EXPLANATORY MODEL FOR FATAL

VEHICLE ACCIDENTS IN THE UNITED STATESTORONTO AREA SAS SOCIETY

PRESENTED BY: KATHERINE HEIGHINGTON, MUKUL PANDEY, SUNNY GIROTI

SAS Student Symposium – Our Team

KATHERINE HEIGHINGTONB.Sc., B.Ed.

SUNNY GIROTIB.Eng.

MUKUL PANDEYB.Eng., M.B.A.

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Fatal Vehicle Accidents in the US

Compared to 19 other highincome countries, the UnitedStates had highest deaths per100,000 people.

2013

32,000

2 Million

Deaths

Injuries

2013 2015

number of deaths has

increased by 10% to over

35,000 deaths.

4.5

5.1

5.4

5.6

10.3

Japan

France

Canada

New Zealand

United States

Deaths per 100,000 people in 2013

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Fatality Analysis Reporting Data

ACCIDENT

VEHICLE

PARKWORK

PERSON

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Data Cleansing and Aggregation

Missing Data

Explanation: Impute the mean for continuous variables and Indicate level for categorical variables

Reason: linear regression models can’t have missing data

PROC HPIMPUTE

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

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Variable Selection

Variable Selection Using Stepwise•Explanation: Selects significant variables that add

information to the model•Reason: Reduce the complexity of the model PROC REG

Multicollinearity between Variables•Explanation: Two variables provide the same

information (highly correlated)•Reason: Causes the model to be unstable PROC REG

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

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ANALYSIS &

REPORTING

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

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Schulich – Master of Business AnalyticsFa

ll Te

rm 2

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•Predictive Modeling

•SAS Data Programming

•Data Science: Machine Learning

•Economic Forecasting: R

•Quantitative Methods for Business W

inte

r Te

rm 2

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SAS Data Programming

Multivariate Methods for Analytics

Data Science: Machine Learning using Python

Analytics Consulting

Case Analysis

Marketing Metrics and Research

Sum

me

r Te

rm 2

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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)

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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

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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)

Our Team - Katherine Heighington

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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

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Thank You !

Q&A

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