Course Recommendation System

This Python Flask application is designed to provide personalized course recommendations based on course descriptions. The application leverages a content-based filtering approach using TF-IDF Vectorization and Cosine Similarity to identify and recommend courses similar to a keyword entered by the user.

Prerequisites

Before running the application, ensure you have the following installed:

• Python 3.x
• pip (Python package installer)

Project Structure

Setup Instructions

1. Clone the repository or download the project files.

   git clone https://github.com/saboye/Course-Recommendation-System.git
   cd Course-Recommendation-System

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   venv\Scripts\activate  # On Windows
   # source venv/bin/activate  # On macOS/Linux

3. Install the required dependencies:

   pip install -r requirements.txt

4. Ensure the dataset (cleaned_dataset.csv) is in the project directory.

Running the Application

1. Run the Flask application:

   python app.py

2. Access the application: Open your web browser and go to http://127.0.0.1:5000/.

Application Usage

• Homepage: The homepage will display an input field where you can enter a keyword related to the course you are looking for.
• Recommendations: After entering a keyword and submitting the form, the application will display a list of recommended courses that match the keyword.

Example

1. Start the Flask server:

   python app.py

2. Open your web browser and navigate to http://127.0.0.1:5000/.

3. Enter a keyword (e.g., "Python") in the input field and click the "Get Recommendations" button.

4. View the recommended courses: The application will display a list of courses that match the entered keyword, including details such as course title, URL, description, university, rating, and skills.

Additional Information

• TF-IDF Vectorization: This technique is used to convert course descriptions into numerical vectors, highlighting the importance of words in each description.
• Cosine Similarity: This metric measures the similarity between course descriptions to provide relevant recommendations.
• Flask Framework: The application is built using Flask, a lightweight web framework for Python.