This data (attached below named sample_appointment_data.xls) includes a general

This data (attached below named sample_appointment_data.xls) includes a general category for the appointment type and a corresponding description.
To gain further insights into students’ career support, I would like you to utilize the information and descriptions provided to generate new variables. These variables should offer us additional understanding in the following areas:
1. The primary topics for which students are seeking assistance.
2. For students requesting help with their CVs, is there any additional information that could help us comprehend their specific needs?
3. Can we identify students’ career trajectories, such as their prospects in the labor market, pursuit of advanced degrees, or enrollment in professional schools, based on their career appointments?
I already did some analysis by using python, and my analysis report is also attached below. In this time, I want you keep working on the data on the main.py (attached report) and try to extract and illustrate the primary topics for which students are seeking assistance by using OpenAI with Python. Focus on improving the code (especially main.py) to analyze the topics of career appointments. So, again, you main goal is to generate subtopics within student seeking for career, achieve by analyzing paragraphs: frequency topics, like writing CV…
I am considering you using the integration of OpenAI with Python to generate your analysis. keyword_recognition.py, as an example, shows how to import your Chatgpt account in a Python file.
Deliver: your updated main.py.& a report of your analysis which shows the topics you generate