This competition is challenging because the attendants are asked to use Microsoft Azure to analyze any open data. The judge would evaluate team's performance based on visualization and modeling. Our team analyzed the factors that cause multiple car crashes and the correlation between Vehicle crashes and repair shops.

1. Data Collection – Vehicle Crashes
 Collected 1,048,575 records of vehicle crashes in New York from 2009 to 2012.
(https://data.ny.gov/Transportation/Motor-Vehicle-Crashes-Case-Information-Beginning-2/e8ky-4vqe)
Queens
New York
Bronx
Hempstead
# of crashes in 2012
(Group by municipality)

2. Day of Week vs. Time in a Day (Weekday)
 Vehicle crashes in weekday has two peaks in a day (about 8:00 and 17:00)

3. Day of Week vs. Time in a Day (Weekend)
 Vehicle crashes in weekend has only one peak in a day (after 12:00 pm)

4. Collision Type vs. Weather Condition
 The part of a car is hit the most under the normal weather condition (clear, cloudy, rain) are in
Rear and Right Angle

5. Collision Type vs. Weather Condition
 The part of a car is hit the most under the unclear weather (snow, sleet, fog) have the same ratio
of collision types

6. Data Mining
 Problem: What factors cause multiple vehicle crashes
 Output variables:
 If this crash has more than 3 cars involved
 Input variables:
 Lighting Conditions
 Road Descriptor
 Traffic Control Device
 Road Surface Conditions
 Year, Day of Week, Time
 Modeling: Bayes Point, Logistics Regression, Decision
Forest, Neural Network, SVM

7. Model Result – Bayes Point as an Example
RecallROC Curve
Precision
 Top 3 variables: Traffic Control Device, Time and Lighting condition
Variable Contribution

8. Model Result Comparison
SVM
Neural Network
Logits Regression
Decision Forest
 Choose Logistic Regression
as the optimal model
 Lighting Conditions and
Traffic Control Device are
the most important factors
Variable Contribution

9. Number of Repair Shops in New York
 Collected 3,836 records of repair shops in New York