Today is a freaking cool day. Why do you ask? Because today I am writing an article on how to use two of the coolest freaking big data/data science tools out there together to do epic shit! Lets start with HBase. HBase is a way to have a big data solution with query performance at an interactive level. So many folks are starting to just dump data into HBase. In the project teddy solution, we are dumping tweets, dialogue and dialogue annotations to power our open domain conversational api. There really is no other way that is easy to use for us to do this.
The second part of project teddy is to predict based on an incoming conversational component, what sort of response the speaker is attempting to illicit from the teddy bear. If we power our teddy bear with predictive analytics and big data, this would be perfect. What better platform to do this quickly and easily than AzureML?
Many folks may know that the South Florida Evangelism team is undertaking a task that many think is impossible. Well, in that statement all I hear is “there is still a chance!” The end goal is to create a teddy bear that can have a conversation about anything. So step one is to collect as much dialogue as possible from as many sources as possible and annotate them. What better place to power an association engine for word and phrase relevance than something that forces you down to 140 characters to get your message across.
So as any normal developer I decided to start by looking for samples already out there. MSDN has a great starter for writing tweets and doing sentiment analysis with HBase and C#. The only issue with the sample is, that it is very poorly written and difficult to understand with no separation of concerns. So I want to go through simplifying the solution and separating a few concerns out.
So I had a life changing event this past Sunday at 8:55am 5/24/2015. My first child was born! Both child and wife are healthy and happy. Everything is good in life. Like many couples though, my wife and I struggled to find the right name for our child. We didn’t want something too common, or was an old person name, or so rare and funky that nobody could spell it. We also realized we just had a general lack in knowing what names were out there. So after much debate and discussion over what to name her, I started doing a bit of an analysis using some census data. I want to thank Jamie Dixon for providing the data that he found for use in his Dinner Nerds article. The data itself can be found here. This article will discuss the code used to go through all of the data and provide insights into child names.
As many of you may know at this point, I am relocating to South Florida. Final location to be determined, but will probably be renting around Pompano Beach or Fort Lauderdale while working out of Venture Hive and the Microsoft Fort Lauderdale Offices. So what does this have to do with Zillow? Well, It has EVERYTHING to do with Zillow. What I’ve found while searching for homes is that between Realtors, Zillow and Trulia, they really just don’t have a predictive analytics solution that works for me. So I decided to give a shot at AzureML to mash together a few datasets to send me notifications more to my liking than is currently being sent. So step 1 in this plan is to data mine Zillow. Luckily, Zillow has an api for that. Or if you are feeling particularly frisky, Zillow gets their data from ArcGIS (example for Raleigh). So lets get cracking…
I have recently been informed that many of my articles may be a bit advanced for folks, so I am going to kick off a series of C# articles dedicated to the Beginner to programming. I have no idea how long this series is going to be, I’ll just keep adding to it as requests come in for various topics. This series is meant to take the absolute beginner to a level in which they can possibly derive value from my other articles. Those of you who do Jiu Jitsu with me, know you have to shrimp before you can roll, so this is sort of like shrimping.
Welcome to Part 2! We will be discussing Binary Classification. So I hope many of you have started using AzureML. If not, you should definitely check it out. Here is the link to the dev center for it. This article series will focus on a few key points.
Understanding the Evaluation of each Model Type.
Understanding the published Web Service of each Model
If you are looking for how to build a simple how to get started, check out this article.
The series will be broken down into a three parts.