Explore the market, one filter at a time.
Price, mileage and condition. A market explored through filters.
Streamlit · Plotly · Pandas
What changes.
Objective
Enable exploration of U.S. vehicle data through interactive visualizations, dynamic filters, and a reproducible pipeline.
Results
Interactive experience to explore the dataset without code.
How it was built.Decisions, evidence and details
Objective
Enable exploration of U.S. vehicle data through interactive visualizations, dynamic filters, and a reproducible pipeline.
Methodology
- Data cleaning: remove outliers and normalize price and odometer.
- EDA in notebooks: identify the most relevant variables.
- Streamlit app: filters and Plotly charts (histograms and scatter).
- Documentation and requirements to reproduce locally.
Key visualizations
- Price histogram with condition and transmission filters.
- Price vs. odometer scatterplot with brand/model selection.
- Summary of aggregated metrics by vehicle category.
Results
- Interactive experience to explore the dataset without code.
- Libraries and data ready to reproduce or extend the app.
- Faster analysis time thanks to instant filters and visuals.