Data Analytics Contest organised by ideatory.co Stage 1: Survey rating for various events by users to be predicted Stage 2 : Recommend predictive modeling idea based on daily routine life issues

1. Submitted by: Parindsheel S Dhillon
Contest Organized by: Ideatory.co

2. User Modeling Data Analytics
Contest-Stage 1
Survey rating for various events by users to be predicted
Geospatial data of users provided
Event Details & Geospatial details of events provided
25 million observations of training Data25 million observations of training Data
0.3 million unique user-event combination rating to be predicted
Adopted Analytics techniques
Data pre processing
Ordinal Linear regression, clustering, decision trees
Analytics Tools
R, WEKA, MS Office

3. User Modeling Data Analytics
Contest-Stage 2
Recommend predictive modeling idea based on daily
routine life
Problem scenario
Predictive modeling ideaPredictive modeling idea
Data collection & Data Dictionary
Data Sample(representative)
Data Analytics & Result Delivery

4. Scenario
Obesity is one of the biggest problem
• More than 1.4 billion adults are overweight in 2008 (WHO)
• More than 40 million children under the age of 5 were
overweight or obese in 2012 (WHO)
• More than 2/3 of USA current population is overweight• More than 2/3 of USA current population is overweight
Overweight is leading factor for various diseases
• Cardiovascular diseases, Diabetes type 2, Osteoarthritis &
some cancers like endometrial, breast, and colon (WHO)
Changing lifestyle & eating habits
• Over use of Packaged food containing Trans Fat, sugar & Salt
• Sedentary lifestyle with increase in use of Television,
computer, mobile & sitting jobs.

5. Predictive modeling Idea
By predicting body weight change in 3 months based on
some daily activities
• Many people will foresee their overweight future & its
associated problems
• Create awareness against obesity to save livesCreate awareness against obesity to save lives
• Gym, Health centers etc can also be strategically involved to en-
cash this opportunity by using weight change predictive
modeling
Idea adoption
• Increasing awareness regarding health issues
• Zero figure culture among female population

6. Data Collection
Data collection from below activities
Lifestyle & food intake
Work profile
Additional workout (if any)
Personal & Demographics data
Data Collection ProcessData Collection Process
Food intake data will be collected using a smart phone app.
Daily work out e.g. walking, cycling, running & swimming etc
could also be collected using smart phone app.
Personal & demographic data will be collected when a user
signs up for the app

8. Representative Data
28 number of dependent variables having affect on body weight along with
demographic variables have been suggested. Imaginary data for two
observations is as below
Date CustID Age Sex Weight Height Place Origin
reg
Breakfast
reg
Lunch
reg
Dinner
reg
Other
total
Cal
sugar
Freq
junk
Freq
1/8/2014 1 35 M 65 165 Chandigarh Indian 1000 1000 1200 300 3500 1-2 times 3-5 times
1/8/2014 2 48 F 80 162 Los Angeles American 700 1500 1400 500 4100 3-5 times more than 5
alcohol
Freq
alcohol
Qty
softDrink
Freq
skip
Breakfast
parent
Overwt
medical
Prob
medication
sleep
Hours
quit
Smoke
work
Profile
work
Hrs
fitness
Activity
mins
Fitness
weight
Change
3-5 times 60ml very rarely Nil yes no no 7 no sedentary 8 No 0 ?
Nil Nil
more than
500ml
Nil yes No No 6 no light 10 gym 60 ?

9. Analytics
Data Pre-processing
Variable transformation e.g. Net calorie stored in body
Calorie count might need to be calculated for energy used and
energy intake
Outlier detection & certain medical obesity issues
Statistical techniques for data analyticsStatistical techniques for data analytics
Linear regression (stepwise)
Akaike information criterion (AIC) will be used for relative model
quality
Analysis of Variation (ANOVA)
Time Series can also be used for long time prediction

10. Analytical Result & Delivery
Analytical Result
Body weight change in 3 months based on daily activities will
be predicted for any individual
For longer duration prediction Time series can be used along
with Markov chains analysis
Result DeliveryResult Delivery
Phone application like fat booth need to be accommodated in
original phone app to show the prediction along with weight
bar, photo need to be taken for this additional activity.
Strategic alliance heath centre address & contact can be
forwarded along with the results
General advice like reduction in sugary content or soft drinks
etc can be given to customer based on data

11. Thanks
Parindsheel Singh Dhillon
in.linkedin.com/in/psdhillon