Akisa Vujel

Exploratory analysis of insurance datasets to uncover risk patterns and policy-level insights across demographic groups.

• Cleaned and preprocessed 1,000+ rows of insurance claim data
• Identified key risk factors correlating with high claim costs via correlation analysis
• Visualized the impact of age, BMI, and smoking habits on insurance premiums

Complete end-to-end analysis of over 10,000 Himalayan expeditions from 1905–2019 — from data cleaning, EDA, SQL querying to predictive modeling and an interactive Tableau dashboard.

• Cleaned and prepared raw Himalayan expedition data using Python and Pandas
• Exploratory Data Analysis with Matplotlib & Seaborn: seasonal trends, peak popularity, team sizes
• Performed SQL analysis for top peaks, most successful/unsuccessful expeditions, and seasonal risk
• Built Tableau dashboard with KPIs, bar charts, and maps to visualize expedition success patterns
• Trained Logistic Regression model to predict expedition success based on season, peak, oxygen use, and team composition

End-to-end analysis of global life expectancy data — from web scraping and SQL queries to an interactive Tableau dashboard covering country and gender trends.

• Scraped life expectancy data from World Population Review using requests + BeautifulSoup
• Performed SQL analysis: averages, highest/lowest countries, gender gap, and threshold filters (>80 / <60 years)
• Built a Tableau dashboard with 5 KPIs, 4 bar charts, and a global choropleth map
• Identified significant gender differences in life expectancy across 150+ countries

Statistical A/B test comparing click-through rates of two YouTube thumbnail designs to determine which drives higher engagement.

• Conducted two-sample proportion z-test to validate statistical significance
• Calculated p-values and confidence intervals for experiment results
• Delivered a clear data-backed recommendation based on CTR improvement