- I specialize in data analysis, visualization, and business intelligence, transforming raw data into actionable insights.
- Experienced in building data-driven dashboards and automated reports that support informed decision-making.
- Passionate about development, automation, and artificial intelligence, and always exploring new tools to make workflows smarter.
- I have a personal passion for customizing my Arch Linux setup and experimenting with BSPWM, crafting efficient and minimal system environments purely for fun. 🐧
Projects demonstrating expertise in Power BI, Python, and SQL via interactive dashboards, detailed reports, and data-driven solutions that generate meaningful business insights.
An analysis of 4,000+ IMDb movies uncovering patterns in revenue, ratings, genre performance, and contributor impact. It highlights trends in budgets, gross earnings, IMDb scores, and profitability across decades, revealing key factors behind movie success and how industry dynamics have evolved over time.
Superstore Sales Data Analysis
A deep dive into Superstore’s sales and profitability, examining how regions, customer segments, and product categories contribute to overall performance. The analysis surfaces high-value customers, top-selling items, margin-driving categories, and areas where discounts reduce profits. It also highlights year-over-year growth trends, regional strengths, and patterns that influence operational efficiency and revenue outcomes.
Book Publishing Sales Analysis
An exploratory analysis of book publishing data uncovering how pricing, ratings, genres, authors, and publishers influence overall sales performance. It highlights patterns in revenue concentration, genre behavior, pricing strategy, contributor roles, and the combined factors that drive a book’s commercial success.
An exploration of IMDb movie data uncovering how budgets, ratings, genres, popularity metrics, and geographic factors shape overall movie performance. The analysis highlights revenue patterns, genre trends, audience engagement signals, and the influence of countries and directors, revealing the key elements that contribute to both commercial success and viewer reception.
Bank Customer Conversion Analysis
A combined SQL and Python analysis of the Bank Marketing dataset, performed in PostgreSQL through DBeaver with additional validation in Python. The study examines how demographics, occupations, education levels, contact methods, and campaign behaviors influence term-deposit subscriptions. The insights reveal clear conversion patterns across customer groups and highlight the factors most strongly associated with successful marketing outcomes.