Summary#

Data scientist with experience in the complete analytics pipeline: EDA, feature engineering, model training, and result interpretation. Applied data science to healthcare analytics, resulting in peer-reviewed IEEE publications.

How I Apply This Skill#

• Conducted comprehensive EDA on beer/brewery datasets identifying correlations (0.67 IBU-ABV) and data quality issues (41.7% missing IBU values)
• Performed feature engineering on Titanic data: binning ages, creating fare bands using quartiles, encoding categoricals
• Trained and evaluated models with proper train/test/validation splits and cross-validation
• Created visualizations with Matplotlib and seaborn for statistical insights and result presentation
• Published healthcare analytics research in 3 IEEE conferences

Key Strengths#

• EDA Workflow: Missing value analysis, statistical examination, plotting distributions, correlation matrices
• Feature Engineering: Categorical encoding, binning, derived features, filling null values
• Healthcare Analytics: Long COVID-19 prediction, demographic analysis, symptom clustering
• Visualization: Matplotlib, Seaborn, distribution plots, heatmaps, model performance curves
• Statistical Analysis: Correlation, quantile statistics, descriptive statistics

Related Projects#

• Long COVID Prediction Research (IEEE Published)
• CIFAR-10 Image Classification
• Exploratory Data Analysis on Beers & Breweries Datasets
• Titanic Survival Prediction