File size: 2,871 Bytes
e665510 c9aa749 e665510 a48d292 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
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**
```bash
pip install -r requirements.txt
```
PNG exports require the optional `kaleido` dependency included above.
2. **Launch the dashboard**
```bash
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.
|