I am Esin Bilgin
"your one-stop terminal to access unique ideas and new possibilities"
I am a data scientist who thinks in data, solves problems with purpose, and turns analysis into strategy. With a solid foundation in engineering, I approach challenges with a structured and solution-oriented mindset. Throughout my M.S. in Data Science, I’ve built and validated real-world models, extracted insights from complex datasets, and presented actionable results. I am now ready to contribute to data-driven teams by solving real business problems and delivering measurable impact through analytical thinking.
"Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced by Statistical Analysis" – In my master's thesis, I developed a predictive model for estimating lung cancer severity using clinical data. The study rigorously evaluated various machine learning techniques, including Random Forest, Logistic Regression, SVM, Decision Tree, and Naive Bayes, with performance measured through accuracy, F1-score, precision, and recall. Furthermore, to ensure a fair approach to mitigating overfitting, cross-validation and hyperparameter tuning were employed to enhance performance on both training and test sets. Additionally, comprehensive statistical analyses—such as Analysis of Variance, Chi-Square, Kruskal-Wallis, Spearman Correlation, and a Correlation Matrix—were utilized to validate the findings and reinforce the model’s robustness. For further details, please refer to the link.
As a presenter at CAIAC 2024, I showcased a pilot study that encapsulated the core themes of my thesis. This brief research highlighted the effectiveness of the Random Forest algorithm, underscoring the transformative potential of artificial intelligence in revolutionizing healthcare applications. For further details, please refer to the link.
Country Gender Demographics
Bicycle Traffic on Fremont Bridge
NYC Taxi Data
Healthcare Provider Analysis (NPI)
Decision Tree, Random Forest, SVM, kNN, Logistic Regression, Naive Bayes, Statistics, Cross-validation, Hyperparameter Tuning, Domain-based Feature Selection
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