This repository hosts a Linear Regression analysis for an Ecommerce company based in New York City. The company operates both online and offline, offering personal stylist sessions in-store and facilitating purchases via a mobile app or website. The analysis's goal is to determine whether the company should focus on enhancing their mobile app or their website experience.
The Ecommerce.csv file used in this analysis contains various metrics about the customers' interaction with the company's services. This dataset was obtained from Jose Portilla at Pieran Training.
The analysis involves building a Linear Regression model to identify the relationship between how much time customers spend on different platforms (app and website) and the amount of money they spend. The notebook 02-Linear Regression Project.ipynb details the model building and interpretation process.
The Linear Regression model provided the following coefficients, suggesting which factors have more impact on the yearly spend:
| Feature | Coefficient |
|---|---|
| Avg. Session Length | 25.981550 |
| Time on App | 38.590159 |
| Time on Website | 0.190405 |
| Length of Membership | 61.279097 |
Based on the model's findings, the recommendations are:
- To focus more on the mobile app, given its significant impact on revenue.
- To improve the website's functionality to leverage the untapped market potential and increase overall revenue.
02-Linear Regression Project.ipynb: A Jupyter notebook containing the detailed linear regression analysis and findings.Ecommerce Customers: A folder which is expected to include data files and potentially scripts related to the customer analysis.README.md: The markdown document providing an overview of this repository and the analysis.
For more details on the analysis and findings, refer to the Jupyter notebook included in this repository.