Rate my professor data analysis

A data-driven deep dive into online professor ratings: uncovering bias, trends, and teaching quality through statistical and machine learning methods.

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Overview

This project explores over 5,000 professor ratings from RateMyProfessors.com to answer 10 research questions across themes such as:

• Gender-based bias in professor ratings
• The impact of physical attractiveness (“hotness”) on perceived quality
• How experience (number of ratings) affects student reviews
• Teaching difficulty vs. likability
• Differences between online and offline teaching styles
• Predictive modeling to forecast professor "hotness"

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Tools and Skills Used

• Python (pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels)
• Statistical Testing (Mann-Whitney U, Spearman correlation, chi-squared)
• Machine Learning (Logistic + Linear Regression, ROC-AUC)
• Data Cleaning & EDA
• Visualizations for storytelling and insights

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Project Structure

RMP-data-analysis/ ├── data/ # Raw CSV files (rmpCapstoneNum.csv, rmpCapstoneQual.csv) ├── plots/ # All figures saved from analysis ├── code_1.py# Main analysis script ├── FinalReport.pdf # PDF summary of full analysis ├── requirements.txt # Python dependencies ├── .gitignore # Files to exclude from Git └── README.md # Project documentation (this file)

PDF Report

A full PDF report with all findings, visualizations, and explanations is included:
FinalReport.pdf