Background
This mini project studies a small public dataset with the goal of practicing a complete analysis loop: data cleaning, exploratory summaries, simple statistical modeling, and communication.
Method
The workflow begins with missing value checks and variable summaries. It then moves to grouped comparisons, correlation exploration, and a small regression-style model to test whether the visible patterns remain after basic controls.
Results
The project emphasizes cautious interpretation. Instead of treating every pattern as a conclusion, the writeup separates descriptive findings from model-based evidence.
Technical Stack
- pandas for data cleaning
- Matplotlib for exploratory plots
- Jupyter for iterative analysis
- Markdown for the final project writeup
Next Steps
- Add confidence intervals to the main comparisons
- Improve chart captions and accessibility
- Publish the notebook alongside a shorter written summary