A collection of Python projects from Ho Chi Minh City University of Banking, exploring financial markets, people, retail performance, and consumer behavior.
Table of Contents
HUB Data Analysis is a collection of independent student projects built around one shared goal: turning real-world data into clear, useful conclusions.
The notebooks move through the full analytical process—from collecting and cleaning data to exploring patterns, building models, evaluating results, and communicating insights. Together, they demonstrate how the same data science foundations can be applied across finance, workforce planning, international retail, and behavioral research.
| Domain | What the collection explores |
|---|---|
| Financial Markets | Stock prices, trading volume, market behavior, technical trends, clustering, and price estimation |
| Human Resources | Employee profiles, compensation, departments, salary prediction, and workforce segmentation |
| Global Retail | Sales, profit, customers, product categories, regional performance, and business opportunities |
| Consumer Research | Service quality, satisfaction, attitude, behavioral intention, and survey response patterns |
The work combines exploratory analysis with regression, classification, clustering, dimensionality reduction, and business-focused visualization.
| Purpose | Tools and methods |
|---|---|
| Development | Python 3.10+, Jupyter Notebook |
| Data Processing | pandas, NumPy |
| Visualization | Matplotlib, Seaborn |
| Machine Learning | scikit-learn |
| Modeling | Linear, Ridge, and Logistic Regression; K-Means; PCA; GridSearchCV |
| Data Sources | vnstock, Global Superstore, employee records, and survey responses |
- Python 3.10 or newer
- pip
- Jupyter Notebook
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Clone the repository.
git clone https://github.com/Vuog23/hub-data-analysis.git cd hub-data-analysis -
Install the dependencies.
pip install pandas numpy matplotlib seaborn scikit-learn jupyter vnstock
-
Launch Jupyter Notebook.
jupyter notebook
Choose a notebook and run its cells from top to bottom. Each notebook contains its own preparation, analysis, visualizations, models, and conclusions.
Local datasets are included where required. Stock market analyses may need an internet connection to retrieve data through vnstock.