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
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:
Please note that all personal information within the dataset is fictitious and used exclusively for the purpose of this educational project.
In evaluating the performance of our linear regression model, we obtained the following metrics:
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.
Distributed under the MIT License. Click LICENSE.md for more information.
Akhin Abraham - twitter.com/akhinabr - theakhinabraham@gmail.com
Repository Link: https://github.com/theakhinabraham/ecommerce-spending-prediction