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Data Mining – Top 10 Rarest Gen II Pokemon in Singapore

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Overview

Right after the release of the 2nd Generation Pokemon in Pokemon Go, I’ve got my scanners working and collecting Pokemon spawn data. Over the last 24 hours (minus a few hours of downtime), I’ve collected 182,898 individual spawn data for Gen II Pokemon.

As I begin working on the data, here is a quick glimpse of how rare different Pokemon are.   

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Trade Bitcoins in Singapore

Buy/Sell Bitcoins in Singapore

Buy Bitcoins

Sell Bitcoins

Buying and selling bitcoins in Singapore can be very perplexing for first-time buyers.

As an early adopter of technologies, I’ve been there myself. I’ve got my first bitcoins on Localbitcoins back in 2014. Since then, I’ve dealt with various platforms like Remitano, CoinHako, etc.

However, I’ve realised that most people do not want a deep dive into bitcoins on day one. They just want to get their hands onto a bitcoin or two without the hassle of understanding how bitcoin works!

While I trade bitcoins on major marketplace, where they provide a safer environment for everyone, I’ve offered my buyers/sellers options of conducting the trades off such platforms to receive better pricing.

So I’ve thought of extending this service to you if you would like to buy/sell bitcoins in Singapore.

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Finding Dratini Nest w DBSCAN

dbscan-dratini

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.

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Step-by-step Pokemon Go Clustering

step-by-step-clustering

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!

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Data Mining Talk & Release of Dataset

dataminingwpokemon

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!

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Latest Pokemon Nest from Data Mining (ft. Dratini Nest)

Data Mining Pokemon Nest (Part 3)

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.

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More Pokemon nests in Singapore unveiled through data mining

New Pokemon Nest

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)

Bulbasaur, Charmander, Squirtle, Sandshrew, Pikachu, Clefairy, Vulpix, Jigglypuff, Diglett, Meowth

Map 2 (56, 58, 95)

Mankey, Growlith, Onix

Map 3 (113, 131, 137, 142, 143, 149)

Chansey, Lapras, Porygon, Aerodactyl, Snorlax, Dragonite

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Data Mining Pokemon Spawns in Singapore

data-mining-pokemon-singapore

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.

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Staring at the Grim Reaper in the Eyes

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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…

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Bullets then Cannonballs

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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.

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