There are many proponents of democracy and how it is the greatest policy. The question is then “Is (true) democracy a suitable policy for every country?”. I was having a discussion with my girlfriend a couple of days ago on US policy and I just thought that the concept of “ensemble methods” from data science could be used to understand the mathematics behind it.
My previous post documented the method I’ve used to rank the various Pokemon Nests. For many beginners, the whole process seemed to be a little lengthy. In this post, I’ll be showing a much faster way to search for the nests amidst the noise using another clustering algorithm, DBSCAN. I’ll be using the Dratini Dataset for this process.
Last Sunday I’ve the honour of introducing basic concepts of data mining to a group of enthusiast who signed up for the talk on my blog. During the talk, they were introduced to how I collect the data, how the data were processed and how the data were data mined. They also have a first-hand experience of analysing the nest through the clustering technique. In this post, I’ll be walking through the steps to find and rank the Pokemon nests. The data used for the analysis can be downloaded in this post, so make sure you follow along!
In my previous blog post, I’ve mentioned that I’ll be releasing the Pokemon spawn data used for the analysis as well as to provide more details to the upcoming data mining w/ Pokemon talk. Apologies for the delay, I’ve been bogged down by lots of work (and holiday). For that, I’ll be releasing a new set of data that I’ve not worked on. Go ahead and write about the nest migration!
And now… Here’s the information about the upcoming data mining talk!
First of all, thank you for your support for the previous posts. If you’ve missed the post about ultra-rare Pokemon, it’s here. If you’ve missed the one on the starter Pokemon, it’s here . This post will cover the remaining Pokemon nests in Singapore, featuring Dratini’s nest for players to farm up their Dragonite.
With this post, most of the Pokemon nest will have been revealed with the exception of Gastly, Hitmonlee, Hitmonchan & Likitung. The reason for leaving that 4 Pokemon out is that their appearance rates belong to the Ultra-rare group. To view the Pokemon nest plotted on Google map, scroll to the bottom of the entire post.
The last Pokemon nest map featuring Chansey, Lapras, Porygon, Aerodactyl, Snorlax and Dragonite was very well received. And as promised I’ve added more Pokemon nests to the map. This time featuring the starter Pokemons, Clefairy, Vulpix, Jigglepuff, Diglett, Growthlith and Onix!
As Google map only allow a limited number of layers, I’ve placed the new nests on another map. The links are as followed:
Map 1 (1, 4, 7, 25, 27, 35, 37, 39, 50, 52)
Map 2 (56, 58, 95)
Map 3 (113, 131, 137, 142, 143, 149)
So every blog/company is jumping on the Pokemon bandwagon to “unveil Pokemon nests around Singapore”. Here’s one more, but backed with data.
I’ll be revealing what data has revealed about the pattern of Pokemon spawns in Singapore in this post. In another post, I will discuss about the data collection, dataset and techniques used. I’ll try to leave technical findings and discussions to the other post and leave the juicy ‘actionable’ findings here.
Update: New Pokemon nests has been uploaded and can you find them in this post.
So many time I’ve told my girlfriend that a particular startup is dead (or in most of the aspects, have no future). There is so many ways a startup could die. From having no money, to having too much money. From having too many specialist to too many generalist. During one of the coffee breaks I had with my interns I told him Captivoo is dead. We were on the topic of the future of the company…
Another two weeks passed for my internship. The last two weeks was spent on marketing. Pure marketing. One of our experiments hit a jackpot and I’m here to share what works.
So its been a couple of weeks since I’ve worked full time in Captivoo. Now that the first phase of product development is completed, I’ve shifted focus to sales and marketing. How will things look like when you put a computer scientist into sales and marketing role?