Machine Learning Data Science

Tin Nguyen

***.***.**** — # **********@*****.*** — ï https://www.linkedin.com/in/tin-nguyen-04a86a278/ — § https://github.com/t-i-n2003 Summary — Data Science student skilled in Machine Learning and Natural Language Process. Developed LSTM (Long-Short Term Memory) models for stock prediction and Natural Language Process systems for financial news analysis. Proficient in Python, Pandas, and model deployment. Seeking data scientist roles to apply analytical skills. Skills

Machine Learning : LSTM, Regression, Classification, Scikit-learn, Time Series

Natural Language Process : Sentiment Analysis, NLTK, Transformers, BERT

Tools : Python (Pandas/NumPy), Docker,

Streamlit, SQL, Git

Analytics : Data Visualization, Statistical

Modeling, Power BI

Experience

Tinh Hoa Solutions Oct 2024 – Feb 2025

Data Engineering Intern

– Optimized 15+ SQL procedures improving query speed by 40%

– Developed automated ETL pipelines serving 20+ monthly reports

– Implemented data validation checks reducing errors by 30% Education

Nha Trang University

Bachelor of Information Technology

Minors: Information System

GPA: 3.22/4.00

Projects

Stock Price Prediction Using LSTM [December 2024] – [June 2025]

– Built a deep learning system using LSTM to predict next-day stock closing prices based on historical data.

– Collected and cleaned stock data using ‘vnstock‘, engineered features, and optimized model with RandomizedSearchCV.

– Deployed interactive web app using Streamlit Cloud for real-time prediction and visualization.

– Tools: Python (TensorFlow, scikit-learn, Pandas), Streamlit, Matplotlib, Plotly.

– GitHub: https://github.com/t-i-n2003/streamlit-app-sp Customer Churn Prediction System [Mar 2025] – [June 2025]

– Built an end-to-end machine learning system to predict customer churn with up to 95% accuracy

– Developed preprocessing and feature engineering pipelines; trained models including Random Forest, Logistic Regression, and Gradient Boosting

– Integrated counterfactual analysis using DiCE for model explainability and actionable business recommendations

– Designed Power BI dashboards and generated automated retention strategy reports for decision-making

– Tools used: Python (scikit-learn, dice-ml, joblib), Power BI, Pandas, Seaborn, Git, Docker

– GitHub: github.com/t-i-n2003/churn system

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