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# EcomPredict
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[](https://github.com/haybnzz/EcomPredict/blob/main/LICENSE)
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[](https://www.python.org/downloads/)
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[](https://github.com/haybnzz/EcomPredict/issues)
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[](https://github.com/haybnzz/EcomPredict/pulls)
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[](https://github.com/haybnzz/EcomPredict/stargazers)
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- **Targeted Marketing**: Provides insights for creating personalized marketing campaigns based on customer behavior trends.
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- **Product Recommendation**: Suggests products to customers based on their browsing and buying history.
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- **Sales Forecasting**: Helps in predicting future sales by understanding customer behavior and market trends.
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- **Customer Retention**: Identifies at-risk customers and provides strategies for increasing retention and loyalty.
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- **Market Segmentation**: Classifies customers into different segments based on behavior for more focused strategies.
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- **E-commerce Optimization**: Optimizes user experience and inventory management by predicting demand and trends.
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##
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1. Go to the [EcomPredict GitHub repository](https://github.com/haybnzz/EcomPredict).
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2. Click on the "Fork" button in the upper right corner of the page to create a copy of the repository under your own GitHub account.
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Once you've forked the repository, clone it to your local machine:
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cd EcomPredict
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pip install -r requirements.txt
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python data_read.py
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python csv_to_model.py
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python heatmap.py`
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- **Command:** `python data_read.py`
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- **Description:** Shows database
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- **Visualization:** (Fig 1.)
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##
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- **Command:** `python csv_to_model.py`
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- **Description:** Converts `data.csv` file to model `ecom_linear_regression_model.pkl` (670 B)
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- **Visualization:** (Fig 2.)
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- **Command:** `python heatmap.py`
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- **Description:** Creates a heatmap
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- **Visualization:** (Fig 3.)
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## 📜 License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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**Unauthorized use is strictly prohibited.**
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📧 Email: cubedimension@protonmail.com
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### Contributors and Developers
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[<img src="https://avatars.githubusercontent.com/u/67865621?s=64&v=4" width="64" height="64" alt="haybnzz">](https://github.com/haybnzz)
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[<img src="https://avatars.githubusercontent.com/u/144106684?s=64&v=4" width="64" height="64" alt="Glitchesminds">](https://github.com/Glitchesminds)
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## ☕ Support
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If you find this project helpful, consider buying us a coffee with cookies:
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[](https://ko-fi.com/codeglitch)
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# Model Card for EcomPredict
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<!-- Provide a quick summary of what the model is/does. -->
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This model card serves as a template for the EcomPredict model, designed to predict e-commerce trends using advanced machine learning techniques.
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## Model Details
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### Model Description
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EcomPredict is an advanced machine learning model aimed at predicting e-commerce sales trends, analyzing customer behavior, and optimizing product recommendations. It leverages historical data, sales patterns, and customer preferences to predict future trends with high accuracy.
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- **Developed by:** Hay.Bnz (Creator of EcomPredict)
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** Predictive Model
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- **Language(s):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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EcomPredict can be used directly by e-commerce businesses to forecast product demand, optimize inventory, and improve marketing strategies. The model generates sales predictions based on historical trends, which can be utilized by retail analysts and business decision-makers.
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### Downstream Use [optional]
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When fine-tuned for specific tasks, such as customer segmentation or personalized recommendations, EcomPredict can be incorporated into larger e-commerce ecosystems, improving product recommendations and customer experiences.
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### Out-of-Scope Use
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EcomPredict is not designed for uses unrelated to e-commerce trend prediction. Misuse may include applying the model in contexts outside retail, such as predicting trends for non-commercial industries without further customization.
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## Bias, Risks, and Limitations
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EcomPredict's predictions are based on historical data and may inherit biases present in the data. These biases could reflect demographic, geographical, or socio-economic trends, potentially leading to skewed predictions for certain customer segments.
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### Recommendations
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Users should be cautious when applying the model to new, unseen data. We recommend combining the model's output with domain expertise to account for external factors and mitigate potential biases.
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## How to Get Started with the Model
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To get started with EcomPredict, follow the code example below:
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```python
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# Example to load and use EcomPredict
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from ecompredict import EcomPredict
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model = EcomPredict.load("path_to_model")
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predictions = model.predict(input_data)
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