desktop_windows Streamlit
scatter_plot Plotly
terminal Python
Interactive Visualization with Streamlit
Streamlit app to explore U.S. vehicle data with Plotly charts and dynamic filters for price, odometer, and condition. Includes notebooks and a reproducible cleaning pipeline.
Stack
Streamlit
Plotly
Pandas
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Objective
Enable exploration of U.S. vehicle data through interactive visualizations, dynamic filters, and a reproducible pipeline.
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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.
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Key visualizations
- • Price histogram with condition and transmission filters.
- • Price vs. odometer scatterplot with brand/model selection.
- • Summary of aggregated metrics by vehicle category.
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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.