Educational institutions often struggle to identify and support students at risk of academic failure. This web application aims to address this challenge by providing a tool powered by artificial intelligence to predict student success and offer actionable insights for educators.
Welcome to our AI-Driven Student Success Forecaster project, developed by Our Team! Predict student success using AI-driven insights.
Access the AI-Driven Student Success Forecaster web application without the need for local installation by simply visiting the following URL: Student Success Forecaster Web App
Our Team aims to:
- Improve student retention rates.
- Provide early intervention for students at risk.
- Enhance the overall educational experience for both students and educators.
- User-Friendly Web Interface
- Data Analysis & Predictive Modeling
- Customizable & Actionable Insights
These instructions will help you set up and run the project on your local machine.
You will need Python 3.x and pip installed on your machine.
- Clone the repository:
git clone https://github.com/cu2021/Students_grade_forcasting.git - Navigate to the project directory:
cd Student-grade-forcasting_web_app - Install the required packages using pip:
pip install -r requirements.txt
- Start the Flask web application:
python app.py - Access the Web App in your web browser at http://localhost:5000.
- Our Mentor - Anas Alyan - Data Analyst - LinkedIn Profile
- Othman Shbeir - Data Analyst - LinkedIn Profile
- Sajeda Ali - Data Analyst - LinkedIn Profile
- Rami Asad - AI Engineer - LinkedIn Profile
- Shurouq Abu Ewaili - AI Engineer - LinkedIn Profile
- Sary Hammad - AI Engineer
- Yousef Jedrini - Web Developer
- Islam Safia - Web Developer - LinkedIn Profile