Anytime an employee is hired, it is a massive gamble for the company. Ensuring they did the right job, they work feverishly, checking the resume, conducting interview after interview, maybe an assessment or LeetCode challenge to hire an employee, and desperate that they made the right choice out of the other 500 applicants. So much effort has gone into one person. The last thing this company needs is another employee gone, creating a vacancy to fill. So, what is it that makes an employee leave? How can we as a company predict and see what may influence an employee to leave, so before they do, we handle it and keep them satisfied? This data story will walk that path, and in the end, build a machine learning model to predict it in the future.

This is a step-by-step representation of my first analysis in R, using the R Markdown as well. I used the diamonds dataset from the ggplot2 package to analyze and plot functional visualizations.

Understanding Customer Behavior and Predicting Churn in Banking Institutions

Dashboard del trabajo final