Exploratory Data Analysis (EDA) is a crucial step in the data analysis process, involving a variety of techniques to maximize insight into a data set, uncover underlying structure, extract important variables, detect outliers and anomalies, test underlying assumptions, and develop parsimonious models.
- The project mainly focusses on how a simple Decision Tree Classifier could be utilized in concluding employee's attrition rates.
- A detailed Exploratory Data Analysis has been performed using the Pandas library and visualization tools such as matplotlib and Seaborn.
- The major concerns of Employee Attrition has been addressed.
- Feature engineering being a key factor that must be performed before testing the models has been tested with various features of the dataset.
Here we observe that there is a clear trend of employee who travels very frequently tend to have higher attrition compared to an employee who doesn't travel.
