https://github.com/husal90/retail-sales-forecasting-tool
A standalone browser-based application for retail sales forecasting and analysis. This tool helps retail businesses predict future sales trends using historical data, with no server-side processing required.
https://github.com/husal90/retail-sales-forecasting-tool
analysis css csv forecasting html javascript papaparse retail sales webapp
Last synced: 5 months ago
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A standalone browser-based application for retail sales forecasting and analysis. This tool helps retail businesses predict future sales trends using historical data, with no server-side processing required.
- Host: GitHub
- URL: https://github.com/husal90/retail-sales-forecasting-tool
- Owner: husal90
- License: mit
- Created: 2025-04-29T14:35:32.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-05-02T09:28:07.000Z (7 months ago)
- Last Synced: 2025-05-16T13:11:43.444Z (6 months ago)
- Topics: analysis, css, csv, forecasting, html, javascript, papaparse, retail, sales, webapp
- Language: HTML
- Homepage:
- Size: 194 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Retail Sales Forecasting Tool
A standalone browser-based application for retail sales forecasting and analysis. This tool helps retail businesses predict future sales trends using historical data, with no server-side processing required.
## Features
### Data Management
- **CSV Upload**: Import your sales data from CSV files with flexible column mapping
- **Sample Data**: Test the application with built-in sample data
- **Automatic Detection**: Handles different column names (sales/revenue, month/period/date)
### Forecasting Engine
- **Customizable Forecasts**: Set forecast length (1-12 periods)
- **Adjustable Parameters**:
- Smoothing factor
- Seasonality detection and inclusion
- Trend analysis and projection
- **Confidence Intervals**: Visualize upper and lower forecast bounds
### Visual Analytics
- **Interactive Charts**: View historical and forecast data
- **Pattern Recognition**: Analyze seasonal patterns with radar charts
- **Trend Analysis**: Linear regression trend line visualization
- **Key Performance Indicators**: Total sales, monthly averages, peak periods
### Export Options
- **CSV Export**: Download forecast data in CSV format
- **Report Generation**: Create detailed HTML reports with charts and analysis
## How It Works
The forecasting tool uses several statistical methods:
1. **Exponential Smoothing**: Applies weighted averages to historical data, giving more importance to recent data points
2. **Seasonality Detection**: Identifies repeating patterns in your data (monthly, quarterly)
3. **Trend Analysis**: Analyzes the overall direction of your sales data
4. **Confidence Intervals**: Calculates prediction ranges based on historical volatility
## Getting Started
### Installation
No installation required! Simply download the HTML file and open it in any modern web browser.
```bash
git clone https://github.com/husal90/retail-sales-forecasting.git
cd retail-sales-forecasting
```
Then open `retail_sales_forecasting_tool.html` in your browser.
### Usage
1. **Data Upload Tab**
- Upload your sales data CSV or use the sample data
- Preview your data in both chart and table formats
2. **Forecast Tab**
- Set your forecast parameters (periods, smoothing factor, seasonality, trend)
- Generate the forecast to see predictions with confidence intervals
3. **Analysis Tab**
- View detailed sales metrics and visualizations
- Analyze seasonal patterns and long-term trends
4. **Export Tab**
- Download your forecast data as CSV
- Generate a comprehensive HTML report
## CSV Format
Your CSV file should contain at least two columns:
- A time period column (month, date, period, time, etc.)
- A sales value column (sales, revenue, amount, value, etc.)
Example:
```
month,sales
Jan,12000
Feb,15000
Mar,18000
```
## Browser Compatibility
Tested and working in:
- Chrome 89+
- Firefox 86+
- Edge 89+
- Safari 14+
## Dependencies
This project uses the following libraries (included via CDN):
- [Chart.js](https://www.chartjs.org/) - For data visualization
- [PapaParse](https://www.papaparse.com/) - For CSV parsing
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Roadmap
- Add multiple forecasting algorithms
- Support for multi-variable forecasting
- Dark mode theme
- Save/load forecast projects
- Anomaly detection in historical data
## Author
GitHub: [@husal90](https://github.com/husal90)