This project employs the Google Global Superstore dataset to predict product and category sales across different branches of the superstore. The analysis aims to compare the effectiveness of classic machine learning and deep learning methods in optimizing sales predictions within a global retail setting.
- Sales Prediction: Utilizes both classic machine learning and deep learning methods to forecast product and category sales.
- Global Retail Setting: Analyzes sales patterns across different branches of the Google Global Superstore.
- Model Comparison: Compares the performance of various models to optimize sales predictions.
- Implement classic machine learning models for sales forecasting.
- Apply deep learning methods to compare and contrast with traditional approaches.
- Evaluate and analyze the effectiveness of each method in predicting sales across global superstore branches.
- Checkout requirements.txt
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Clone the repository:
git clone https://github.com/your-username/sales-forecasting-global-superstore.git
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Install dependencies:
pip install requirements.txt
- SMBH
This project is licensed under the GPLv3 - see the LICENSE file for details.