README.md

metadata

title: Business Intelligence Dashboard
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false

Business Intelligence Dashboard

Interactive Gradio application for exploring business datasets, generating insights, and exporting filtered results.

Features

• Upload CSV or Excel files with automated validation and previews.
• Comprehensive statistics: numeric and categorical summaries, missing value report, correlation matrix.
• Dynamic filtering by numeric ranges, categorical selections, and date ranges with live row counts.
• Visualizations: time series, distribution, category comparisons, scatter plots, correlation heatmap.
• Automated insights: top/bottom performers, trend detection, anomaly identification.
• Export filtered data as CSV and download charts as PNG (requires kaleido).

Project Structure

BID/
├── app.py                  # Gradio UI wiring
├── data_processor.py       # Data loading, cleaning, filtering utilities
├── visualizations.py       # Plotly chart generators
├── insights.py             # Insight extraction helpers
├── utils.py                # Shared helpers/constants
├── data/                   # Curated datasets from Kaggle & UCI
│   ├── sales_train.csv
│   ├── items.csv
│   ├── item_categories.csv
│   ├── shops.csv
│   ├── test.csv
│   └── online_retail.csv   # add this file from the UCI dataset
├── requirements.txt        # Python dependencies
└── README.md               # Project overview (this file)

Sample Datasets

• Kaggle Predict Future Sales (sales_train.csv plus lookup tables items.csv, item_categories.csv, shops.csv).
• UCI Online Retail (online_retail.csv — place the downloaded CSV in data/).

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.

Getting Started

1. Install dependencies

   pip install -r requirements.txt
   

   PNG exports require the optional kaleido dependency included above.

2. Launch the dashboard

   python app.py
   

3. Load data

   • Upload your own CSV/Excel file or pick one of the bundled datasets via the Load Sample dropdown.
   • Ensure the raw Kaggle/UCI CSV files reside in data/ so the sample loader can detect them.

4. Explore

   • Apply filters, switch between visualizations, inspect automated insights, and download filtered results or charts.

Notes

• The app infers column types automatically; ensure date columns are parseable for time-series plots and trend insights.
• Large datasets may need additional preprocessing before upload to stay within local resource limits.