README for Visualization Script

File Structure

This project has a main Python script (./final_code/main.py) that generates visualizations for review data from an e-commerce platform. The script is dependent on a data file, which is found as truncated_filtered_reviews.csv.

The directory structure is as follows:

ECE_143_Project/
│
├── final-code/ # The Code to be used in the jupyter notebook portion and reviewed.
│   ├── data_parser.py # Code to parse the amazon review data (not needed to run since we're using a smaller pre-processed data that could be uploaded to the GitHub repo)
│   └── main.py # Main Code that contains the processing and visualization code.
├── final-plots/ # Contains all of the final visualization files that ended up in use for the presentation
├── scratch-code/ # Contains code from individual testing
├── plots/ # Contains all of the old plots that were made during testing and processing
├── truncated_filtered_reviews.csv # The filtered dataset that the code parses
└── ECE 143 Group 12 Slides.pdf # Presentation Slides PDF
└── presentation_graphs_notebook.ipynb # The jupyter notebook that runs and gets all of the graphs used in the presentation

note: Due to the large file size of the original filtered reviews csv file, there is a trucated_filtered_reviews.csv which is shortened to be within GitHub's file limits. Due to this, you may not have similar results to the results stored on the respository.

How to Run the Code

To run the script, please ensure that you have Python installed on your system along with the required third-party modules.

1. Install the necessary Python modules using pip if you haven't already:

   pip install pandas matplotlib seaborn nltk wordcloud numpy
   

2. Ensure that you have changed directory into the GitHub repo's base directory

3. locate ~/final_code/main.py and go to the bottom and uncomment the 3 functions that are {name}_main()

4. Run main.py from the repository base directory as the working directory.

Third-Party Modules Used

The script uses the following third-party Python modules:

• pandas: A fast, powerful, flexible and easy-to-use open-source data analysis and manipulation tool.
• matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
• seaborn: A Python data visualization library based on matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics.
• nltk: Python module that includes a library of stopwords which are used to filter stop words out of reviews when doing word frequency counts.
• wordcloud: Visualization module that allows for creation of wordclouds while utilizing matplotlib.
• numpy: used for the isnan function

Please ensure these modules are installed before executing the script. If the modules are not installed, the script will not run successfully.