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--- |
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title: Business Intelligence Dashboard |
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emoji: 📊 |
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colorFrom: blue |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: 4.44.1 |
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app_file: app.py |
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pinned: false |
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--- |
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## Business Intelligence Dashboard |
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Interactive Gradio application for exploring business datasets, generating insights, and exporting filtered results. |
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### Features |
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- Upload CSV or Excel files with automated validation and previews. |
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- Comprehensive statistics: numeric and categorical summaries, missing value report, correlation matrix. |
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- Dynamic filtering by numeric ranges, categorical selections, and date ranges with live row counts. |
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- Visualizations: time series, distribution, category comparisons, scatter plots, correlation heatmap. |
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- Automated insights: top/bottom performers, trend detection, anomaly identification. |
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- Export filtered data as CSV and download charts as PNG (requires `kaleido`). |
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### Project Structure |
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``` |
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BID/ |
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├── app.py # Gradio UI wiring |
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├── data_processor.py # Data loading, cleaning, filtering utilities |
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├── visualizations.py # Plotly chart generators |
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├── insights.py # Insight extraction helpers |
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├── utils.py # Shared helpers/constants |
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├── data/ # Curated datasets from Kaggle & UCI |
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│ ├── sales_train.csv |
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│ ├── items.csv |
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│ ├── item_categories.csv |
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│ ├── shops.csv |
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│ ├── test.csv |
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│ └── online_retail.csv # add this file from the UCI dataset |
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├── requirements.txt # Python dependencies |
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└── README.md # Project overview (this file) |
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``` |
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### Sample Datasets |
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- Kaggle *Predict Future Sales* (`sales_train.csv` plus lookup tables `items.csv`, `item_categories.csv`, `shops.csv`). |
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- UCI *Online Retail* (`online_retail.csv` — place the downloaded CSV in `data/`). |
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Use the **Load Sample** controls on the *Data Upload* tab to bootstrap analysis with these datasets. The app augments the Kaggle sales data by joining the lookup tables automatically. |
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### Getting Started |
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1. **Install dependencies** |
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```bash |
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pip install -r requirements.txt |
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``` |
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PNG exports require the optional `kaleido` dependency included above. |
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2. **Launch the dashboard** |
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```bash |
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python app.py |
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``` |
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3. **Load data** |
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- Upload your own CSV/Excel file **or** pick one of the bundled datasets via the *Load Sample* dropdown. |
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- Ensure the raw Kaggle/UCI CSV files reside in `data/` so the sample loader can detect them. |
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4. **Explore** |
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- Apply filters, switch between visualizations, inspect automated insights, and download filtered results or charts. |
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### Notes |
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- The app infers column types automatically; ensure date columns are parseable for time-series plots and trend insights. |
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- Large datasets may need additional preprocessing before upload to stay within local resource limits. |
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