October Kaggle Dataset Publishing Awards Winners' Interview

This interview features the stories and backgrounds of the October winners of our $10,000 Datasets Publishing Award–Zeeshan-ul-hassan Usmani, Etienne Le Quéré, and Felipe Antunes. If you're inspired to contribute a dataset and compete for next month's prize, check out this page for more details.

First Place, US Mass Shootings - Last 50 Years (1966-2017) by Zeeshan-ul-hassan Usmani

Can you tell us a little about your background?

I am a freelance A.I and Data Science consultant. I have a Masters and a Ph.D. in Computer Science from Florida Institute of Technology. I've worked with the United Nations, Farmer's Insurance, Wal-Mart, Best Buy, 1-800-Flowers, Planned Parenthood, Vicrtoria's Secret, MetLife, SAKS Analytics, North Carolina Health Department and some other small companies, governments, and universities in the US, Pakistan, Canada, United Kingdom, Lithuania, China, Bangladesh, Ireland, Sri Lanka and the Middle East. Currently, I am working on a few consulting assignments regarding the government's use of AI in a cyber-connected world. Here are two of my CNN interviews on the power of datasets and who is joining ISIS. I've recently published a book called Kaggle for Beginners. I have one wife, four boys, two cats and a lovely dog.

What motivated you to share this dataset with the community on Kaggle?

I’ve started flirting with datasets during my Master’s thesis on crowd’s behavior to increase sales, and since then it’s been a continuous affair. I have posted a few datasets in the recent past on Kaggle on Pakistan Drone Attacks, Pakistan Suicide Bombing Attacks, My Uber Drives and My Complete Genome and was surprised to see the results. Altogether, my datasets received close to 7,000 downloads, 123 Kernels, and dozens of comments and forks. I witness the power of a crowdsourced data science community and thought it should be used for a noble cause. The recent mass shootings at Las Vegas concert was a heartbreaker and the first thing that came to mind was how to use Kaggle's data science community to solve or at least understand this issue that is going epidemic in the United States.

What have you learned from the data?

Quite a few things. I see a huge gap in definition and transparency to report such events. Multiple sources report wildly different number of mass shooting incidents in the United States. I went with the FBI’s definition of mass shooting when four or more people got killed or injured. Contrary to popular believe, I also found a good number of white shooters and people with mental health problems (it tells us that these incidents are preventable if we can predict in advance). The dataset also gives me the confidence to use external data sources which may not be considered related to the untrained eye. For example, the correlation with mass shooters and domestic violence or their gaming profiles.

What questions would you love to see answered or explored in this dataset?

I see a lot of good Kernels out there, for example, this Kernel did a wonderful job on an exploratory data analysis, but what I really would like to see is to combine this dataset with external data sources to see if there are any correlations or if there is a way to predict and protect from future attacks. Examples include, other datasets on gun ownership to Federal and State laws and from medical reports to traffic convictions.

Second Place, French Employment, Salaries, Population per Town by Etienne LQ (Etienne Le Quéré)

Can you tell us a little about your background?

I am Etienne, a 23 years old french student who just graduated from engineering school with a master's degree in Operational Research. I'm going to start a PhD in Operational Research soon.

What motivated you to share this dataset with the community on Kaggle?

To help a friend with her job search, I wanted to build an interactive map to highlight where big firms were in France. When I realized that the community liked the piece of dataset I provided, I increased its size with other files to help Kaggler discover the richness of INSEE (France's National Institute of Statistics and Economic Studies).

What have you learned from the data?

Nothing very surprising :

• Big firms are in/around big cities and so are big salaries.
• Sadly salary inequality between men and women in France is still pretty obvious and increase with the job’s qualification and the experience of the employee.

Third Place, Electoral Donations in Brazil by FelipeLeiteAntunes (Felipe Antunes)

Can you tell us a little about your background?

I’m a Senior Data Scientist at Itaú-Unibanco, the largest financial conglomerate in the southern hemisphere. I joined Itaú-Unibanco last year after starting and closing two startups and working for another startup as a lead data scientist. Also, I’m a PhD candidate in physics and my thesis is entitled "Data Science Applications to the Government Sector". My main interests are machine learning methods in complex networks, with a focus on fraud detection. Recently, I was invited to do live coding on Udacity and used Porto Seguro’s Competition as a case of study.  In the past, I was Global Shaper and a TEDx organizer.

What motivated you to share this dataset with the community on Kaggle?

I didn't even know about the prize when I posted the Electoral Donations Dataset. It’s part of my PhD research, regarding the investigation of anomalies in donations made during Brazil’s last elections. There are a lot of accusations that donations have a central role in elections (you can read a few here and here). Using this dataset, I’m able to measure the impact of donations on the electoral results, and determine if there's evidence of fraud using Benford’s law. This is the subject of a paper submitted to Physica A and part of this kernel. More developments can be found on my Github.

What have you learned from the data?

Applying well established statistical techniques and results to data concerning Brazil’s election campaign's financing and results, it's possible to identify strong evidence that democratic principles are corrupted: the determining factor on whether a candidate is elected is the amount of money donated to them. There's strong evidence that fraud has been committed in the financial declarations made by the players. If fraud has been committed in these declarations, it is not possible to really determine how the money came to the candidates and therefore it is impossible to know which interests they will be defending once elected.

What questions would you love to see answered or explored in this dataset?

Here are a couple of questions I'd love to see answered:

• Since we know that the money affects the election results and fraud has been committed in these declarations, could we indicate who are the suspects?
• Who donated to him, and finally, what was their interests (maybe this other dataset could help)?