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I’m currently building my personal portfolio website using HTML, with ongoing updates and improvements each day. The site is designed to showcase my projects, skills, and experiences in data analytics, IT, and web development. It serves as a central hub where visitors can learn more about me, view my resume, explore my work, and get in touch. While still a work in progress, I’m focusing on clean design, responsive layout, and easy navigation to create a professional and user-friendly experience. As I continue to grow my skills, I’m integrating more advanced features like interactive charts and project demos. This evolving project reflects my commitment to continuous learning and serves as a live canvas for applying what I’ve learned in real time.
I used Power BI to transform raw datasets into interactive dashboards and visual reports. These projects focus on uncovering insights through data modeling, DAX measures, and dynamic visuals—helping stakeholders make informed, data-driven decisions with ease.
I created interactive dashboards on Tableau Public to visualize trends in real-world datasets. Each project highlights my skills in data storytelling, filtering, and design. From financial insights to health data, my visualizations make complex information clear and engaging for any audience.
I built an Amazon web scraper using Python to extract product details like titles, prices, and availability in real time. It uses requests and BeautifulSoup to parse HTML and collect data, with optional automation using schedule or time. This project helps track price changes efficiently for analysis or alerts.
I conducted Exploratory Data Analysis using Pandas, NumPy, Seaborn, and Matplotlib to explore and visualize data. I used statistical summaries, correlation heatmaps, and distribution plots to uncover patterns, detect outliers, and understand relationships between variables—laying the groundwork for deeper analysis and modeling.
I cleaned and prepared a messy layoff dataset using SQL by removing duplicates, standardizing formats, handling missing values, and converting dates. The result is a clean, structured table ready for analysis and visualization.
I explored a cleaned layoff dataset using SQL to uncover trends and insights. I analyzed layoffs by company, country, year, and funding stage, calculated averages and rolling totals, and ranked top companies by impact. This helped visualize patterns in workforce reductions across time and industries.