Recently I needed some NetFlow data samples, I’ve looked all over the internet for some of those, but for obvious privacy reasons there were none. No one shares their NetFlow data. Not even a little sample. So what could I do, I had no Cisco equipments to generate traffic on and then to collect it in data flows. So I’ve improvised by using my laptop as a router in the campus network and collecting the traffic that went through it in data flows. This post is about how to generate and collect Netflow data on your own network.

Continuing the Outreachy article series, this article describes the outcome of applying linear regression to the m-lab data and the first steps in establishing a model of correlation between the socio-economic factors (population and median income) and the internet speed and availability for the New England region.

This article is the second one in the m-lab Outreachy series and it describes the process we followed in obtaining a cluster visualization and the following analysis of the internet speeds and characteristics for USA using K-means. If you remember from the previous article the purpose of this project is to be able to state some facts about the internet connection speed/availability in different parts of the United States and how those speeds correlate with socio-economic factors. The next step we decided on was to do a cluster analysis and see if any communities are clustering together based on their internet speed (upload and download speed) and RTT (round time trip) and then observe if any of those cluster share some socio-economic traits.

This article will be one of a few articles done during my internship with the M-Lab organization link: in the Outreachy Summer 2015 program. During this time I will work with different data from link: and link: and probably other sites to try and establish a correlation between the internet broadband connection penetration rate in a community (or how many people have high-speed internet) and the characteristics of that community. Basically, what I want to do is to characterize the communities with internet connection and those without and see why the former are attractive to the Internet Service Providers and the latter are not. (Maybe some socio-economic factors are influencing the availablity of internet connection: like income, education, age, race).

I hope this to be one of the many to come short and to the point posts about different aspects of data analysis and how to tackle data analysing problems. The purpose of this is to be a short refresher course in different core aspects of machine learning and data analysis. This being said the first problem I tackle is the Conditional probability.