An interactive analysis of British Airways' passenger reviews, exploring trends in cabin staff service, in-flight entertainment, food, ground service, seat comfort, and value from March 2016 to October 2023.
This project analyzes sales data from 2023 to uncover key business insights, including regional revenue trends, top-performing sales representatives, product profitability, and the impact of discounts on sales performance. By leveraging Pandas, NumPy, Matplotlib, and Seaborn, I explored how different factors—such as pricing strategies, customer segmentation, and sales channels—affect overall revenue and profitability. Findings from this analysis help businesses optimize pricing, improve revenue forecasting, and enhance customer targeting, demonstrating my ability to apply data-driven decision-making in a commercial context.
This project explores SQL query optimization using Lahman’s Baseball Database, focusing on performance improvements through indexing, execution plan analysis, and query restructuring. By leveraging EXPLAIN ANALYZE, implementing B-tree and Hash Indexes, and comparing JOINs, subqueries, and CTEs, I optimized query efficiency while balancing tradeoffs in speed, memory usage, and indexing overhead.
This project applies exploratory data analysis (EDA) and data visualization to examine unemployment rate trends using real-world economic data from FRED (Federal Reserve Economic Data). By leveraging Python, Pandas, and Matplotlib, I analyzed labor market fluctuations, identified economic downturns, and evaluated the impact of external factors on employment trends. I demonstrated expertise in time-series analysis, trend forecasting, and statistical interpretation, showcasing my ability to extract meaningful conclusions from complex economic datasets. This project highlights my proficiency in data analytics, economic research, and visualization techniques, essential for informed decision-making, business strategy, and policy evaluation.
This project focuses on entity resolution, a critical task in data engineering and business analytics, to accurately match and standardize company names across datasets. Using fuzzy matching techniques, Levenshtein distance, and string preprocessing, I developed an efficient algorithm to resolve discrepancies in firm names due to misspellings, abbreviations, and formatting inconsistencies. By applying data cleaning, standardization, and advanced matching algorithms, this project enhances data integrity, improves record linkage, and streamlines business intelligence processes, demonstrating my expertise in data engineering, NLP-based matching, and automation for business analytics.
leverages Alpaca's API to execute data-driven swing trades. Using Python, financial indicators, and real-time market data, it identifies optimal entry/exit points, automates trades, and manages risk. This system enhances trading efficiency by minimizing emotional bias and optimizing strategy for improved profitability