This project represents the culminating effort of our team for the BAIT 508 course in the Master of Business Analytics program at the UBC Sauder School of Business.
Project Overview:
The primary objective of this project was to perform a comprehensive analysis of publicly-listed American companies in the food stores sector . This was achieved through a combination of diverse data analysis methods and the application of natural language processing (NLP) techniques.
Data Utilized:
The project utilized two main data sources. The first, '2020_10K_item1_full.csv', comprises a selection of 5,988 firms, along with the content from the 'item 1' section of their 2020 10-K reports. The second, 'public_firms.csv', includes data on public U.S. firms, sorted according to their industry sectors, which align with the first two digits of their Standard Industrial Classification (SIC) codes.
Included Files:
The project includes the following key files:
Analyis.ipynb: A Jupyter notebook segmented into three sections, focusing on the quantitative analysis, text analysis, and a detailed analysis of a select firm within the industry sector. Report.pdf: This report outlines the processes of data cleaning and analysis, and provides a comprehensive description of the results, including various charts and visuals.
Contributors :
Christi Mariam and Young Ji Tuen.
Acknowledgments:
We extend our gratitude to Gene Moo Lee, Jaecheol Park, and Xiaoke Zhang for their invaluable mentorship and support. A significant portion of the code used in this project was sourced from the UBC Master of Business Analytics BAIT 508 course.