To explore the complete project with interactive visualizations, please visit: Statistical-Analysis-Attrition-Classification. Our primary goal is to address the challenge of employee attrition, seeking to predict and understand the reasons behind employees leaving their jobs. Employee attrition is a significant concern for companies, often resulting in increased costs and workplace disruptions. Our project is dedicated to predicting attrition and gaining insights into the underlying factors driving it.
We've incorporated an in-depth exploratory data analysis (EDA) phase into our project. This step involves scrutinizing the dataset to uncover patterns, relationships, and factors that influence employee attrition.
Our project's core focus is the application of various classification techniques. These include Logistic Regression, Naive Bayes, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and k-Nearest Neighbors (KNN). Through these methods, we aim to effectively predict attrition.