This project aims to create a predictive model for student project performance. It will assist educators in identifying students requiring early support, thereby enhancing overall success.

This will help you understand how you may give instructions on setting up your project locally. To get a local copy up and running, follow these simple example steps.

Follow these steps to install and set up the project directly from the GitHub repository:

1. Clone the Repository

   • Open your terminal or command prompt.

   • Navigate to the directory where you want to install the project.

   • Run the following command to clone the GitHub repository:

     git clone https://github.com/rohitmtak/student-performance-prediction.git
     

2. Create a Virtual Environment (Optional but recommended)

   • It's a good practice to create a virtual environment to manage project dependencies. Run the following command:

     conda create -p <environment_name> python==<python version> -y
     

3. Activate the Virtual Environment (Optional)

   • Activate the virtual environment based on your operating system:

     conda activate <environment_name>/
     

4. Install Dependencies

   • Navigate to the project directory:

     cd [project_directory]
     

   • Run the following command to install project dependencies:

     pip install -r requirements.txt
     

5. Run the Project

   • Run the following command which is a Flask application to get the required UI locally

     python app.py
     

1. MLflow URI

   • MLflow tracking server hosted at Dagshub

   https://dagshub.com/rohitmtak/student-performance-prediction.mlflow
   

2. MLflow Username

   • Username used to authenticate with the MLflow tracking server

   rohitmtak
   

3. MLflow Password

   • Password for authentication

   01a097a729d09de27bbe1b3cf35bb6c7e1a3e2399
   

https://github.com/rohitmtak

Rohit Tak - [email protected]