- Investigated how lifestyle factors such as stress, alcohol consumption, smoking, and unhealthy eating behaviors correlate with the increasing incidence of strokes in young adults.

- Utilized Python libraries (Pandas, Matplotlib, NumPy, Seaborn and SciPy) for data cleaning, preparation, manipulation and visualization to effectively manage and analyze 5 large datasets including Stress Data, Alcohol Consumption Data, Smoking Data, Unhealthy Eating Behaviors Data and Stroke Data.

- Conducted statistical testing, Chi-square tests, and ANOVA to identify significant correlations in each dataset.

- Applied machine learning techniques including Classification, Linear Regression, K-Nearest Neighbors, Decision Trees, Logistic Regression and Random Forest Classifiers using Scikit-learn to the Stroke Data to explore how stress, alcohol consumption, smoking and unhealthy eating behaviors correlate with the increasing incidence of strokes in young adults.

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