This project focuses on predicting employee attrition and conducting in-depth analysis using machine learning and data analytics techniques. Employee turnover is a critical challenge for organizations, impacting productivity and morale. Understanding the factors contributing to churn and predicting potential departures can help companies take proactive measures to retain valuable talent.

Key Features:

Data Exploration and Cleaning: Thorough exploration and cleaning of the dataset, ensuring data integrity and reliability.

Descriptive Statistics and Visualization: Utilization of descriptive statistics and visualizations to gain insights into the distribution of data and identify patterns.

Feature Engineering: Creation of new features to enhance the model's ability to capture relevant information.

Exploratory Data Analysis (EDA): Investigation of relationships between variables to identify potential factors influencing employee churn.

Statistical Analysis: Application of statistical tests to assess the impact of categorical variables on performance scores.

Neural Network Model: Implementation of a neural network model for churn prediction, alongside traditional machine learning models.

Results:

Accuracy: The model achieved an accuracy of 1.0, indicating perfect predictions on the testing set.

Classification Report: Precision, recall, and F1-score were all 1.00 for both classes (0 and 1), suggesting excellent overall performance.

Contributions:

This project provides a comprehensive analysis of employee churn, offering actionable insights for organizations to improve employee retention strategies.

Future Work:

Future enhancements could include the deployment of the predictive model in a real-world setting and continuous monitoring to refine predictions.