This project explores how students describe male and female professors differently using data from RateMyProfessors.com. The goal is to analyze whether students' reviews exhibit gender-based differences using sentiment analysis, word clouds, and machine learning models.
Data Source: (https://data.mendeley.com/datasets/fvtfjyvw7d/2)
RateMyProfessorReviewAnalysis.ipynb: The main notebook containing the code for the cleaned and analyzed data variaus
data2 folder - contains 2,763 cvs files of each indiviual student reviews from ratemyprofessor for variaus University/colleges and departments
concatenated_data.cvs - unlcleaned dataset containing the concatenated dataset of each indiviual student reviews, resulting in 20,000 rows of student reviews
Python libraries that you may need to be installed before running the notebook.
Required Libraries: Pandas NumPy Matplotlib Seaborn Scikit-learn nlt