Predicting Employees under Stress

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  Predicting Employees Under Stress

Inspiration

Stress in the workplace can lead to decreased productivity, poor mental health, and employee burnout. I wanted to build a solution that uses data to identify stressed employees early, so companies can take preventive actions.

What it does

This project analyzes employee data like working hours, job role, workload, and satisfaction levels to predict if an employee is under stress using machine learning models.

How we built it

I collected and cleaned employee data, performed feature selection, and used Python with libraries like pandas, scikit-learn, and matplotlib. I trained models like Logistic Regression and Random Forest to classify employees as stressed or not stressed.

Challenges we ran into

Handling missing or imbalanced data Choosing the right features for prediction Improving model accuracy Making the output easy to understand for non-technical users

Accomplishments that we're proud of

Achieved good prediction accuracy using machine learning

What we learned

Data preprocessing techniques like normalization and encoding Machine learning model training and evaluation Importance of data visualization and feature selection How to interpret model results for decision-making

What's next for Predicting Employees under Stress

Add a user-friendly dashboard with Power BI or Streamlit Use more advanced models like XGBoost or neural networks

Built With

• css
• html
• javascript
• mysql
• python