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Popular comments by tomislavk

Datathon Telenor Solution – Analysing and Predicting Delays in Mobile Data Connectivity

Team, for any “scientific” article and for anybody who has access to data, whole process should be repetable, i.e. anyone should be able to take your code/work and get same end results. Good part is that you already presented lot of code you used, but bad part is that it is either attachment (not clearly visible process, or embeded as picture). If you are able, I would like that you add those as textual part of your article for easier verification.

The Telenor Case – Social Network Analysis

Interesting case and let’s try to do more than required and described within Research Problem paragraph. I will be part of this case as one of mentors. Previosuly I implemented multiple SNA solutions with focus on Telco domain, but also in Banks, Insurrance, etc., so please feel free to contact me and link with me in DSS chat esp. if you need someone for your team (covering both business and technical side).

Weather Disruption of Public Transport Analysis Using Python

Also, I would advise using some additional datasets which were not part of the initial dataset, like aggregated daily traffic estimates on an hourly basis provided by some navigation applications because that can additionally help with model precision. We all know that bus driers should be professionals but the majority of “normal” non-bus driers are not and they are heavily impacted in distracting sensor inputs (thunderstorm, rain, people cutting in, or even forgetting how to drive when weather condition changes). – I’m adding my last sentence about additional dataset to all teams focusing on this problem because no one did even consider it and that is something you can always do on any project – focus not on internal/provided data but find something to augment it 😉

Weather Disruption of Public Transport Analysis Using Python

The same segments repeat across article.
The entire focus of the article is based on data analysis where we are missing all models which are aligning two datasets together and finds appropriate correlation and causality in data.
I know that time was short, so I would recommend teaming with someone else next time so that work can be split among team members.