Eskimo is a Golang service that handles User Account Management on the ice network.

Team : 'Escimo' . Team project Ice Cream

eskimo is an entity-component system written in Haxe, focused on having a small codebase, simple API, and performance.

Assists in creating 3d scratch holograms (aka abrasion holograms or chatoyant holograms). They can be created on acrylic or metal sheets using only an adjustable compass as described on Bill Beaty's website: http://www.eskimo.com/~billb/amateur/holo1.htm

Eskimo is a state of the art Big Data Infrastructure and Management Web Console to build, manage and operate Big Data 2.0 Analytics clusters on Kubernetes. This is the git repository of Eskimo Community Edition.

snowflake is a simple way to create a landing page built on top of eskimo.io

Husky is a loyal and hardworking watchdog that alerts eskimo's and delivers them their news and notifications.

Yup'ik Eskimo morphological parser, dictionary look-up, machine translation pre-trained models

(Deprecated - use Eskimo) A simple entity system written in Haxe - Round 2

eskimo is a wrapper for all major linux distro package managers (apt, pacman, dnf, zypper)

2D platformer made with SpriteKit

An open source Flex mobile library providing native control patterns to your apps on multiple mobile platforms.

https://forums.developer.apple.com/thread/8030

In this assignment students need to predict whether a person makes over 50K per year or not from classic adult dataset using XGBoost. The description of the dataset is as follows: Data Set Information: Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)) Attribute Information: Listing of attributes: >50K, <=50K. age: continuous. workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. fnlwgt: continuous. education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. education-num: continuous. marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Profspecialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black. sex: Female, Male. capital-gain: continuous. capital-loss: continuous. hours-per-week: continuous. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

A framework for 2D games in JavaScript with the canvas.

Eskimo — Fetch The Dogs 🐕 Eskimo is a simple web application that interacts with the Dog CEO API to fetch a list of dog breeds and display images for each breed in a carousel format. It allows users to select from various dog breeds and view up to 10 random images for the selected breed.

No description available

ESKIMO Fishing Trip game source

My personal blog, mostly about discoveries I've made

Eskimotron - A little trading app originally designed for Monero (XMR). Works with Poloniex and Bittrex and supports other currencies.

# Machine Learning Engineer Nanodegree ## Project 3: Finding Donors for CharityML ### Project Description This is the 3rd project for the Machine Learning Engineer Nanodegree. In this project, I used sklearn and supervised learning techniques on data collected for the U.S. census to help a fictitious charity organization identify people most likely to donate to their cause. Here, I first investigate the factors that affect the likelihood of charity donations being made. Then, I use a training and predicting pipeline to evaluate the accuracy and efficiency/speed of three supervised machine learning algorithms (GaussianNB, SVC, Adaboost). I then proceed to fine tune the parameters of the algorithm that provides the highest donation yield (while reducing mailing efforts/costs). Finally, I also explore the impact of reducing number of features in data. ### Install This project requires **Python 2.7** and the following Python libraries installed: - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org) - [matplotlib](http://matplotlib.org/) - [scikit-learn](http://scikit-learn.org/stable/) You will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html) We recommend students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. ### Code The main code for this project is located in the `finding_donors.ipynb` notebook file. Additional supporting code for visualizing the necessary graphs can be found in `visuals.py`. Additionally, the `Report.html` file contains a snapshot of the main code in the jupyter notebook with all code cells executed. ### Run In a terminal or command window, navigate to the top-level project directory `finding_donors/` (that contains this README) and run one of the following commands: ```bash ipython notebook finding_donors.ipynb ``` or ```bash jupyter notebook finding_donors.ipynb ``` This will open the iPython Notebook software and project file in your browser. ### Data The modified census dataset consists of approximately 32,000 data points, with each datapoint having 13 features. This dataset is a modified version of the dataset published in the paper *"Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid",* by Ron Kohavi. You may find this paper [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf), with the original dataset hosted on [UCI](https://archive.ics.uci.edu/ml/datasets/Census+Income). **Features** - `age`: Age - `workclass`: Working Class (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked) - `education_level`: Level of Education (Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool) - `education-num`: Number of educational years completed - `marital-status`: Marital status (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse) - `occupation`: Work Occupation (Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces) - `relationship`: Relationship Status (Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried) - `race`: Race (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black) - `sex`: Sex (Female, Male) - `capital-gain`: Monetary Capital Gains - `capital-loss`: Monetary Capital Losses - `hours-per-week`: Average Hours Per Week Worked - `native-country`: Native Country (United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands) **Target Variable** - `income`: Income Class (<=50K, >50K)

Implementation of Dijkstra and A-Star w/wo using Dubins path

Declarative text formatting for Ruby.

Eskimo Theme Version 1

Task Manager using Electron, written in JavaScript

A Wordpress theme framework developed for rapid theme development.

Eskimo Studio รับออกแบบโลโก้ เอสกิโม เราคือชนเผ่าที่แตกต่างทางความคิด สร้างสรรค์ผลงานด้วยไอเดียแปลกใหม่

eSKIMo is a text skimmer and labelling tool built using TensorFlow in Python. The project is hosted on the provided weblink with a React.js framework.

Path planning algorithms

My blog hosted on eskimon.github.io