MSDS 6306 *SMU Doing Data Science

This Repository is for Clark Consulting's Attrition and Salary analysis for Frito Lay Employees

Introduction The modern workforce in the United States is becoming more fluid than ever before. As the cost of living in the country continues to increase, wages are remaining stagnant, as reported in the latest economic trends. A result of this, we are seeing more employees shift between companies after just a few years and using the larger pay raise associated with switching jobs (as compared to staying put a single job) as a way to bolster their income to make a living.

To combat this trend and potentially get a closer feel of the status of each employee and their potential for attribution, we are going to conduct a data analysis to identify trends in the employee base job titles, overall sentiment as well as salary with hopes to be able to predict which employee is likely to leave the company and what salaries are associated with the employee’s current standing.

Repository Contents: CaseStudy2-data.csv - Traning dataset of Frito Lay Employees (includes salary and attrition) CaseStudy2CompSet No Salary.csv - Includes Testing Set of Frito Lay Employees (does not include salary data for prediction) CaseStudy2CompSet No Attrition.csv - Includes testing Set of Frito Lay Employees (does not include Attriton) Codebook.html - HTML version of description of data for Training and testing sets DanielClark_DDS_CaseStudy2.Rmd - R Markdown file of data cleaning and analysis DanielClark_DDS_CaseStudy2.html - HTML knit of data cleaning and analysis process MSDS_DDS_CaseStudy2.ppt - PowerPoint used in presentation to FritoLay executive team

Author: Daniel Clark

Contact info: [email protected]

MSDS_DDS_CS2

Youtube presentation - https://youtu.be/YTCdQHtOVpw