EXPLANATORY MODEL FOR FATAL VEHICLE ACCIDENTS IN THE UNITED STATES

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1 EXPLANATORY MODEL FOR FATAL VEHICLE ACCIDENTS IN THE UNITED STATES TORONTO AREA SAS SOCIETY PRESENTED BY: KATHERINE HEIGHINGTON, MUKUL PANDEY, SUNNY GIROTI

2 SAS Student Symposium Our Team 2 MUKUL PANDEY B.Eng., M.B.A. SUNNY GIROTI B.Eng. KATHERINE HEIGHINGTON B.Sc., B.Ed.

Fatal Vehicle Accidents in the US 3 2013 Compared to 19

Zealand Canada France Japan 4.5 5.4 5.1 5.6 10.

3 Fatal Vehicle Accidents in the US Compared to 19 other high income countries, the United States had highest deaths per 100,000 people. Deaths per 100,000 people in 2013 United States New Zealand Canada France Japan ,000 Deaths 2 Million Injuries number of deaths has increased by 10% to over 35,000 deaths.

4 Fatality Analysis Reporting Data 4 ACCIDENT VEHICLE PARKWORK PERSON

5 Data Cleansing and Aggregation 5 Aggregate Data from Multiple Files Explanation: Aggregated the variables so there is only one observation per crash Reason: Allow for proper comparison of different crashes PROC SQL Explanation: Impute the mean for continuous variables and Indicate level for categorical variables Missing Data Reason: linear regression models can t have missing data PROC HPIMPUTE

6 Variable Selection 6 Hierarchical Clustering to Reduce Categorical Variable Levels Explanation: Collapses Levels in a way that minimally disturbs the Chi square values Reason: Reduce the complexity of the model PROC CLUSTER PROC TREE Multicollinearity between Variables Explanation: Two variables provide the same information (highly correlated) Reason: Causes the model to be unstable PROC REG Variable Selection Using Stepwise Explanation: Selects significant variables that add information to the model Reason: Reduce the complexity of the model PROC REG

ANALYSIS & REPORTING 7 Key explanatory factors

the time such as Dawn) had a high correlation

7 ANALYSIS & REPORTING 7 Key explanatory factors for high fatality were Drugs Intake, Alcohol Consumption and Ejection from the Vehicle Important finding Day of the week (as well as the time such as Dawn) had a high correlation with fatality. - Wee hours of Saturday and Sunday were the worst

8 Fall Term 2016 Winter Term 2017 Summer Term 2017 Schulich Master of Business Analytics 8 Predictive Modeling SAS Data Programming Data Science: Machine Learning Economic Forecasting: R Quantitative Methods for Business SAS Data Programming Multivariate Methods for Analytics Data Science: Machine Learning using Python Analytics Consulting Case Analysis Marketing Metrics and Research Capstone Project: 12-week project with hands-on problem driven research with a company SAS Certified upon Graduation WORKSHOPS: Big Data, Data Governance, Tableau, SAS Visualization Analytics, Text Analytics (Scheduled in March & April)

9 9 Our Team - Mukul Pandey ACADEMIC BACKGROUND: B. Engineering, Electronics & Communication (University of Delhi, India) M.B.A, Finance & Strategy (Indian School of Business, India) PROFESSIONAL EXPERIENCE: SAP ERP Consulting at PwC, ERP & Process Consulting at Ernst & Young Strategy and Marketing at Schneider Electric FAVOURITE MBAN COURSES: Predictive Modeling using SAS, Economic Forecasting using R, and Data Sciences using Python RELEVANT PROJECTS: Forecasting Index of Industrial Production using advanced (ARIMA/Holt-Winters/VAR) Models Account Based Marketing strategy for a GTA based Digital & Analytics startup Machine Learning techniques for improved prediction of key global indices

10 Our Team - Katherine Heighington 10 ACADEMIC BACKGROUND: B.Sc. Human Biology (University of Toronto) B.Ed. Mathematics Education (Ontario Institute for Studies in Education) PROFESSIONAL EXPERIENCE: Former Math Teacher FAVOURITE MBAN COURSES: Predictive Modeling (SAS Based Course) and Economic Forecasting RELEVANT PROJECTS: Forecasting Alcohol Sales with Time Series Analysis Writing a Data Governance Case Study with the CDO of TD Bank and MBAN Program Director Max TV Media Marketing Analysis Consultant (In progress)

Business Intelligence Technical Consulting at Deloitte Consulting USI and Sopra Steria Pvt. Ltd.

11 11 Our Team - Sunny Giroti ACADEMIC BACKGROUND: B. Engineering, Computer Science (Jaypee Institute of Information Technology, India) Graduate Gemologist, Diamonds (Gemological Institute of America) PROFESSIONAL EXPERIENCE: SAP Business Intelligence Technical Consulting at Deloitte Consulting USI and Sopra Steria Pvt. Ltd. Entrepreneur and Gemology Advisor at Giroti Jewels (Colored Stones, Diamonds, Gold) FAVOURITE MBAN COURSES: Predictive Modeling (SAS), Data Science (Machine Learning), Analytics Consulting and Case Analysis RELEVANT PROJECTS: MRKT360 s Market Identification and Penetration Strategy (Live project with the company, in progress) Market Research based project to launch a new Baby Wearable Device in Canada

12 12 Thank You! Q&A

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