The document presents a study on predicting corporate relocations using a dataset of 2 million companies over a 10-year period, focusing on identifying factors that may signal relocation. The study faced challenges related to data granularity and platform limitations but ultimately used classification algorithms to analyze company characteristics, finding financial health and age as significant predictors. Key takeaways highlight the feasibility of conducting big data projects within a short timeframe and potential avenues for future research and model enhancement.

1. Corporate
Relocation Prediction
A 10 Year, 2 million company use
case
Rahul Shetty, Ana Maldonado, Mauricio Rodriguez Lara

2. Who We Are

3. Who We Are

4. Relocation Prediction use case
Problem:
businesses, schools, hospitals, etc. move locations over time
(growth, bankruptcy, new markets, etc)
Can we predict if they will relocate?
- To where?
- When?
- Why?
=> For now, we focus only on
relocation probability

5. For businesses we have historical Corporate Data:
- Company size, credit rating, relocation, etc...
=> Can company characteristics predict relocation?
- Useful information for service providers, realtors, city councils,
investors and developers
=> Investigatory POC: 6 week study
- Limit the scope to determine if relocation can be predicted, and
if so, which properties can be a signal
Relocation Prediction use case

6. (Big) Data
We encountered some challenges:
- Monthly data from branches of 2 million companies, going back
10 years… ~ 300 million rows
- Disperse Data: where/how should it be gathered?
- Monthly data too granular: how to aggregate?
- Client did not have a suitable platform for data handling and
analysis...

7. Data & Modeling Considerations
- High dimensional time series data
- Preserve the temporal granularity to maximize information
- Neural Networks?
- LSTM or CNNs?
- NN design/exploration time > available time
- Simplify data and modeling due to time constraints

8. Preparing the Data
- Step 1: Collect the data on an appropriate platform:
- Set up Google Cloud platform in one week
- Step 2: Aggregate the Data
- From monthly to yearly: predicted relocation based on
yearly data
- Choose how to deal with categorical variables
- Subsequent Steps : Spawn virtual machine(s) on GCP for
modeling

9. Summary Statistics
- Final dataset: 75 features from 1 year and ‘has_relocated’
target from following year
- 2 million entries per year
- ~5% relocation (imbalanced dataset)
- Goal: Build a model that can predict ‘has_relocated’
better than randomly (better than 95% accurate)

10. Modeling step 1: Exploring Models
- Apply binary classification algorithms: SVM, logistic regression,
decision trees (DT), random forests (RF)
- Choose models with best performance: AUC, kappa
- DTs and RF did best
- Apply Sampling Techniques to improve models
- Tune model parameters
- Validate

11. Modeling step 2: ResultsTPR
FPR
AUC: 0.66
Best DT model produced
by undersampling data,
5-fold CV, and DT
parameters explored via
grid search

12. Modeling Results: Features
The most important
features having an
influence on a
‘has_relocated’ index
were related to:
- Company financial
assessments and
health
- Company age

13. Validation
How well can yearly models
predict the next year’s
relocation?
AUC

14. Validation
How well can yearly models
predict the next year’s
relocation?
… in general, rather well
AUC

15. Validation
How well can yearly models
predict the next year’s
relocation?
… in general, rather well
… except for 2016 (?)
AUC

16. - Company properties can be indicative of whether they relocate
- Yearly aggregated data is sufficient for high level indications of
relocation.
- More granular modeling (e.g. with NN) may provide additional
information
Take aways
- Possible to perform successful
POC on big data within 6 weeks
on GCP

17. Having had more time we would have:
- Full time series modeling
- NN, hierarchical modeling, etc...
- Automate prediction, given company characteristics
- Investigate anomalous year
- Make use of modeling results:
Future work

18. Any questions?
rshetty@qualogy.com
Thank you!