Palestine Launchpad Hackathon Team: [TeamP3-2]

Welcome to the [Your Team Name] repository! We are a passionate group of individuals competing in the Palestine Launchpad Hackathon—an exclusive AI, Data Science, and Web Development challenge. Our team is here to build innovative solutions, collaborate effectively, and make an impact by solving real-world problems. This repository will document our progress, the work we've done, and the exciting solution we're developing for the Personalized Learning Path Recommender problem statement.

Team Members

• [Member 1 Name] - Role (e.g., AI Engineer, Data Scientist, Web Developer)
• [Member 2 Name] - Role (e.g., Frontend Developer, Data Scientist)
• [Member 3 Name] - Role (e.g., Backend Developer, Designer)
• [Member 4 Name] - Role (e.g., Project Manager, QA)

Project Overview

We are building a Personalized Learning Path Recommender system that analyzes a learner's profile to suggest tailored learning paths for mastering technical fields such as AI, Data Science, and Web Development. Our solution will make it easier for individuals to map out their learning journey and improve their skills efficiently.

Problem Statement:

Create a system or web app that personalizes learning paths for users based on their prior knowledge and goals in fields like AI, Data Science, or Web Development.

Core Features:

• Profile Creation: Users can create profiles and input data about their knowledge, interests, and learning goals.
• Recommendation System: An AI-driven algorithm will suggest personalized learning paths tailored to the user’s specific needs.
• Progress Tracking: The system will track users' progress, helping them stay on track and update recommendations.
• User Interface: A clean and intuitive web application that allows users to easily interact with the system.

Our Tech Stack

We’re using the following tools and technologies to build our solution:

• Frontend: HTML5, CSS3, JavaScript
• Backend: Python (Flask/Django), Node.js
• Bussiness Intelegence: Power Bi
• AI/ML: Scikit-learn, TensorFlow, Keras
• Version Control: Git, GitHub
• Deployment: Heroku, Docker

Roadmap

We have a clear plan of action to make sure we deliver our solution on time. Here’s a quick look at our roadmap:

• Phase 1: Idea conceptualization and team formation
• Phase 2: Data Analysis
• Phase 3: Developing the recommendation algorithm
• Phase 4: Building the frontend web interface
• Phase 5: Integration and testing
• Phase 6: Final submission and presentation

How to Contribute

We believe in open collaboration! If you're interested in helping or contributing, feel free to fork the repository, create a branch, and submit a pull request. Here are some ways you can contribute:

• Code Contributions: Help us with algorithms, frontend, backend, or database setup.
• Design: Provide UI/UX feedback and suggestions to improve the platform.
• Testing: Assist with bug testing, user experience feedback, and ensuring the solution works across different environments.

License

This project is licensed under the MIT License.