Predicting Online Shopper Purchasing Intention

Contributers/Authors

• Calvin Choi
• Nour Abdelfattah
• Sai Pusuluri
• Sana Shams

Project Summary

Given the surge of online shopping, online retailers may get a lot of site traffic but what ultimately matters is whether or not users finalize their purchase. Marketing and User Experience teams are tasked with optimizing a site’s interface and content in order to improve customer retention and the site’s revenue. Given this, understanding customer browsing behaviour and web page features is crucial for not only improving the user’s experience, but also maximizing the retailer’s revenue.

This project aims to analyze various features of online shopper’s sessions on a site to predict whether the customer makes a purchase. We will use the dataset, Online Shoppers Purchasing Intention dataset from the UCI Machine Learning Repository.

How to Run the Data Analysis

To replicate our analysis on your machine:

1. Clone this GitHub Repository on your local machine:
   • Click the green Code <> button and copy the URL.
   • On your local machine's terminal, navigate to the location where you would like this repository to reside in.
   • Run the command git clone <URL> in the terminal.
2. Creating the virtual environment
   • Navigate to the cloned repository on your machine. Ensure you are in the root of the repository.
   • Run the command conda env create --file environment.yaml in the terminal.
   • Activate the virtual environment by running the following command in the terminal: conda activate project_env
3. Running the analysis
   • Open the repository folder on your IDE (may vary depending on IDE)
   • Navigate to the analysis file by opening the project folder, then open the file Milestone1.ipynb
   • Make sure the kernel in your IDE is set to project_env.
4. Run the report from top to bottom in your IDE.
5. To deactivate the virtual environment, run the command conda deactivate

Running the Docker Container and Analysis

To run the project, you will have to run a docker container. To do so:

• Clone to project repository to your local computer
• Navigate to the project directory DSCI-310_Group-Project_Group8 in a new terminal
• Type docker-compose pull in your terminal

To view in IDE

• Type docker-compose run --rm project-image bash to enter the container
• Ensure you are in the root directory
• Use command make clean-all to reset the project
• Use command make all to run the analysis and produce the HTML report.
• Type exit in terminal to exit container.

OR

To view in Jupyter Notebook

• Type docker-compose up in your terminal
• This runs the container. You should find a set of URLs has been produced.
• Launch the link that starts with http://127.0.0.1 in your browser to view files in a jupyter notebook
• Now that you are in jupyter, open a terminal and make sure you are in the project's root directory.
• Use command make clean-all to reset the project
• Finally, type the make all command to run the analysis.
• Type ctrl + C (Windows) or command + C in terminal to exit.

Dependencies

• conda==23.11.0
• python=3.12
• pandas== 2.2.1
• jupyterlab==4.0.10
• numpy==1.26.4
• scikit-learn==1.4.0
• matplotlib==3.8.2
• seaborn==0.13.2
• click==8.1.7
• pytest=8.1.1
• pyYAML=6.0.1
• tabulate=0.9.0
• ucimlrepo

License Information

This project is licensed under the terms of the MIT Licence, offered under the MIT open source license. See the LICENSE.md file for more information.