Projects

The tire crack project aims to develop a machine learning model that can accurately classify tire images as either "cracked" or "not cracked and identify where the cracks are by inputting an image by using TensorFlow.  Click on "image" to view the project.

Exploring COVID-19 data with SQL involves querying databases for insights. Analyzing cases, deaths, and recoveries by region, date, and demographics can reveal trends. Joining tables enables comprehensive examinations, aiding public health strategies and policy decisions.Click on "image" to view the project.

This Nashville Housing Data project involves refining property addresses by breaking them into distinct columns (Address, City, State). Standardizing the "Sold as Vacant" field to 'Yes' and 'No,' removing duplicates, and deleting unused columns optimize data quality and structure, facilitating more insightful analyses of Nashville housing trends. Click on "image" to view the project.

The Facial Recognition Attendance System is a Python-based project that leverages facial recognition technology to automate the attendance tracking process. This project uses OpenCV, NumPy, and face-recognition libraries to create a secure and efficient authentication system..  Click on "image" to view the project.

Conducted comprehensive COVID-19 data analysis using Tableau, leveraging SQL queries to extract information from the Our World in Data website. Developed insightful visualizations, including the percentage of the population infected by continent and total deaths per continent. Communicated findings effectively for data-driven decision-making, showcasing analytical skills and proficiency in data visualization tools. Click on "image" to view the project.