This is an app to Predict the sales of Favorita Stores. This application leverages machine learning techniques to provide accurate sales predictions, enabling businesses to optimize inventory, plan promotions effectively, and make informed decisions. Whether you're a small business owner or a retail manager, this app offers a powerful solution for forecasting sales trends and maximizing profitability.
Make sure you have the following installed:
- Python 3.6+
- streamlit
- pandas
- Pillow (PIL)
- xgboost
- clone repository
- cd into file path where files are
- Install Dependencies:pip install -r requirements.txt
- Run the App:Strealit run sales_predict_app.py. The app will be accessible at local host
- Select the date for your prediction.
- Adjust the promo number using the slider.
- Choose specific days, clusters, stores, and product categories for prediction.
- Click the Predict button to generate sales predictions based on your inputs.
- Predictions will be displayed with appropriate formatting and color-coding.
The app uses an XGBoost-based machine learning model trained on historical sales data. By providing relevant input parameters, the app predicts future sales for informed decision-making.
We would like to thank Azubi Africa for the opportunity to learn how to build an app with treamlit and the team who made this possible inspite of the setbacks.
| Name | GitHub link |
|---|---|
| Doe Edinam | https://github.com/doeabla |
| Kofi Asare Bamfo | https://github.com/akbamfo |
| Enoch Taylor-Nketiah | https://github.com/kojoboyoo |
| Project | Name | Published Article |
|---|---|---|
| LP4 | Streamlit App | [Streamlit App LP4](https://medium.com/@eadoe97/empowering-retail-businesses-the-retail-store-sales-prediction-app-2b0a8fbaba8 |