In previous posts, I tried to predict if a bank customer is likely to leave, OR if an app user is likely to churn or subscribe. Now I will share recent work in the human resource domain to bring some predictive power to any firm struggling to retain their employees.

In this first post, I will focus on exploring datasets for any interesting patterns. As always, it is split into below:

1. Problem Statement

2. Data Review

3. Distribution Analysis

4. Independent Variable Correlation Analysis

5. Response Variable Correlation Analysis

6. Takeaways

Let’s begin the journey 🏃‍♂️🏃‍♀️.

1. Problem Statement

If you are from the Human Resources department, you may agree that hiring and retaining employees are complex tasks that require capital, time, and skills.

According to toggle hire, companies spend 15%-20% of the employee’s salary to recruit a new candidate. Hiring an employee in a company in a company with 0–500 people costs an average of $7,645 🙀🙀.

As a data scientist, you are tasked to develop a model that could predict which employees are more likely to quit. Your findings will support more effective and efficient employee retainment.

2. Data Review

The dataset used here is IBM HR Analytics dataset from Kaggle. There are 1470 records with 35 features. Check the video below to have a real touch on the raw dataset.

#exploratory-data-analysis #correlation #data-visualization #data-science #binary-classification