Predicting Employee Retention

Project Overview

This project focuses on analyzing employee data to identify the key factors influencing employee retention and attrition. Using exploratory data analysis and machine learning techniques, the project predicts the likelihood of employees leaving the organization and provides insights to support data-driven HR decision-making.

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Business Objectives

• Identify factors contributing to employee attrition.
• Analyze employee demographics and job-related characteristics.
• Build a predictive model for employee retention.
• Support HR teams with actionable insights to improve employee retention.

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Tools & Technologies

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

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Project Workflow

1. Data Preparation

• Cleaned and preprocessed employee data.
• Handled missing values and encoded categorical variables.
• Prepared the dataset for modeling.

2. Exploratory Data Analysis

• Employee demographics analysis
• Attrition trend analysis
• Correlation analysis
• Feature importance assessment

3. Predictive Modeling

• Data preprocessing
• Model training and evaluation
• Employee retention prediction
• Performance assessment using classification metrics

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Key Analysis

• Employee Attrition Analysis
• Department-wise Retention
• Salary & Job Satisfaction Analysis
• Experience & Tenure Analysis
• Feature Importance
• Employee Retention Prediction

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Business Insights

• Identified the primary factors influencing employee attrition.
• Evaluated employee characteristics associated with higher retention.
• Developed a predictive model to identify employees at risk of leaving.
• Generated insights to support workforce planning and retention strategies.

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Project Deliverables

This repository contains:

• Jupyter Notebook (.ipynb) – Complete data analysis and predictive modeling.
• Project Report (PDF) – Methodology, model evaluation, and business insights.
• Presentation (PPT) – Summary of findings and recommendations.

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Repository Structure

Predicting_Employee_Retention
│
├── Predicting_Employee_Retention.ipynb
├── Predicting_Employee_Retention_Report.pdf
├── Predicting_Employee_Retention.zip
└── README.md

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Skills Demonstrated

• Exploratory Data Analysis (EDA)
• Data Cleaning & Preprocessing
• Machine Learning
• Classification Models
• Feature Engineering
• Model Evaluation
• Statistical Analysis
• Python
• Scikit-learn
• Data Visualization
• Business Analysis
• Data Storytelling

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Future Enhancements

• Compare multiple machine learning algorithms.
• Perform hyperparameter tuning for improved model performance.
• Deploy the model as an interactive web application.
• Build an HR analytics dashboard using Power BI.

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Author

Kartikey Singh

Data Analyst | Power BI | Python | SQL | Excel

LinkedIn: Kartikey_Singh

GitHub: Kartikey_Singh

Portfolio : Kartikey_Singh

Complete WriteUp: Coming Soon....