Identifying At-Risk Students Using Clustering Techniques

Group Name

At-Risk Student CLustering

Project Title

Identifying At-Risk Students Using Clustering Techniques

Group Members

• Jayadev Tripathi
• Arbin Shrestha
• Ujwal Raj Bhattarai

Project Description

Project Idea and Approach

This project explores how clustering algorithms, specifically k-means, can be used to group students based on their performance metrics. The aim is to identify at-risk students by categorizing them into groups such as "high-performing," "average," and "at-risk." This classification helps educators to provide timely interventions and support where needed. The project uses a dataset with various student performance metrics and builds a model to perform clustering. An interactive dashboard is included to visualize these clusters and their correlations.

Tech Stack Used

• Python: Programming language used for the implementation.
• Streamlit: Framework used to develop the interactive web interface for data visualization.
• scikit-learn: Library used for implementing the k-means clustering algorithm.
• pickle: Used for saving and loading the clustering model.
• Pandas: For data manipulation and handling.
• NumPy: For numerical operations.
• Plotly: For interactive data visualizations.

Installation Instructions

1. Create Virtual Environment

  python -m venv 'env'

2. Activate Virtual Environment

venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

After setting up the environment and installing the dependencies, you can run the project as described in the project documentation.

Running the Project

To run the project, you can use the following command:

streamlit run interface.py

Screenshot: