Documentation

• 📊 Project Analysis - Detailed project structure and completion status
• 🐘 PostgreSQL Setup - Database configuration guide
• 🤝 Contributing Guidelines - How to contribute to this project
• 📋 Changelog - Project version history and changes
• 📁 Module READMEs - Each directory contains specific instructions

Course Modules

Module 1: Python Fundamentals & Data Analysis

• Week 2 (M1S2) - Basic Python scripts
• Week 3 (M1S3) - Data structures
• Week 4 (M1S4) - File I/O and modularization
• Week 5 (M1S5) - Pandas & NumPy analysis
• Week 6 (M1S6) - Healthcare data insights project

Module 1: Database & SQL

• Week 7 (M1S7) - ER modeling
• Week 8 (M1S8) - DDL, DML, normalization
• Week 9 (M1S9) - Advanced queries

Module 2: Advanced Analysis

• Week 2 (M2S2) - Advanced data cleaning and statistical analysis with real datasets
• Week 5 (M2S5) - Data pipeline and ETL with local Data Warehouse
• Week 8 (M2S08) - Interactive sales dashboard with Streamlit
• Week 9 (M2S09) - Interactive sales dashboard with Streamlit

Module 3: Machine Learning & RPA

• Week 1 (M3S01) - Linear regression: pharmaceutical demand forecasting
• Week 2 (M3S02) - ML preprocessing: features and target variable split
• Week 3 (M3S03) - DecisionTreeRegressor and cross-validation
• Week 4 (M3S04) - RPA with PyAutoGUI: mouse tracking, keyboard automation, Gupy scraping, Gemini analysis, automated e-mail
• Week 5 (M3S05) - RPA with Selenium: CMED spreadsheet, web scraping, file renaming, complete RPA flow
• Week 7 (M3S07) - Mini-project: predictive analysis of medical insurance costs (EDA, feature engineering, 5 models, cross-validation, GridSearchCV)

Common Issues & Solutions

Python Environment

# Common dependency conflicts
pip install --upgrade pip
pip install -r requirements.txt --force-reinstall

Jupyter Notebooks

# Cannot find data files
# Ensure you run jupyter from project root
cd /path/to/data-analysis-devinhouse
jupyter lab

PostgreSQL Connection

• Refer to POSTGRESQL_SETUP.md for authentication issues

Contributing

1. Fork this repository
2. Create a feature branch (git checkout -b feature/new-analysis)
3. Commit your changes (git commit -am 'Add new analysis')
4. Push to the branch (git push origin feature/new-analysis)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• 🎓 DEVinHouse 2025 for the comprehensive data analysis curriculum
• 📚 Course instructors and materials creators
• 🤝 Fellow students for collaboration and knowledge sharing