{"id":20977378,"url":"https://github.com/kaloa2025/adapted","last_synced_at":"2025-05-14T14:32:20.818Z","repository":{"id":241326948,"uuid":"806003224","full_name":"kaloa2025/AdaptEd","owner":"kaloa2025","description":"An web application designed to recommend courses based on a user's programming background and preferences. The application uses a machine learning model to predict the best courses for users. 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The application uses a machine learning model to predict the best courses for users, which are then displayed in a user-friendly interface. The project includes a backend server, a frontend application, and a Python-based prediction service.\n\n## Table of Contents\n\n- [Features](#features)\n- [Tech Stack](#tech-stack)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Project Structure](#project-structure)\n\n## Features\n\n- User Authetication\n- User can input their programming background and preferences.\n- Machine learning model predicts a score based on user input.\n- Courses are recommended based on the predicted score.\n- User gives a quiz prior to enrolling in a course.\n- Quiz generates a score which categorizes user in different levels.\n- Based on levels user are suggested to start course from specific Lecture number.\n- User-friendly interface to display recommended courses.\n- Easy Navigation\n\n## Tech Stack\n\n- **Frontend**: React.js, CSS\n- **Backend**: Node.js, Express\n- **Machine Learning**: Python (pandas, joblib)\n- **Database**: MongoDB (Atlas)\n\n## Installation\n\n### Prerequisites\n\n- Node.js and npm installed\n- Python installed\n- MongoDB database set up\n\n### Steps\n\n1. **Clone the repository:**\n\n```bash\ngit clone https://github.com/your-username/adapted-course-recommendation.git\ncd adapted-course-recommendation\n```\n\n2. **Set up the backend:**\n\n```bash\ncd backend\nnpm install\n```\n\n3. **Set up the frontend:**\n\n```bash\ncd ../frontend\nnpm install\n```\n\n4. **Set up the Python environment:**\n\n```bash\ncd ../Prediction\npip install -r requirements.txt\n```\n\n5. **Load the machine learning model:**\n\nPlace the `prediction_model.pkl` file in the `Prediction` directory.\n\n6. **Set up environment variables:**\n\nCreate a `.env` file in the `backend` directory with the following content:\n\n```\nMONGODB_URI=your_mongodb_connection_string\n```\n\n## Usage\n\n### Running the Backend Server\n\n```bash\ncd backend\nnodemon index.js\n```\n\n### Running the Frontend Application\n\n```bash\ncd frontend\nnpm start\n```\n\n### Running the Prediction Service\n\n```bash\ncd Prediction\nstreamlit run predict.py\n```\n\n## Project Structure\n\n```\nadapted-course-recommendation/\n│\n├── backend/\n│   ├── routes/\n│   ├── models/\n│   ├── controllers/\n│   ├── index.js\n│   └── .env\n│\n├── frontend/\n│   ├── src/\n│   │   ├── components/\n|   |   |   |── screens/\n│   │   ├── contexts/\n│   │   ├── hooks/\n│   │   ├── styles/\n│   │   ├── App.js\n│   │   └── index.js\n│   ├── public/\n│   └── package.json\n│\n├── Prediction/\n│   ├── predict.py\n│   └── prediction_model.pkl\n│\n├── README.md\n└── .gitignore\n```\n\nHappy coding! 😊\n\nThis README provides a comprehensive overview of your project, including installation and usage instructions, which should help users get started quickly. Make sure to replace placeholders like `your-username` and `your_mongodb_connection_string` with the actual values.\n---\n\n## See Working Here\nhttps://youtu.be/EpBzKjrsVRE\n\n### SCREENSHOTS\n---\n![Pre_Planning3](https://github.com/kaloa2025/AdaptEd/assets/113432220/edfeec36-5c9e-43ef-814f-8c414e5d2a4e)\n![Pre_Planning](https://github.com/kaloa2025/AdaptEd/assets/113432220/e68b2f97-e33e-4045-a0f2-2a2ab2f97203)\n![Pre_Planing2](https://github.com/kaloa2025/AdaptEd/assets/113432220/d32debcb-0556-4177-8da2-b7592f724357)\n---\n![Figma](https://github.com/kaloa2025/AdaptEd/assets/113432220/0c624b09-acfe-45d3-8d0f-bded2c9fb18b)\n\u003cimg width=\"820\" alt=\"DB_Structure\" src=\"https://github.com/kaloa2025/AdaptEd/assets/113432220/87fc483e-f52c-48c4-b014-72ed9c09d469\"\u003e\n![wireframe](https://github.com/kaloa2025/AdaptEd/assets/113432220/1c398cf4-078e-4f1f-84f6-10e67011ab40)\n---\n![Landing_Page](https://github.com/kaloa2025/AdaptEd/assets/113432220/3a5d7fd6-f03e-42b1-a459-35a1069efb29)\n![Course_Detail_page](https://github.com/kaloa2025/AdaptEd/assets/113432220/28c12659-328e-40f8-becb-f85240448aed)\n![Suggested_Page](https://github.com/kaloa2025/AdaptEd/assets/113432220/d6923393-3443-4bbb-8729-921d6559a752)\n![Screenshot 2024-06-02 220403](https://github.com/kaloa2025/AdaptEd/assets/113432220/6f06dc7c-6726-4a20-a3ba-99d88a6065f8)\n---\n![Python_Folder_Structure](https://github.com/kaloa2025/AdaptEd/assets/113432220/cc36849c-0e36-42ad-b228-d254fc58ef8a)\n![Project_Structure](https://github.com/kaloa2025/AdaptEd/assets/113432220/7f7692e6-75ff-4cfa-b51c-51f5b920d729)\n![Terminals](https://github.com/kaloa2025/AdaptEd/assets/113432220/aa0730e2-ff9a-43b6-a4fd-518c48a55889)\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkaloa2025%2Fadapted","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkaloa2025%2Fadapted","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkaloa2025%2Fadapted/lists"}