Predicting Student Performance Using ML

Introduction

This project utilizes machine learning techniques to predict student performance based on historical data. The goal is to provide insights that can help improve educational outcomes.

Project Structure

student-performance-predictor/
├── assets/
│ └── img.png
├── data/
│ └── data.csv
├── src/
│ ├── __init__.py
│ ├── logger.py
│ ├── exception.py
│ ├── utils.py
│ ├── components/
│ │ ├── __init__.py
│ │ ├── data_ingestion.py
│ │ ├── data_transformation.py
│ │ └── model_trainer.py
│ ├── pipeline/
│ │ ├── __init__.py
│ │ ├── train_pipeline.py
│ │ └── predict_pipeline.py
│ └── templates/
│   └── index.html
├── app.py
├── requirements.txt
└── README.md

Technologies Used

• Python: Version 3.10 for core programming.
• Scikit-learn: For building and training machine learning models.
• Flask: For creating a web application to serve predictions.

Installation

To set up the project, follow these steps:

1. Create a new Conda environment:

   conda create --name myenv python==3.10

2. Activate the environment:

   conda activate myenv

3. Install the required dependencies:

   pip install -r requirements.txt

Running the Project

To run the project, follow these steps:

1. Navigate to the src/components directory (To do model training and saving the model):

      python data_ingestion.py

2. Navigate to the project directory:

   cd project

3. Run the Flask server:

   python app.py

Once the server is running, you can access the application in your web browser at:
http://127.0.0.1:5000