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Coursera Course Prediction

This project uses a Kaggle Coursera dataset to recommend courses based on user preferences and patterns in the data. It leverages machine learning techniques to analyze course metadata and user behavior to provide personalized course recommendations.

Table of Contents

Features

  • Course Recommendation: Suggests relevant Coursera courses to users based on their preferences or activity.
  • Data Analysis: Provides insights into trends and patterns in the Coursera dataset.
  • Machine Learning Integration: Implements algorithms to predict course preferences.

Dataset

The dataset used in this project is sourced from Kaggle and contains information about Coursera courses, including:

  • Course names
  • Categories
  • User reviews
  • Ratings
  • Enrollments

Link to Dataset: Kaggle Coursera Dataset

Technologies Used

  • Programming Language: Python
  • Libraries:
    • Pandas
    • NumPy
    • Scikit-learn
    • Matplotlib/Seaborn (for visualization)
    • Flask (optional, for web deployment)

Setup Instructions

  1. Clone the Repository

    git clone https://github.com/Crusherbolt/Coursera-Course-Prediction.git
cd Coursera-Course-Prediction

  2. Install Dependencies Ensure you have Python 3.8+ installed. Then, install the required libraries using:

    pip install -r requirements.txt

  3. Download the Dataset

    • Download the Kaggle Coursera dataset from the link provided above.
    • Place the dataset file in the project directory and rename it as coursera_data.csv.

  4. Run the Script Execute the main script to generate predictions:

    python main.py

Usage

  • For Data Analysis: Run the analysis module to visualize and explore trends in the dataset.
  • For Recommendations: Input user preferences, and the system will output a list of recommended courses.

Results

The project predicts courses based on user input and has shown promising results, including:

  • Accurate matching of courses to specific categories or user preferences.
  • Insights into popular courses and trends in the dataset.

Future Improvements

  • Incorporate additional datasets to enhance recommendations.
  • Develop a web-based interface using Flask for easier interaction.
  • Include more advanced machine learning models for better accuracy.
  • Implement real-time user feedback to refine recommendations.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that you follow the project guidelines and include proper documentation.

License

This project is licensed under the MIT License. See the LICENSE file for details.