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https://github.com/kaloa2025/adapted
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. With a backend server, a frontend application, and a Python-based prediction model.
https://github.com/kaloa2025/adapted
aiml courses-app expressjs mongodb-atlas nodejs postman prediction profile python reactjs study
Last synced: about 1 month ago
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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. With a backend server, a frontend application, and a Python-based prediction model.
- Host: GitHub
- URL: https://github.com/kaloa2025/adapted
- Owner: kaloa2025
- Created: 2024-05-26T05:02:05.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-06-17T16:02:06.000Z (6 months ago)
- Last Synced: 2024-06-18T12:42:12.805Z (6 months ago)
- Topics: aiml, courses-app, expressjs, mongodb-atlas, nodejs, postman, prediction, profile, python, reactjs, study
- Language: JavaScript
- Homepage: https://adapted-1-front.onrender.com/
- Size: 19.7 MB
- Stars: 1
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AdaptEd
This project is a 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, which are then displayed in a user-friendly interface. The project includes a backend server, a frontend application, and a Python-based prediction service.
## Table of Contents
- [Features](#features)
- [Tech Stack](#tech-stack)
- [Installation](#installation)
- [Usage](#usage)
- [Project Structure](#project-structure)
## Features
- User Authetication
- User can input their programming background and preferences.
- Machine learning model predicts a score based on user input.
- Courses are recommended based on the predicted score.
- User gives a quiz prior to enrolling in a course.
- Quiz generates a score which categorizes user in different levels.
- Based on levels user are suggested to start course from specific Lecture number.
- User-friendly interface to display recommended courses.
- Easy Navigation
## Tech Stack
- **Frontend**: React.js, CSS
- **Backend**: Node.js, Express
- **Machine Learning**: Python (pandas, joblib)
- **Database**: MongoDB (Atlas)
## Installation
### Prerequisites
- Node.js and npm installed
- Python installed
- MongoDB database set up
### Steps
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/adapted-course-recommendation.git
cd adapted-course-recommendation
```
2. **Set up the backend:**
```bash
cd backend
npm install
```
3. **Set up the frontend:**
```bash
cd ../frontend
npm install
```
4. **Set up the Python environment:**
```bash
cd ../Prediction
pip install -r requirements.txt
```
5. **Load the machine learning model:**
Place the `prediction_model.pkl` file in the `Prediction` directory.
6. **Set up environment variables:**
Create a `.env` file in the `backend` directory with the following content:
```
MONGODB_URI=your_mongodb_connection_string
```
## Usage
### Running the Backend Server
```bash
cd backend
nodemon index.js
```
### Running the Frontend Application
```bash
cd frontend
npm start
```
### Running the Prediction Service
```bash
cd Prediction
streamlit run predict.py
```
## Project Structure
```
adapted-course-recommendation/
│
├── backend/
│ ├── routes/
│ ├── models/
│ ├── controllers/
│ ├── index.js
│ └── .env
│
├── frontend/
│ ├── src/
│ │ ├── components/
| | | |── screens/
│ │ ├── contexts/
│ │ ├── hooks/
│ │ ├── styles/
│ │ ├── App.js
│ │ └── index.js
│ ├── public/
│ └── package.json
│
├── Prediction/
│ ├── predict.py
│ └── prediction_model.pkl
│
├── README.md
└── .gitignore
```
Happy coding! 😊
This 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.
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## See Working Here
https://youtu.be/EpBzKjrsVRE
### SCREENSHOTS
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