This project was conducted during my internship at HubbleMind to estimate obesity levels based on eating habits and physical conditions. The goal was to leverage data science techniques to uncover meaningful insights and provide actionable solutions for promoting healthier lifestyles.
The project focused on analyzing relationships between eating habits, physical activity, and overall physical health to predict obesity levels.
✅ In-depth Analysis: Investigated correlations between eating habits, physical activity, and physical health metrics.
✅ Model Development: Built and fine-tuned machine learning models using key features to improve predictive accuracy.
✅ Deployment: Created an interactive web application using Streamlit for seamless user interaction.
You can explore the project through the deployed web application:
🌐 Obesity Estimation App
Comprehensive documentation detailing the project workflow, analysis, and insights is available at:
📘 Project Documentation
The insights derived from this project can assist in:
- Encouraging healthier lifestyle choices.
- Supporting data-driven decision-making in the healthcare sector.
I would like to express my heartfelt gratitude to my mentor, Krishna Kumar, for his guidance and support throughout this project. His expertise and insights were invaluable in shaping the success of this initiative.
Thank you to HubbleMind for the opportunity to work on this impactful project, which enhanced my technical expertise and strengthened my ability to deliver meaningful, data-driven solutions.
- Python: For data analysis and modeling.
- Machine Learning: For building predictive models.
- Streamlit: For deploying the interactive web application.
#DataScience #MachineLearning #HealthTech #ObesityEstimation