This document describes a crowdsourcing system for predicting behavioral outcomes. Users visit a website and provide their outcome value. They can then answer questions and generate new questions, building a dataset. Models continuously predict outcomes from the questions and responses. The system was tested to predict electricity usage and body mass index, and participants successfully identified significant predictors for both. The goal is to demonstrate how non-experts can collectively formulate predictors of an outcome through this system.

1. Crowdsourcing Predictors of
Behavioral Outcomes
Presented by
Alekya.Yermal(Lead)
(10NJ1A0502)
I.Aiysha Sham (10NJ1A0514)
S.M.V.N.Sowndarya(10NJ1A0537)
V.Padmavathi (10NJ1A0542)
Under the guidance of
Mrs.D.Usha Rajeswari

2. CROWD SOURCING
PREDICTORS OF
BEHAVIORAL OUTCOMES

3. Retrieving bulk of data from crowd

4. Introduction
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•Generating models from large data sets—and determining which subsets of data to
mine —is becoming increasingly automated.
• This was accomplished by building a Web platform in which human groups
interact to both respond to questions likely to help predict a behavioral outcome
and pose new questions to their peers.
• Result- dynamically growing online survey, this behavior also leads to models
that can predict the user’s outcomes based on their responses to the user-generated
survey questions.
• Example:
1) Predict users’ monthly electric energy consumption.
2) Predict users’ body mass index.

5. Existing System
• Statistical tools such as multiple regression or neural networks provide mature
methods for computing model parameters when the set of predictive covariates
and the model structure are pre-specified.
• The task of choosing which potentially predictive variables to study is largely
a qualitative task that requires substantial domain expertise.
•Example:
1) A survey designer must have domain expertise to choose questions
that will identify predictive covariates.
2) An engineer must develop substantial familiarity with a design in order to
determine which variables can be systematically adjusted in order to optimize
performance.

6. Disadvantages of existing system
• There are many problems in which one seeks to develop predictive
models to map between a set of predictor variables and an outcome.
• One aspect of the scientific method that has not yet yielded to automation is the
selection of variables for which data should be collected to evaluate hypotheses.
• In the case of a prediction problem, machine science is not yet able to select the
independent variables that might predict an outcome of interest, and for which
data collection is required.

7. Proposed System
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• The goal of this research is to test an alternative approach to model in which the wisdom of
crowds is harnessed to both propose which potentially predictive variables to study by asking
questions and to respond to those questions, in order to develop a predictive model.
• This paper introduces, for the first time, a method by which non-domain experts can be
motivated to formulate independent variables as well as populate enough of these variables for
successful modeling.
• Users arrive at a Web site in which a behavioral outcome is to be modeled.
Users provide their own outcome and then answer questions that may be predictive of that
outcome .
• Periodically, models are constructed against the growing data set that predict each user’s
behavioral outcome. Users may also pose their own questions that, when answered by other
users, become new independent variables in the modeling process.

8. Advantages of proposed system
• Participants successfully uncovered at least one statistically significant predictor of
the outcome variable.
• For the BMI outcome, the participants successfully formulated many of the correlates
known to predict BMI and provided sufficiently honest values for those correlates to
become predictive during the experiment.
• While, our instantiations focus on energy and BMI, the proposed method is general
and might, as the method improves, be useful to answer many difficult questions
regarding why some outcomes are different than others.

9. SOFTWARE CONFIGURATION
Operating System : Windows 7, 8.
Coding Language: JAVA/JSP
Front End design: HTML, CSS, JavaScript
Database : MYSQL
Database Connectivity: JDBC
Server: Apache Tomcat V.7

10. Hardware requirements
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Processor - Pentium –IV
Speed - 1.1 GHz
RAM - 256 MB(min)
Hard Disk - 20 GB
Key Board - Standard Windows Keyboard
Monitor - SVGA

11. Main Modules:-
1. Investigator Behavior
2. User Behavior
3. Model Behavior

12. Investigator Behavior
The investigator is responsible for initially creating the web platform, and
seeding it with a starting question. Then, as the experiment runs they
filter new survey questions generated by the users.
 However, once posed, the question was filtered by the investigator as
to its suitability . A question was deemed unsuitable if any of the
following conditions were met:
(1) the question revealed the identity of its author (e.g. “Hi, I am John
Doe. I would like to know if...”) thereby contravening the Institutional
Review Board approval for these experiments;
(2) the question contained profanity or hateful text;
(3) the question was inappropriately correlated with the outcome (e.g.
“What is your BMI?”).

13. User Behavior
 Users who visit the site first provide their individual value for
the outcome of interest. Users may then respond to questions
found on the site .
 Their answers are stored in a common data set and made
available to the modeling engine.
 At any time a user may elect to pose a question of their own
devising. Users could pose questions that required a yes/no
response, a five-level Liker rating, or a number. Users were
not constrained in what kinds of questions to pose.

14. Model Behavior
•The modeling engine continually generates predictive
models using the survey questions as candidate
predictors of the outcome and users’ responses as the
training data.

15. Architecture

16. System Design

17.  Admin

18. User

19. Use Case Diagram

20. Class Diagram

21. Activity Diagram

22. Sequence Diagram

23. Sample screens of
implementation

24. •Creating user profile

25. •User login page

26. •Home Page

27. User creates quiz question

28. User creates poll question

29. Select quiz

30. Add question to quiz

31. User playing quiz

32. User selects poll

33. Poll question-user

34. User submits poll question

35. Rate quiz

36. Rating

37. Admin login

38. Add question - pool

39. Conclusion
This paper introduced a new approach to social science modeling in which the
participants themselves are motivated to uncover the correlates of some human
behavior outcome, such as homeowner electricity usage or body mass index.
In both cases participants successfully uncovered at least one statistically significant
predictor of the outcome variable.
The main goal of this paper is to demonstrate a system that enables non domain
experts to collectively formulate many of the known (and possibly unknown) predictors
of a behavioral outcome, and that this system is independent of the outcome of
interest.