This project utilizes machine learning, specifically an XGBoost model, to address the critical issue of mental health among tech workers. By analyzing various factors related to work-life balance, stress levels, and personal well-being, this model aims to proactively identify individuals who might benefit from seeking psychological support.
- Data-Driven Approach: Utilizes a dataset collected through surveys and questionnaires to train and evaluate the XGBoost model.
- Predictive Modeling: Leverages the power of XGBoost to predict the likelihood of a tech worker needing to consult a psychologist.
- Early Intervention: Facilitates the early identification of individuals at risk, enabling timely interventions and support.
- Promote Mental Well-being: Contributes to a healthier and more supportive work environment for tech professionals.
- Reduce Stigma: Encourages open conversations about mental health and seeking help when needed.
- Increase Productivity: Fosters a workforce that is mentally and emotionally resilient, leading to improved productivity and engagement.
This project is intended for research and educational purposes. It is not a substitute for professional medical advice or diagnosis. Always seek the guidance of a qualified healthcare professional for any concerns about your mental health.
- Contribute: Provide feedback, suggest improvements, or share relevant data.
- Raise Awareness: Help promote discussions about mental health in the tech industry.
Together, let's create a more supportive and understanding environment for tech workers!