“Behind the Sale: Unveiling Patterns in Nashville’s Property Market”

A Full-Cycle Data Project: From SQL Cleaning to Python Visualization This project presents an end-to-end analysis of Nashville housing data. The workflow begins with data cleaning and transformation using MySQL — addressing null values, formatting inconsistencies, and structural issues in the raw dataset. Once the dataset is refined, we move into Python for exploratory data analysis and custom visualizations that reveal trends in property value, land use, and sales activity. The final outputs are not only insight-driven, but also crafted with storytelling in mind — making the project both technically sound and portfolio-ready.

Nashville Housing Data Cleaning (MySQL)

This project focuses on preparing raw real estate data from the Nashville housing market using structured SQL techniques. The workflow addresses missing values, inconsistent formatting, and redundant fields. Key transformations include splitting composite address fields, normalizing date formats, handling NULLs, and removing duplicates — ensuring the dataset is analysis-ready. This cleaned dataset serves as a reliable foundation for downstream visualizations and insights.

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Nashville Housing Storyline

A data storytelling project that visualizes market trends from the Nashville housing dataset using Python. Building on the cleaned data from MySQL, this project explores how factors like property age, land use, and building-to-land ratios influence sale prices. Through custom visualizations created with Matplotlib and Seaborn, the project uncovers pricing trends, transactional patterns, and property value dynamics — packaged in a polished, portfolio-ready format.

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Global HIV Trends (2000–2018)

This project explores the global trajectory of HIV prevalence and treatment coverage across countries and regions between 2000 and 2018. Using cleaned and structured data, it highlights disparities in infection rates, access to antiretroviral therapy, and regional progress toward epidemic control. The analysis is supported by clear visualizations that reveal both global patterns and country-specific insights — offering a data-driven lens on one of the world’s most pressing public health challenges.

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Coffee Sales Intelligence: Dual Dashboard Edition.

This project explores coffee sales performance through two complementary lenses. First, Excel was used to clean and visualize the data — uncovering trends in revenue, product categories, and customer segments. Then, Tableau was used to reimagine the dataset from a fresh perspective, offering interactive dashboards that highlight regional performance, seasonal patterns, and the rising popularity of Costa Rica’s blend. Together, these tools showcase a full-cycle BI workflow from spreadsheet to storytelling.

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