# Retail Data Analysis and Student Machine Learning Practice: Technical Exploration of the SCY1101 Course Project

> This article introduces a student course project that conducts machine learning analysis on retail datasets, covering the complete workflow of data exploration, feature engineering, model training, and evaluation, demonstrating the value and challenges of practical machine learning teaching in higher education.

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- 发布时间: 2026-05-18T20:15:51.000Z
- 最近活动: 2026-05-18T20:31:02.544Z
- 热度: 150.8
- 关键词: 零售数据, 机器学习, 学生项目, 数据科学教育, 特征工程, 模型训练, Python, 课程实践
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## [Introduction] Exploration of Retail Data Analysis and Student Machine Learning Practice Project

This article introduces the SCY1101 course project at Duoc UC University in Chile, which conducts end-to-end machine learning analysis on retail datasets, covering the complete workflow of data exploration, feature engineering, model training, and evaluation. It demonstrates the value and challenges of practical machine learning teaching in higher education and cultivates students' data science capabilities.

## Project Background and Positioning

SCY1101 is a data science-related course at Duoc UC University. The final project requires student teams to complete a full machine learning analysis workflow, choosing retail datasets (classic, practical, and data-rich) as the subject. Project objectives include: technical capability cultivation (mastering the full workflow), team collaboration (simulating real work modes), problem-solving (handling data noise, etc.), and outcome presentation (reproducible code and reports).

## Machine Learning Workflow and Technical Implementation

**Workflow**: 1. Data exploration and understanding (loading check, descriptive statistics, visualization); 2. Preprocessing (missing value/outlier handling, type conversion); 3. Feature engineering (date extraction, derived indicators, lag/rolling statistics); 4. Model selection and training (regression/classification/clustering models); 5. Evaluation and optimization (indicator selection, cross-validation, hyperparameter tuning).
**Tech Stack**: Python ecosystem (Pandas/NumPy for data processing, Matplotlib/Seaborn for visualization, Scikit-learn/XGBoost for modeling, Jupyter Notebook for development); the code uses a modular structure (directories like data/notebooks/src) and Git for version control.

## Learning Outcomes and Project Challenges

**Outcomes**: Students master data processing, feature engineering, model selection, evaluation and validation, and engineering practice capabilities.
**Challenges**: Data quality issues (dirty data/missing values), difficulty in feature engineering, confusion in model parameter tuning, overfitting traps, difficulty in result interpretation, team collaboration issues (code conflicts/unclear division of labor).

## Reflection on Educational Value and Improvement Suggestions

**Practical Value**: Concept internalization (understanding algorithms through hands-on implementation), problem-driven learning (strong motivation), learning from mistakes (debugging failures are opportunities), and holistic perspective (full workflow cognition).
**Improvement Suggestions**: Dataset diversity (medical/financial fields, etc.), real business scenarios (corporate collaboration), model interpretability (emphasizing decision explanation), deployment环节 (building APIs with Flask/FastAPI), and ethical discussions (data privacy/algorithm bias).

## Project Presentation and Evaluation Dimensions

**Submission Materials**: Code repository (complete runnable code, README, requirements.txt), technical report (problem definition/data exploration/methodology/result analysis/limitations), and presentation (oral presentation + Q&A).
**Evaluation Dimensions**: Technical correctness, code quality, depth of analysis, result presentation, and team collaboration.

## Project Significance and Conclusion

The SCY1101 project represents the direction of machine learning education from theory to practice. Retail data is an ideal entry field (data-rich, intuitive problems, clear value). For students: project experience is a resume highlight and proof of ability; for educators: it is necessary to balance challenge and accessibility (datasets, goals, and time are key). Cultivating practical data science talents is the mission of the education sector, and this project is a concrete practice.