A machine learning project that predicts yearly customer spending using linear regression on an e-commerce dataset.
This project implements a linear regression model to predict the yearly amount spent by customers based on various features from an e-commerce dataset. The model helps businesses understand customer spending patterns and make data-driven decisions for marketing and sales strategies.
The project uses an E-commerce Customer Dataset containing customer information and their spending patterns. Features
Avg. Session Length: Average session duration on the website/app Time on App: Time spent on the mobile application Time on Website: Time spent on the website Length of Membership: How long the customer has been a member Yearly Amount Spent: Target variable - total yearly spending (to be predicted)
Python 3.x Jupyter Notebook Pandas - Data manipulation and analysis NumPy - Numerical computations Matplotlib - Data visualization Seaborn - Statistical data visualization Scikit-learn - Machine learning library
The linear regression model achieves:
R² Score: 103.9155413650325 Mean Absolute Error (MAE): 8.426091641432068 Root Mean Square Error (RMSE): 10.193897260863114
Contributions are welcome! Please feel free to submit a Pull Request.
Fork the project Create your feature branch Commit your changes Push to the branch Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.