Ecommerce Spending Prediction Model

Objective of the Project:

The aim of this training project is to simulate a data analysis scenario for educational purposes. We will assist a hypothetical company in making a strategic decision regarding whether to prioritize enhancements to their mobile app experience or their website. Although the data used in this project is synthetic and intended solely for learning, it will guide us in providing insights and recommendations based on data-driven analysis.

I got the data from Kaggle

Data Description:

For this project, we will utilize a dataset sourced from Kaggle, which contains various metrics about customers interacting with an e-commerce platform. The dataset includes:

Average Session Length: Represents the average duration of in-store style advice sessions, providing insight into customer engagement during these interactions.

Time on App: Indicates the average amount of time customers spend on the mobile app, measured in minutes.

Time on Website: Shows the average duration customers spend on the website, also measured in minutes.

Length of Membership: Details the number of years each customer has been a member of the platform.

Please note that all personal information within the dataset is fictitious and used exclusively for the purpose of this educational project.

Evaluation

In evaluating the performance of our linear regression model, we obtained the following metrics:

Mean Absolute Error (MAE): 103.92

Mean Squared Error (MSE): 103.92

Root Mean Squared Error (RMSE): 10.19

The Mean Absolute Error indicates that, on average, the model's predictions deviate from the actual values by approximately 103.92 units. Similarly, the Mean Squared Error, which also equals 103.92, reflects the average squared differences between predicted and actual values. The Root Mean Squared Error, a more interpretable metric due to its unit consistency with the target variable, is 10.19, suggesting that the model's predictions are within this range of the actual values. These metrics collectively provide a comprehensive view of the model's prediction accuracy and reliability.

License

Distributed under the MIT License. Click LICENSE.md for more information.

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Contact

Akhin Abraham - twitter.com/akhinabr - theakhinabraham@gmail.com

Repository Link: https://github.com/theakhinabraham/ecommerce-spending-prediction

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