Today marks the first real step in my Sales Forecasting for Small Business project.

Why?
Because after exploring Seoul’s open business datasets and running my own store in the past, I wanted to visualize and analyze actual sales trends across different neighborhoods and industries.

📌 What I did today

1. Loaded the raw dataset from Seoul’s commercial analysis platform
   • Format: .xlsx (2024 data)
   • Structure: Quarterly sales by district (행정동) and industry category (서비스_업종_코드_명)
2. Cleaned the dataset
   • Checked for missing values and basic structure using df.info() / df.describe()
3. Grouped sales by district + quarter
   • Created a pivot-like structure to analyze trends
4. Visualized Top 10 industries in Q4 2024
   • Used matplotlib and configured Korean font (Malgun Gothic) to avoid text issues
   • Horizontal bar plot to show industry-wise revenue dominance

💡 Takeaways

• Basic structure is ready — the data is clean and well-organized
• Certain industries clearly dominate (especially seafood, fruits/vegetables)
• Need to investigate how trends shift over time and by region

🚧 Next steps

• Add time-series plots by industry and neighborhood
• Try a geographical visualization (Seoul choropleth map?)
• Consider log transformation to address large sales variance
• Create README and translate key column names

That’s it for Day 1 — clean start, clear structure, and one solid chart! 😎
Stay tuned for more insights as this project unfolds.