CourseFinder

A content-based course recommendation system built with FastAPI, scikit-learn, and NLP. Select a course you love and discover the 5 most similar courses using TF-IDF vectorization and cosine similarity.

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Table of Contents

• Features
• Demo
• Installation
• Usage
• Deployment on Render
• Tech Stack
• Project Structure
• Contributing
• License

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Features

• Search-based course selection with real-time filtering and keyboard navigation
• Top 5 NLP-powered recommendations ranked by cosine similarity score
• Multi-page layout: Home, Recommend, About, Contact
• Dark/light theme toggle with localStorage persistence
• Responsive monochrome design, no external UI frameworks
• Instant results via precomputed similarity matrix

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Demo

Live Deployment (Render): https://udemy-harshit-course-recommender.onrender.com

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Installation

Clone the repository:

git clone https://github.com/HARSHIT071004/Course-recommender.git
cd Course-recommender

Create a virtual environment and activate it:

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

Install dependencies:

pip install -r requirements.txt

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Usage

Run the development server:

python -m uvicorn app:app --host 127.0.0.1 --port 5000 --reload

Or without reload:

python -m uvicorn app:app --host 127.0.0.1 --port 5000

Open your browser at http://127.0.0.1:5000.

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Deployment on Render

Ensure requirements.txt contains:

fastapi
uvicorn[standard]
jinja2
python-multipart
scikit-learn
pandas
gunicorn

On Render:

1. Create a New Web Service
2. Select Python 3 as environment
3. Connect your GitHub repo
4. Branch: main
5. Build Command: pip install -r requirements.txt
6. Start Command: gunicorn app:app
7. Deploy

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Tech Stack

• FastAPI — async Python web framework
• uvicorn — ASGI server
• scikit-learn — TfidfVectorizer, cosine_similarity
• pandas — data loading and manipulation
• Jinja2 — template rendering
• Vanilla JS — no framework, no build step

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Project Structure

Course-Recommender-NLP/
├── app.py                 # FastAPI routes and server entry point
├── model.py               # CourseRecommender class (TF-IDF + similarity)
├── requirements.txt       # Python dependencies
├── data/
│   └── courses.csv        # Udemy course dataset (11 columns)
├── static/
│   └── style.css          # Monochrome design system with dark/light themes
└── templates/
    ├── base.html           # Shared layout (nav, theme toggle, footer)
    ├── home.html           # Hero, features, how-it-works
    ├── recommend.html      # Search bar + recommendation grid
    ├── about.html          # Project info, tech stack, stats
    └── contact.html        # Contact form

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Contributing

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a Pull Request

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By Harshit Sharma

This project is licensed under the MIT License.