Skip to content

Latest commit

 

History

History
51 lines (35 loc) · 1.43 KB

README.md

File metadata and controls

51 lines (35 loc) · 1.43 KB

Data Science for Everyone Workshop

Project Status: Completed

Installation

The repository for this project can be cloned on your device using the commands below:

git init

git clone https://github.com/MSADS-505-Data-Science-for-Business/predicting_employee_attrition.git

Project Objectives

  • To familiarize the users with the basic syntax of R and Python
  • To acquaint the users with exlploratory data analysis (EDA) and pre-processing.
  • To introduce the users to machine learning models while explaining various performance metrics and their relationships.

Partner(s)/Contributor(s)

  • Dr. Ebrahim Tarshizi
  • Erin Cooke
  • Leonid Shpaner

Methods Used

  • Inferential Statistics
  • Data Mining
  • Predictive Modeling
  • Machine Learning
  • Data Visualization
  • Programming
  • Case Study

Technologies

  • R
  • Python

Dataset

Acknowledgements

Thank you Dr. Tarshizi and Erin Cooke for your collaboration on this project. Moreover, thank you to everyone involved as contributors to this repository.

Reference

Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data mining for business
      analytics: Concepts, techniques and applications in Python. John Wiley & Sons, Inc.