Hafiza Teouri
Data Analyst
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This project involved creating an automated process to clean and standardize a U.S. household income dataset. Using SQL, I removed duplicate rows, fixed data issues like typos, and ensured fields like state and city names were consistent. The cleaning process was built into a reusable stored procedure, making it easy to repeat for future updates.
This project analyzed global life expectancy trends and how they relate to factors like GDP and health. Using SQL, I cleaned the data by removing duplicates and filling in missing values. The analysis highlighted patterns, such as the connection between higher GDP and longer life expectancy, and identified trends in mortality and improvements over time.
This project focuses on analyzing movie industry trends using Pandas. By cleaning and exploring data on genres, budgets, revenues, and ratings, it highlights patterns such as the most popular and profitable genres. The analysis provides insights into factors that contribute to the success of films.
This project is focused on extracting structured data from a real website using Python. I used libraries like Beautiful Soup and Requests to scrape a table of population statistics by country. The data was cleaned and organized into a structured format, ready for further analysis or visualization.
This project features a call center dashboard made in Tableau to track important metrics like customer satisfaction, call volumes, resolution rates, and agent performance. The dashboard provides a clear view of the data, helping to improve operations and customer service.
