Employee-churn-prediction

This code compares performance of Random Forest, AdaBoost and GradientBoost

Data

Employees.csv contains information about 1470 employees of a certain company. There are 26 data variables. All variables are self-explanatory. Additional information about a few variables is as follows:

Education

1 'Below College' 2 'College' 3 'Bachelor' 4 'Master' 5 'Doctor'

EnvironmentSatisfaction

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobInvolvement

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

JobSatisfaction

1 'Low' 2 'Medium' 3 'High' 4 'Very High'

PerformanceRating

1 'Low' 2 'Good' 3 'Excellent' 4 'Outstanding'

WorkLifeBalance

1 'Bad' 2 'Good' 3 'Better' 4 'Best'

Task

Visualize natural groupings or clusters of employees using unsupervised machine learning and dimensionality reduction techniques – k-means clustering, PCA and t-SNE. The goal is to interpret these groupings to extract meaningful insights about the employees.