Research Assistant and Data Scientist with experience in predictive modeling, machine learning, data preprocessing, and data visualization. Skilled in Python, SQL, R, Scikit-learn, TensorFlow, PySpark, Tableau, Power BI,…
Research Assistant and Data Scientist with experience in predictive modeling, machine learning, data preprocessing, and data visualization. Skilled in Python, SQL, R, Scikit-learn, TensorFlow, PySpark, Tableau, Power BI, AWS, Azure, and ETL tools, with experience applying these tools to healthcare and financial analytics projects.
• Led a healthcare research project, developing predictive models for proactive interventions, resulting in a 20% increase in patient outcome prediction accuracy.
• Gathered and meticulously preprocessed diverse healthcare datasets, including 50,000+ Electronic Health Records (EHR) and patient demographics, utilizing Python libraries such as pandas and Scikit-Learn.
• Implemented advanced machine learning algorithms, including logistic regression, decision trees, and ensemble methods (random forests, gradient boosting), using scikit-learn and TensorFlow, achieving a 95% accuracy rate in identifying individuals at risk.
• Created insightful visualizations using Tableau, translating complex data into actionable insights for stakeholders and facilitating data-driven decision-making processes.
Led a healthcare research project, developing predictive models for proactive interventions, resulting in a 20% increase in patient outcome prediction accuracy.
Gathered and meticulously preprocessed diverse healthcare datasets, including 50,000+ Electronic Health Records (EHR) and patient demographics, utilizing Python libraries such as pandas and Scikit-Learn.
Implemented advanced machine learning algorithms, including logistic regression, decision trees, and ensemble methods (random forests, gradient boosting), using scikit-learn and TensorFlow, achieving a 95% accuracy rate in identifying individuals at risk.
Created insightful visualizations using Tableau, translating complex data into actionable insights for stakeholders and facilitating data-driven decision-making processes.
Spearheaded a financial analytics project aimed at predicting credit risk and optimizing lending strategies, resulting in a 25% reduction in default rates.
Acquired and meticulously preprocessed diverse financial datasets, including transaction records and credit histories, using Python libraries such as pandas and NumPy.
Implemented cutting-edge machine learning algorithms, including logistic regression, random forests, and XGBoost, leveraging scikit-learn and TensorFlow, to assess creditworthiness and predict loan defaults with 90% accuracy.
Utilized AWS Cloud services, including AWS Glue and S3, to automate data pipelines and optimize data processing workflows, reducing data processing times by 30% and enhancing scalability.
Developed insightful visualizations using Power BI, presenting key financial metrics and risk indicators to stakeholders, facilitating informed decision-making and risk management strategies.
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