Udemy Course Success Analysis

Overview

This project analyzes 209,000+ Udemy courses to uncover what makes a course profitable and highly rated. Using Python, pandas, data visualization, and multiple regression modeling, we investigated which factors — such as price, content length, reviews, and subscribers — influence both revenue and learner satisfaction.

The findings provide actionable insights for instructors, students, and content creators while demonstrating advanced data analytics techniques.

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Key Features

• Dataset: 209,000+ Udemy courses from Kaggle
• Exploratory Data Analysis: Distribution plots, correlations, and feature impact analysis
• Modeling Approach: Multiple Linear Regression for predicting:
  • Revenue (profitability)
  • Average Ratings (learner satisfaction)
• Performance Metrics:
  • R² for Revenue: 0.227
  • R² for Ratings: 0.050
• Core Technologies: Python, Pandas, Matplotlib, Scikit-learn, Statsmodels
• Collaboration: Team project focused on statistical modeling and business insights

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Project Structure

udemy-course-success-analysis/ │── data/ # (Optional sample data or Kaggle link) │── notebooks/ │ └── Udemy_Analysis.ipynb # Cleaned, structured notebook │── reports/ │ └── Computational_Statistics_Project.pdf # Final project report │── images/ # Charts & visualizations (optional) │── requirements.txt # Python dependencies │── README.md # Project documentation

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Insights and Findings

• Pricing and content length are strong predictors of profitability.
• Subscribers and reviews significantly affect learner satisfaction.
• Predicting overall success remains challenging due to hidden factors like teaching style and marketing strategy.
• Human-driven preferences limit predictive power, which explains the relatively low R² scores.

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Methodology

1. Data Preparation

   • Removed courses with invalid ratings (<3)
   • Filtered extreme outliers in revenue
   • Cleaned categorical variables like category and language

2. Exploratory Data Analysis

   • Analyzed revenue distributions and correlations
   • Plotted feature impacts on profitability and satisfaction

3. Model Development

   • Built multiple regression models
   • Evaluated model performance using R² and residual diagnostics

4. Conclusion

   • Identified actionable factors while recognizing modeling limitations

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How to Run the Project

1. Clone the repository:

git clone https://github.com/YOUR_USERNAME/udemy-course-success-analysis.git
cd udemy-course-success-analysis

2.Install dependencies:
pip install -r requirements.txt

3.	Open the notebook:
jupyter notebook notebooks/Udemy_Analysis.ipynb

Technologies Used
	•	Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels)
	•	Jupyter Notebook
	•	Multiple Linear Regression
	•	Statistical Hypothesis Testing
	•	Data Cleaning and Visualization

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Collaborators
	•	Benjamin Morris
	•	Inbar Sapir
	•	Yehuda Wexler

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Future Improvements
	•	Add predictive models like Random Forest or XGBoost to improve accuracy.
	•	Deploy an interactive dashboard using Streamlit for live revenue predictions.
	•	Automate feature selection for scalability.