Demo slides for the 'Legislator Prognosticator' project for Insight Data Science.

1. LEGISLATOR
PROGNOSTICATOR
Brenton McMenamin

2. Can we predict what a congressional
representative will do if elected?

3. Party
interests
Voting
behavior✓

4. Party
interests
Regional
interests
Financial
interests
Proposed
legislation

5. APP DEMO
http://www.legislator-prognosticator.xyz/

6. CLASSIFIER VALIDATION
0.5 0.6 0.7 0.8 0.9 1
Firearms
Immigration
Energy & Oil
Housing
Diplomatic decrees
Spending Defecits
Program Budgets
Voting rights
Insurance
Higher education
Opposition to Mandates
Full Model
Money Only
Party Only
Area under the Curve (AUC)
Cross-validatedperformanceofRandomforestclassifiersindicate
thatwecanpredictinvolvementfor11legislativetopics

7. Iusedtodoneuroimagingtostudyhowbrainslearn
andrepresentinformation.
NowI’mlookingforajobwhereIforcemachinesto
learnandrepresentinformation.
Takenoteoftheparticularlylarge“data-scientistlobe”
This is my brain:

8. EXTRA CREDIT!

9. FINDING DONOR CLUSTERS
FECrecordsfrom>20million
politicaldonationsfrom
2010-2015
Donationsfromindividuals
wereaggregatedbasedon
occupation,resultingin55,000
differentdonationsources
Politicianswereclusteredbased
ondonorsimilarityusingGMM
tominimizeBIC
Optimal Bayesian
Information Criterion
(BIC) with 10 clusters

10. DONOR CLUSTER COMPOSITION
Agriculture Cattelman, Sod grower, Rancher, President FOX TV station
Accounting Sentinel Trust Co., Tax Consultant
Transportation &
Energy
Auto dealer, Oil Consultant, Professor/Editor
Insurance
Insurance & Risk Consultant, Insurance & Financial Services, Zurlo
Investment Trust
Executives
Financial communication, Retired ophthalmologist, Auditor,
Professional board member
Small Business
Contract administration, Research attorney, Small business owner,
Provider
Manufacturing & Big
Business
Circuit designer, Oil consultant
Services & Self-
employed
Building contractor, Developmental consultant, Dance Artist,
Beverage & Food distribution
Natural resources &
Land use
National Park Manager, Conservation Advocate/Author, Land
Conservation
Environmental
Environmental educator, Timberland Investments, Library ass't,
Renewable energy developer

11. FINDING TEXT CLUSTERS
Fulltextof>14,000billsdownloadedfromthe
LibraryofCongressandsubjectedtoLatent
SematicIndexing(LSI)
• Tokenizedwithmulti-wordphrases
• tf-idftransformedwordcounts
• LSIplacedeachdocumentin300-
dimensional“topicspace”
Documentwereclusteredinthehigh-
dimensionaltopicspaceusingDirichletProcess
GMMtoidentifytheprimarylegislativetopics

12. TOPICS

13. TOPICS

14. DONORS