/MLProject

Primary LanguageJupyter Notebook

Student Math Score Prediction Project

Overview

This project focuses on predicting students' math scores based on various features such as

  • gender

  • ethnicity

  • parental_level_of_education

  • lunch

  • test_preparation_course

  • writing_score

  • reading_score

    This project implements machine learning algorithms to predict math scores based on relevant features. The goal is to build a predictive model that can assist in understanding the factors influencing math performance. output1 output2 output3

Project Structure

|-- MLProject
    
    |-- notebooks
        |-- data
          |-- stud.csv               # Raw data file
        |-- EDA Student.ipynb        # Data ingestion and exploration notebook
        |-- model_training.ipynb     # Model training and evaluation notebook
    |-- src
        |-- components                 # Model training module
            |-- data ingestion.py              # Load the data
            |-- data_transformation.py         # do the Feature engineering
            |-- model_trainer.py               # Model training module

        |-- Pipline               # creat the end to end pipline
            |-- predit_pipline.py
           
        |-- exception.py         # Custome Exception
        |-- loger.py             # creating logs
        |-- utils.py             # make common code

    |-- app.py                   # Flask application for prediction
    |-- requirements.txt             # Project dependencies
    |-- README.md                    # Project documentation

Requirements

  • Python 3.x
  • Jupyter Notebook (optional, for interactive development)
  • Required Python packages listed in requirements.txt Installation

Clone the repository:

git clone

https://github.com/NextIn035846/MLProject.git
cd MLProject.git

Install dependencies:

pip install -r requirements.txt

Flask Application:

Implement the Flask application for prediction using the trained models in the app.py file.

Run the Flask app with the command: python app.py.

Access the prediction endpoint at http://localhost:5000/predictdata with the required input parameters.

Benefits

Early Intervention: Identify students at risk of lower math scores early on, enabling timely intervention and support.

Resource Optimization: Allocate resources efficiently by targeting specific groups or individuals based on predicted performance.

Data-Driven Decision-Making: Empower educators and administrators with data-driven insights to enhance educational strategies.

Continuous Improvement: Continuously refine and improve the model based on new data and evolving educational dynamics.