Personalised Recommendation System Using AWS

A simple-to-use Stream-lit application that recommends AWS learning resources based on user interests.

Features

• Interactive web interface built with Streamlit
• Content-based recommendation system using TF-IDF
• Mock S3 integration for development and testing
• Customizable learning resource database

Installation

1. Clone the repository:

git clone https://github.com/omar-steam/personalised-recommendation-system

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

streamlit run app.py

Usage

1. Enter your learning interests in the text input field
2. Click "Get Recommendations" to receive personalized learning path suggestions
3. View the top 3 recommended AWS learning resources

Testing

Run the tests using pytest:

pytest tests/

Requirements

See requirements.txt for full list of dependencies.

---

File: requirements.txt

streamlit==1.31.0
boto3==1.34.0
scikit-learn==1.3.0
moto==4.2.0
pytest==7.4.0

---

File: .gitignore

__pycache__/
*.py[cod]
*$py.class
.env
.venv
env/
venv/
ENV/
.pytest_cache/
.coverage
.DS_Store

---