Explore a collection of projects showcasing my expertise in data analysis, web scraping, automation, and SQL database management. Each project demonstrates my ability to extract, clean, analyze, and automate data-driven solutions.
Amazon Sales Report
This project highlights my data analysis skills using Python, Pandas, and visualization tools. I analyzed 128,000+ orders, uncovering spending trends, order status patterns, and B2B vs. B2C insights to drive business decisions.
Country Vaccinations
Analyzed global vaccination trends using Python, Pandas, Matplotlib, Seaborn, and Squarify on 86,512 records. Key insights include vaccination growth over time, country-wise distribution, and completion rates. Identified trends in supply chain efficiency and policy impacts, showcasing my skills in data cleaning, analysis, and visualization.
Optimizing E-commerce Engagement (SQL)
This project analyzes an e-commerce customer dataset that I transformed into a relational database. The goal: identify high-value customers, uncover satisfaction drivers, and support smarter business decisions. Each query below includes a short comment explaining its business value covering trends in spending, satisfaction, membership tiers, and location-based behavior.
Automated Laptop Details Extractor
This project automate the extraction of laptop details from eBay using Selenium. The program searches for laptops, collecting important information from each product listing, including the product title, price, quality (new or pre-owned), and a direct link to the item. The collected data is saved into a CSV file for easy analysis.
Job Scraper
This project is a LinkedIn job scraper that automates the job search process based on user-defined criteria. Users can input specific job titles, skills, or locations, and the scraper searches LinkedIn for relevant job listings. It then filters through the results, identifying jobs that match the user's preferences. The project showcases my ability to work with web scraping techniques and automate the extraction of relevant data from LinkedIn, providing an efficient tool for job seekers.
HR Analytics Database Management and Preparation
I transformed a raw HR analytics CSV dataset into a fully organized relational database using PostgreSQL. The project involved thorough data cleaning, including the removal of duplicate rows and validation of data integrity, followed by structuring the dataset into multiple normalized tables to optimize storage and querying efficiency. I also prepared and transformed key tables and columns specifically for advanced data analysis in Python. This project highlights my ability to manage real-world data workflows, from initial ingestion and cleaning to database normalization, relational modeling, and final data export, showcasing strong skills in SQL, database design, and data preparation for analytical environments.