Yingqiang Yuan

Master's

Computer Science

The Snow Agency

Data Analyst Intern

- Utilized Python libraries such as Pandas, NumPy, and Scikit-learn to develop and implement machine learning algorithms, focusing on - Leveraged Python tools, including Pandas for data manipulation and Matplotlib/Seaborn for data visualization, to identify trends and patterns in customer behavior across multiple datasets. Over a three-month analysis phase, this effort led to a 10% increase in the accuracy of sales predictions, providing the marketing team with more reliable insights for campaign planning - Conducted an evaluation of various machine learning algorithms, including Decision Trees, Random Forest, and Support Vector - Machines, over a four-month testing period. Employed cross-validation techniques to determine the most effective model for predicting - Collaborated the team of three to share insights and develop data-driven strategies, utilizing Python packages such as Pandas, NumPy, and Scikit-learn. This approach contributed to a 20% increase in campaign efficiency by optimizing data preprocessing, feature selection - Enhanced the ability to predict trending products on TikTok Shop by implementing advanced machine learning algorithms, including - Random Forest and Gradient Boosting, within the Scikit-learn framework. This resulted in a 15% increase in sales conversions by - Successfully deployed custom-built analytics tools on the platform, using packages like Flask for the web interface and SQLAlchemy for database management. Boosted productivity for marketing teams by 35%, streamlining data access and accelerating campaign