, MAAA Louise_francis@msn.com 1 Objectives Introduce a relatively new data mining method which can be used as an alternative
Ensemble Learning Better Predictions Through Diversity. Todd Holloway ETech 2008
Ensemble Learning Better Predictions Through Diversity Todd Holloway ETech 2008 Outline Building a classifier (a tutorial example) Neighbor method Major ideas and challenges in classification Ensembles
COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Big Data by the numbers
COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Instructor: (jpineau@cs.mcgill.ca) TAs: Pierre-Luc Bacon (pbacon@cs.mcgill.ca) Ryan Lowe (ryan.lowe@mail.mcgill.ca)
Session 60 PD, Predictive Modeling Real Applications in Life Insurance and Annuities. Moderator: Ricardo Trachtman, FSA, MAAA
Session 60 PD, Predictive Modeling Real Applications in Life Insurance and Annuities Moderator: Ricardo Trachtman, FSA, MAAA Presenters: JJ Lane Carroll, FSA, MAAA Allen M. Klein, FSA, MAAA Scott Anthony
Data Mining: STATISTICA
Data Mining: STATISTICA Outline Prepare the data Classification and regression 1 Prepare the Data Statistica can read from Excel,.txt and many other types of files Compared with WEKA, Statistica is much
Benchmarking of different classes of models used for credit scoring
Benchmarking of different classes of models used for credit scoring We use this competition as an opportunity to compare the performance of different classes of predictive models. In particular we want
Combining Linear and Non-Linear Modeling Techniques: EMB America. Getting the Best of Two Worlds
Combining Linear and Non-Linear Modeling Techniques: Getting the Best of Two Worlds Outline Who is EMB? Insurance industry predictive modeling applications EMBLEM our GLM tool How we have used CART with
Handling attrition and non-response in longitudinal data
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
Machine learning for algo trading
Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with
2011 Data Miner Survey Highlights
Predictive Analytics World New York, NY October 2011 2011 Data Miner Survey Highlights The Views of 1,319 Data Miners Karl Rexer, PhD President Rexer Analytics www.rexeranalytics.com 2011 Data Miner Survey:
Predictive Data modeling for health care: Comparative performance study of different prediction models
Predictive Data modeling for health care: Comparative performance study of different prediction models Shivanand Hiremath hiremat.nitie@gmail.com National Institute of Industrial Engineering (NITIE) Vihar
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in
Data Mining + Business Intelligence. Integration, Design and Implementation
Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution
A Property & Casualty Insurance Predictive Modeling Process in SAS
Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing
High-Performance Analytics
High-Performance Analytics David Pope January 2012 Principal Solutions Architect High Performance Analytics Practice Saturday, April 21, 2012 Agenda Who Is SAS / SAS Technology Evolution Current Trends
Data Science with R. Introducing Data Mining with Rattle and R. Graham.Williams@togaware.com
http: // togaware. com Copyright 2013, Graham.Williams@togaware.com 1/35 Data Science with R Introducing Data Mining with Rattle and R Graham.Williams@togaware.com Senior Director and Chief Data Miner,
Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America
Application of SAS! Enterprise Miner in Credit Risk Analytics Presented by Minakshi Srivastava, VP, Bank of America 1 Table of Contents Credit Risk Analytics Overview Journey from DATA to DECISIONS Exploratory
KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES
HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice
MSCA 31000 Introduction to Statistical Concepts
MSCA 31000 Introduction to Statistical Concepts This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced
Chapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
Predictive Analytics for Life Insurance: How Data and Advanced Analytics are Changing the Business of Life Insurance Seminar May 23, 2012
Predictive Analytics for Life Insurance: How Data and Advanced Analytics are Changing the Business of Life Insurance Seminar May 23, 2012 Session 2 How to Build a Risk Based Analytical Model for Life Insurance
Data Mining Algorithms Part 1. Dejan Sarka
Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses
Prediction of Stock Performance Using Analytical Techniques
136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University
CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19
PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.
Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA
Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team. Moderator: Courtney Nashan
Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team Moderator: Courtney Nashan Presenters: Ian G. Duncan, FSA, FCIA, FIA, MAAA Andy Ferris, FSA, MAAA Christine
Knowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Unit # 11 Sajjad Haider Fall 2013 1 Supervised Learning Process Data Collection/Preparation Data Cleaning Discretization Supervised/Unuspervised Identification of right
Studying Auto Insurance Data
Studying Auto Insurance Data Ashutosh Nandeshwar February 23, 2010 1 Introduction To study auto insurance data using traditional and non-traditional tools, I downloaded a well-studied data from http://www.statsci.org/data/general/motorins.
Overview. Data Mining. Predicting Stock Market Returns. Predicting Health Risk. Wharton Department of Statistics. Wharton
Overview Data Mining Bob Stine www-stat.wharton.upenn.edu/~bob Applications - Marketing: Direct mail advertising (Zahavi example) - Biomedical: finding predictive risk factors - Financial: predicting returns
Marketing Strategies for Retail Customers Based on Predictive Behavior Models
Marketing Strategies for Retail Customers Based on Predictive Behavior Models Glenn Hofmann HSBC Salford Systems Data Mining 2005 New York, March 28 30 0 Objectives Inform about effective approach to direct
Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model
Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model Xavier Conort xavier.conort@gear-analytics.com Motivation Location matters! Observed value at one location is
Data Mining Practical Machine Learning Tools and Techniques
Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea
Big Data and Data Science: Behind the Buzz Words
Big Data and Data Science: Behind the Buzz Words Peggy Brinkmann, FCAS, MAAA Actuary Milliman, Inc. April 1, 2014 Contents Big data: from hype to value Deconstructing data science Managing big data Analyzing
What is Data Mining, and How is it Useful for Power Plant Optimization? (and How is it Different from DOE, CFD, Statistical Modeling)
data analysis data mining quality control web-based analytics What is Data Mining, and How is it Useful for Power Plant Optimization? (and How is it Different from DOE, CFD, Statistical Modeling) StatSoft