heartseed's Stars
cdnjs/cdnjs
🤖 CDN assets - The #1 free and open source CDN built to make life easier for developers.
dropwizard/dropwizard
A damn simple library for building production-ready RESTful web services.
cheft/sanguo
三国霸业乃儿时的步步高经典游戏，三国更是一生的爱好。
chyyuu/os_kernel_lab
OS kernel labs based on Rust/C Lang & RISC-V 64/X86-32
lining0806/PythonSpiderNotes
Python入门网络爬虫之精华版
runshenzhu/6.828-MIT-OS
The implementation of MIT's 6.828 Operating System Engineering
jlsy415/uberATC
Data Visualization for UberATC data
alex-grover/CS244b-final-project
Scalable web cache using consistent hashing for Stanford CS 244b.
TomLous/coursera-cryptography1
Assignments for Coursera's Cryptography I course by Dan Boneh
dataArtisans/flink-dataflow
Google Dataflow Runner for Apache Flink™ (deprecated; please use the up-to-date Beam Runner)
apache/flink
Apache Flink
PokemonGoers/PredictPokemon-2
In this project we will apply machine learning to establish the TLN (Time, Location and Name - that is where pokemons will appear, at what date and time, and which Pokemon will it be) prediction in Pokemon Go.
quantopian/zipline
Zipline, a Pythonic Algorithmic Trading Library
quantopian/qdb
Quantopian Remote Debugger for Python
iam4x/pokemongo-webspoof
👾 Play Pokémon Go from your Mac
xunzhang2/pds_final
Zarel/Pokemon-Gen-6-Learnsets
PS's attempt at data collection for gen 6 learnsets
JampaUchoa/PokemonGo-Dump
Data dump made of Pokemon GO spawns
5andr0/PogoLocationFeeder
Json feed of pokemon location data
sunshineatnoon/Scrapy-Amazon-Sqlite
This is a project crawling backpack information and images from Amazon using python scrapy and store data to sqlite database.
jseabold/538model
538 Election Forecasting Model
zhaochuanshen/forest_prediction
Forest Cover Type Prediction -- a Kaggle Competition
Brideau/pokelyzer
A webhook listener and database schema for doing geospatial analysis and advanced analytics on Pokemon Go data.
visavishesh/clusters-and-pokemon-python
Web Scraping & Python & Hotspot Cluster Analysis
PokemonGoers/PokeData
In this project you will scrape as much data as you can get about the *actual* sightings of Pokemons. As it turns out, players all around the world started reporting sightings of Pokemons and are logging them into a central repository (i.e. a database). We want to get this data so we can train our machine learning models. You will of course need to come up with other data sources not only for sightings but also for other relevant details that can be used later on as features for our machine learning algorithm (see Project B). Additional features could be air temperature during the given timestamp of sighting, location close to water, buildings or parks. Consult with Pokemon Go expert if you have such around you and come up with as many features as possible that describe a place, time and name of a sighted Pokemon. Another feature that you will implement is a twitter listener: You will use the twitter streaming API (https://dev.twitter.com/streaming/public) to listen on a specific topic (for example, the #foundPokemon hashtag). When a new tweet with that hashtag is written, an event will be fired in your application checking the details of the tweet, e.g. location, user, time stamp. Additionally, you will try to parse formatted text from the tweets to construct a new “seen” record that consequently will be added to the database. Some of the attributes of the record will be the Pokemon's name, location and the time stamp. Additional data sources (here is one: https://pkmngowiki.com/wiki/Pok%C3%A9mon) will also need to be integrated to give us more information about Pokemons e.g. what they are, what’s their relationship, what they can transform into, which attacks they can perform etc.
PokemonGoers/Catch-em-all
Now that we have tons of data about Pokemon (what they are, where they are, what’s their relationship, what they can transform into, which attacks they can perform, aso) we want to integrate it all into a comprehensive website. This website should contain sections about each Pokemon and its details. Additionally, the website should register the user’s location and tell the user how close is that the predicted pokemon to him/her. Additionally you will be incorporating the apps that were created by project B,C and D into the website. Your group will need to create automated builds and testing for this apps and use continuous integration to pull in new changes in the code repositories. Apps from projects B-D should be packaged and made available on NPM. Ideally when you completed these tasks the webapp component would integrate the apps by “requiring’ them. Here is a possible user story: when a user opens the website or the app the current location of the user will be shown. Additionally, the website/app will show automatically where the pokemons that are currently active are and where the pokemons that we predict to active in the nearest future (i.e. within half a day) will be located (all of this will be available from the app developed in project D). Hopefully, the website will be somewhat crowded by that data. Then, there needs to be a menu bar or something available (e.g. above the map or on the right side to it) that will list currently active or predicted pokemons. Clicking on one of them will make other pokemons on the map disappear, except of this clicked one. Separate web pages would allow the search and presentation of individual Pokemons and the information we gathered about them, including third party data (project A) and twitter analysis (project C)
sjyk/mldb
Machine Learning Debugger
blerim153/kaggle_airbnb_new_user_bookings
pbamotra/Res-15513
Resources for 15513
seanpor/Firefox-2013
A restricted dataset of bugs in Firefox extracted from the academic release of the bugzilla dataset. This dataset is intended for the use of software reliability researchers.