Employee Attrition Prediction Using Logistic Regression

📌 Project Overview

This project focuses on HR Analytics and implements a Logistic Regression classification model to predict employee attrition. The goal is to help organizations understand workforce trends and identify employees who are at risk of leaving.

---

🎯 Problem Statement

Employee attrition leads to increased hiring costs and productivity loss.
Using historical HR data, this project aims to:

• Predict whether an employee will leave the organization
• Identify important factors influencing attrition
• Evaluate model performance using classification metrics

---

📂 Dataset Details

• File Name: HR_file.csv
• Target Variable: Attrition (Binary: Yes / No)

The dataset includes employee attributes related to:

• Personal demographics
• Job role and department
• Compensation and performance
• Work-life balance indicators

---

🛠️ Tech Stack

• Language: Python
• Libraries Used:
  • pandas
  • numpy
  • matplotlib / seaborn
  • scikit-learn
• Environment: Jupyter Notebook

---

🔍 Methodology

1. Data Preprocessing

• Handled missing values
• Encoded categorical variables
• Scaled numerical features

2. Exploratory Data Analysis

• Analyzed attrition distribution
• Identified key trends influencing employee turnover

3. Model Building

• Implemented Logistic Regression
• Split data into training and testing sets
• Trained model on training data

4. Model Evaluation

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

---

📊 Key Results & Insights

• Model achieved approximately 98% accuracy
• High precision and recall for both classes
• Minimal false predictions
• Logistic Regression proved effective and interpretable

---

📈 Final Conclusion

The Logistic Regression model successfully predicts employee attrition with high accuracy and balanced classification performance. The model can support HR teams in proactive decision-making and employee retention planning.

---

🚀 How to Run the Project

1. Clone the repository
2. Open the notebook in Jupyter Notebook
3. Install required libraries
4. Run all cells sequentially

---