https://github.com/tayyabwaqar/productivity-analytics-bi
A machine learning based application to predict factory productivity.
https://github.com/tayyabwaqar/productivity-analytics-bi
analytics business-intelligence exploratory-data-analysis predictive-modeling trend-analysis
Last synced: 2 months ago
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A machine learning based application to predict factory productivity.
- Host: GitHub
- URL: https://github.com/tayyabwaqar/productivity-analytics-bi
- Owner: tayyabwaqar
- License: mit
- Created: 2024-08-19T18:22:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-21T02:25:05.000Z (about 1 year ago)
- Last Synced: 2025-03-05T02:42:58.449Z (7 months ago)
- Topics: analytics, business-intelligence, exploratory-data-analysis, predictive-modeling, trend-analysis
- Language: Python
- Homepage: https://ml-analytics-bi.streamlit.app/
- Size: 43.9 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Garment Productivity Dashboard
## Overview
The Garment Productivity Dashboard is a Streamlit application designed to analyze and predict the productivity of employees in the garment manufacturing industry. This application provides insights into productivity trends, operational efficiency, and predictive analytics using machine learning models.
## Live Demo
https://ml-analytics-bi.streamlit.app/
## Features
- **Exploratory Data Analysis (EDA)**: Visualizations to understand the dataset, including:
- Distribution of actual productivity
- Correlation heatmap
- Average productivity by department
- Overtime vs. actual productivity scatter plot
- **Predictive Modeling**: Uses Random Forest and Decision Tree algorithms to predict productivity based on various factors.
- **Anomaly Detection**: Identifies and visualizes anomalies in productivity data.
- **Trend Analysis**: Displays productivity trends over time for different departments.
- **Departmental Analysis**: Compares productivity across different departments using box plots.
- **Customizable Reports**: Allows users to select metrics for generating downloadable reports.
- **User Feedback Mechanism**: Collects feedback from users directly within the app.
- **Data Warehousing Concepts**: Provides explanations of key data warehousing concepts.
## Technologies Used
- Python
- Streamlit
- Pandas
- Plotly
- Scikit-learn
- NumPy
## Installation
To run this application locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/yourusername/garment-productivity-dashboard.git
cd garment-productivity-dashboard
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate
3. Install the required packages:
```bash
pip install -r requirements.txt
4. Ensure you have the dataset garment_productivity.csv in the same directory as the app.
## Usage
To run the Streamlit app, use the following command:
streamlit run app.py
## Contribution
Contributions are welcome! If you have suggestions for improvements or new features, please create an issue or submit a pull request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
Thanks to the contributors and the open-source community for their valuable resources and libraries that made this project possible.