Model Card for EcomPredict

EcomPredict is a machine learning model focused on predicting e-commerce customer behavior. It aims to enhance marketing strategies, improve product recommendations, and forecast sales based on customer behavior analysis. The project includes tools for customer segmentation, sales forecasting, and optimizing user experience on e-commerce platforms.

Model Details

• Developed by: haybnzz
• License: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
• Model Type: E-commerce prediction (regression)

Model Sources

• Repository: EcomPredict GitHub

Use Cases

• Direct Use: Predict customer purchasing behavior, recommend products, and forecast sales trends.
• Out-of-Scope Use: Not suitable for non-e-commerce applications or real-time prediction without further tuning.

Bias, Risks, and Limitations

• Limitations: The model may have biases depending on the dataset's representativeness of customer behavior.
• Risks: Misuse for non-targeted marketing strategies could lead to irrelevant recommendations.

How to Get Started

1. Clone the repository: git clone https://github.com/your-username/EcomPredict.git
2. Install dependencies: pip install -r requirements.txt
3. Run scripts:
   • Data reading: python data_read.py
   • Model creation: python csv_to_model.py
   • Heatmap creation: python heatmap.py

Training Details

• Data: Customer behavior dataset stored in data.csv.
• Preprocessing: Data is cleaned and converted into a regression model.

Evaluation

• Metrics: Model accuracy assessed via performance on customer data.