John Calderon

Entry-level Data Analyst with a strong foundation in healthcare data analytics, clinical operations, and Python-based machine learning. Former cardiology transcriptionist experienced in Epic Systems, EHR data management, and HIPAA compliance. Skilled in SQL, data visualization, and statistical analysis to support evidence-based decision-making. Passionate about leveraging healthcare informatics and predictive modeling to improve patient outcomes and operational efficiency.

• Predicted likelihood of cardiovascular disease using patient demographic and clinical data.
• Cleaned and preprocessed structured health data.
• Performed exploratory data analysis (EDA) to identify key predictors and correlations.
• Trained and evaluated multiple machine learning models (Logistic Regression, Decision Tree, Random Forest).
• Selected the best-performing model using AUC-ROC and accuracy metrics.
• Presented medical insights and risk factor interpretations in layman's terms.

• Developed a machine learning model to identify customers likely to churn for a telecom provider.
• Conducted EDA to identify churn drivers, such as contract type, payment method, and service usage.
• Preprocessed data with one-hot encoding, scaling, and feature engineering.
• Trained and tuned multiple models (Logistic Regression, LightGBM, CatBoost), selecting best based on AUC-ROC.
• Delivered actionable insights to inform retention strategies and reduce churn rates.