XIN GUO

Urbana, IL 217-***-**** advi2q@r.postjobfree.com

EDUCATION

University of Illinois Urbana-Champaign Aug 2021-May 2023 Master Beihua of University Science in in Statistics China Aug 2016-Jun 2020 Bachelor of Science in Statistics

Relevant Courses: Data Structure; Database Systems; Probability Theory; Machine Learning; Data Mining; Advanced Algebra; Statistical Modeling; Time Series Analysis. WORK EXPERIENCE

58 Software Information • • • Developed automatic Used manual Participated Engineer SQL work. Technology to scoring batch in implement construction processing of online Co.data, Ltd. stores of cleaning tasks data (58.and table, com) based and illegal optimized on exporting product PyFlink; table tasks image built structure, on review. a the full data and link platform, removed DAG, Jul realized 2020-redundant which Beijing, Dec day-reduced China 2020 level data. Mercedes-Benz Sales Service Co., Ltd. Beijing, China Data Development Intern Sep 2019-Dec 2019

• • • Analyzed analysis Developed to Used visualization automatically Python reports, the data with to reasons aggregation crawl produce and Tableau formulated for and data abnormal for clean and quarterly tables. calculation the sales indicators sales strategies meetings. trend tasks of for data using sales vans of data SQL. new with with Deployed energy colleagues Numpy vehicles tasks in and sales on in Pandas. SAP the department. HANA market, Produced Cloud did RESEARCH EXPERIENCE

Feature Engineering and Ensemble Modeling for Clothing Type Prediction Apr 2022

• Completed K-Means clustering and determined optimal value of k based on Elbow method, realized multi-class classification with support vector machine using one-vs-one approach;

• Constructed voting ensemble model by summing the weighted probability matrices made by different classification models we have fitted; the accuracy of ensemble model we got was up to 0.89. Analysis of Digital Learning During the COVID-19 Pandemic Sep 2021

• Used Python for data preprocessing, applied sequential pattern mining among different education technology products with PrefixSpan;

• Applied a time series decomposition of engagement data to see changes in student engagement over time and across regions, hence providing suggestions for better online learning. Diamond Price Prediction using Different Regression Models Feb 2022

• Handled missing values with Numpy and Pandas, did exploratory data analysis with Matplotlib;

• Compared different machine learning models such as K-Nearest Neighbor, Ridge Regression and Lasso Regression, got the mean squared error as small as 0.02 with Gradient Boosting. SKILLS

• Python, SQL, R, SAS

• PyFlink, Sklearn, Flask

• Linux, Docker, Git, Tableau