Employee-Performance-Prediction-using-R-Shiny-and-Random-Forest-classifier

Human resource has become one of the main concerns of managers in almost all types of businesses which include private companies, educational institutions and governmental organizations. Business Organizations are really interested to settle plans for correctly selecting proper employees. After hiring employees, managements become concerned about the performance of these employees where management build evaluation systems in an attempt to preserve the good performers of employees. Using data mining techniques we are trying to predict an employee's performance band based on their historical evaluation and performance reviews.

There are several attributes affecting the employee performance. Some of these attributes are personal characteristics, others are educational and finally professional attributes were also considered to predict the employee performance. In this project, our aim is to forecast an applicant’s talent based on information filled in the HR application, past experience, and employee reviews and evaluations assessments using Data Mining techniques such as a Random Forest Classifier. So, we have specified certain factors to be considered as attributes for the model, such as, Age, Education level, Performance Rating 1, Performance Rating 2, Performance Rating 3, Performance Rating 4, Building Team Commitment, Strategic Thinking Leads Decision Making and Delivers Results, Analytical Thinking, Customer Relations Service Quality and Planning, Solution Selling In Market Execution, Sales Planning and Forecasting, Negotiation, Actionable Insights, Solving Problems, Engage, Applied Thinking, Change, Drive, Average Comp Score.

Hosted application link: https://mlp9csk.shinyapps.io/PERFORMANCE_PREDICTION/?_ga=2.253854728.1025431563.1603128032-1140325050.1602692543

Please ignore the error message, upload the data set and you are good to go.

Steps:

1. Upload the data from repository.
2. Select the employee ID, you would like to predict the performance for.
3. The output shows a bar plot representing the employee's past performance bands and the predicted local band. Here the local band is a term assigned for an employee's performance level based on a predefined set performance level by the company.