Data Analytics
Team ExperienceMedDiagnose AI
A healthcare analytics and machine learning project identifying patterns in clinical datasets with an XGBoost classification pipeline.
Client
Personal Project
Industry
Healthcare / Clinical Analytics
Services
Data Analytics, AI/ML Development
Status
Completed
The Challenge
Clinical datasets have many interacting variables; understanding which factors relate to outcomes and which drive model predictions required both statistical analysis and interpretable modeling.
The Solution
Ajay performed EDA and statistical analysis with Pandas, visualized feature distributions and correlations with Matplotlib, and built an XGBoost classification pipeline with feature importance analysis to interpret prediction drivers.
Team Contribution
- Data cleaning and exploratory data analysis (Python, Pandas)
- Feature distribution and Pearson correlation heatmap visualizations (Matplotlib)
- Data preprocessing and train-test split design
- XGBoost classification pipeline and model evaluation
- Feature importance analysis for model interpretation
Key Features
- Clinical feature distribution and correlation analysis
- XGBoost-based classification pipeline
- Feature importance interpretation
- Descriptive statistical analysis of clinical variables
Technology
Results
MedDiagnose AI surfaced the clinical variables most predictive of outcomes through feature importance analysis on an XGBoost model.