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Data science and AI solution accelerator suite that provides templates for prototyping, reporting, and presenting data science analytics of specific domains

Home Page: https://github.com/Microsoft/acceleratoRs

License: MIT License

HTML 95.96% Makefile 0.03% Jupyter Notebook 3.71% R 0.23% JavaScript 0.01% Shell 0.01% Python 0.05% Dockerfile 0.01%
r data-science

accelerators's Introduction

Introduction

acceleratoRs are a collection of R/Python based lightweight data science and AI solutions that offer quick start for data scientists to experiment, prototype, and present their data analytics of specific domains.

Each of accelerators shared in this repo is structured following the project template of the Microsoft Team Data Science Process, in a simplified and accelerator-friendly version. The analytics are scripted in R markdown (Jupyter notebook), and can be used to conveniently yield outputs in various formats (ipynb, PDF, html, etc.).

How-to

  • To start with a new acceleator project, use GeneralTemplate for initialization. The GeneralTemplate consists of three parts which are Code, Data, and Docs.

    • Code - Codes of analytics for the data science problem is put in the directory. R markdown is recommended for scripting as it is easy to yield pure code as well as report in various formats (e.g., PDF, html, etc.) for the convenient of presenting.
    • Data - Data used for the analytics. It is highly recommended to put sample data in the dictory while providing reference to full set of it.
    • Docs - Normally related documentations, references, and perhaps yielded reports will be put in this directory.
  • An accelerator should be able to run interactively in an IDE that supports R markdown such as R Tools for Visual Studio (RTVS), RStudio, VS Code with AI extensions.

  • Makefile is by default provided to generate documents of other formats, or alternatively rmarkdown::render can be used for the same purpose.

Contributing

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

accelerators's People

Contributors

avsharapov avatar benmarwick avatar gjwgit avatar hongooi73 avatar microsoftopensource avatar msftgits avatar olgavlpetrova avatar yueguoguo avatar zhoufang928 avatar

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accelerators's Issues

problem with genSequence function

Hi, I'm trying to reproduce the tutorial locally and I have the following error:

> seq_train <- genSequence(df_train)
Generating data ...
Error in y[index, 1] <- reading_y : 
  number of items to replace is not a multiple of replacement length

I'm running it on Windows 10 with Microsoft R Open 3.4.1.

ProductDemandForecast - links to data files broken

Hi Team, I have just started exploring accelaratoRs templates and started with ProductDemandForecast. In the below data page, the links to the csv files seem to be broken. Can some one please look into this?

Page where issue is found: https://github.com/Microsoft/acceleratoRs/tree/master/ProductDemandForecast/Data

Non working links on the page:

  1. https://github.com/Microsoft/acceleratoRs/blob/master/ProductDemandForecast/Data/productQuantity.csv
  2. https://github.com/Microsoft/acceleratoRs/blob/master/ProductDemandForecast/Data/productCategory.csv

How to load MS text translator API

Hello,
I'm running your code to understand. At the end of the source code, there's a step where you use MS translator API. I'm having a difficulty to load the API key with the following code line. Any tips might be appreciated.

source("path_to_your_confidential_information")

No data in Data folder

Hi Microsoft team,
Your project is great. However, I could not find data in CreditRiskPrediction project.
Could you update data?

CreditRiskPrediction Data Oddities

I might use the credit risk prediction data for teaching so I have a few questions:

  1. There are a lot of negative values for creditLimit and income. Is there are reason for this?

  2. The distribution for income is fairly pathological. Is this common for the situation or an issue with the simulation?

Thanks

(edited since I figured one question out)

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