Company X

HR Analysis

The Company X is a well know travel agengy. The HR Director has recently noticed an increase in employees leaving (attrition). She would like to figure out any trends or patterns. She has surveyed the staff of Company X and provided me the data.

She would like to know what the attrition rate is (% of people who have left).

She would also like to know if factors like age, years at the company and income play a part in determining if people will leave or not.

To achieve the goals, I created this project that contains:

Exploration through the dataset, all the columns that could be usefull to analysing the employees leaving

Decision Tree from Machine Learning to measure the variables importance

Visualize the Age group of Employee and how many of each group leaved

Visualize the Monthly Income by Age Group

Employee count by Income

Visualize the Total Average of Monthly Income and compare with the Average of Monthly Income by Age Group

Employee count by Years working at the company and including the attriton

Available on Tableau

The Results Briefing

The patterns that I found out using python indicates a lower Attrition Rate
but most common in employees who have Gender Male, are Single and Travel Rarely.

The Decision Tree highlights other sources:

Total Working Years

Stock option Level

Age

Monthly Income.

Although the Decision Tree accuracy is not bad, it is not enough to analyze the situation, patterns and possibilities for improvement.

So, to help the HR team and the decision makers, I developed a Story available on my Tableau page