if you’re planning to fly with Ghana Airways in December then a good book to read in the airport might be needed!
Conclusion
This is a rich dataset despite the relatively small number of variables that are provided. In the analyses shown here only a very small subset of the questions that could have been investigated have been looked at, but this has already yielded some rich insights into the changing patterns in air travel delays in the UK in the 21st century.
Analytic blog posts about the World cyprus mobile numbers Cup are very popular at the moment, so here’s my effort. I picked Tunisia as they’re my team in the work sweepstake and – and therefore of me picking up the prize money.
In this blog post we’ll undertake some general exploratory data analysis and use the Apteco FastStats Segmentation tool to look at teams who have remained unbeaten for significant periods of time with the aim of showing that Tunisia are a better team than their betting odds would suggest.
The Data
The dataset we’ve used for this analysis has come from two different sources, and is more comprehensive than just matches from the football World Cup. We’ve pulled international match result data from Kaggle (1), and domestic football competition data from an R datasets package (2). This domestic competition data comprises matches from several of the European leagues. The data fields provided for each of the leagues and the international matches differ, so we’ve created a FastStats system that has standardised the available variables.