Employee Burnout Analysis & Prediction (VOIS Project)

📌 Overview

This project explores employee burnout issues in product- and service-based companies.
It uses exploratory data analysis (EDA), feature engineering, and machine learning techniques to analyze patterns and predict burnout levels among employees.

The goal is to help organizations — from startups to even global health bodies like WHO — understand and proactively address employee burnout.

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🎯 Problem Statement

Employee burnout is a critical issue impacting productivity, mental health, and retention rates.
The objective of this project is to:

1. Analyze employee data to identify burnout patterns.
2. Predict burnout scores using machine learning.
3. Provide actionable insights to organizations.

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📂 Dataset

• File: employee_burnout_analysis-AI.csv
• Columns:
  • Employee ID
  • Date of Joining
  • Gender
  • Company Type
  • WFH Setup Available
  • Designation
  • Resource Allocation
  • Mental Fatigue Score
  • Burn Rate (target variable)
• Size: 22,750 records
• Source: Provided as part of a VOIS project/training assignment.

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🛠 Technologies & Libraries Used

• Python: Data analysis & modeling
• Pandas: Data manipulation
• NumPy: Numerical computations
• Matplotlib / Seaborn / Plotly: Data visualization
• Scikit-learn:
  • Preprocessing (LabelEncoder, SimpleImputer)
  • Machine Learning Models (AdaBoostRegressor, RandomForestRegressor)
  • Dimensionality Reduction (PCA)

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📊 Steps in the Project

1. Data Loading & Cleaning

   • Removed duplicates
   • Filled missing values (mean imputation)
   • Removed irrelevant columns (Employee ID)

2. Exploratory Data Analysis (EDA)

   • Count plots for categorical features
   • Histograms for numerical features
   • Correlation heatmap

3. Feature Engineering

   • Label encoding for categorical columns
   • Normalization of feature names

4. Dimensionality Reduction

   • Applied PCA to reduce dimensionality while retaining 95% variance

5. Predictive Modeling

   • Implemented AdaBoost Regressor & RandomForest Regressor for burnout prediction

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📈 Key Insights

• Mental Fatigue Score and Resource Allocation show strong correlation with burnout.
• Burnout levels vary across company types and WFH setup availability.
• PCA reduced the feature space while preserving most information.

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🚀 How to Run the Project

### 1. Clone this repository
git clone https://github.com/<your-username>/employee-burnout-analysis.git
cd employee-burnout-analysis
2. Install dependencies
pip install -r requirements.txt

3. Run the notebook
jupyter notebook ProjectVOIS.ipynb


Or execute the Python script:
python employee_burnout_analysis.py

📌 Future Improvements

Hyperparameter tuning for better model performance.

Deploy as a web dashboard using Streamlit/Flask.

Incorporate additional features such as work hours, leave records, etc.

📜 License

This project is for educational purposes. Dataset provided as part of VOIS training/assignment — not for commercial use.