End to End Machine Learning Project

🎓 Student Performance Prediction

📌 Description

The Student Performance Prediction system is a machine learning-based model designed to predict student scores based on various features such as gender, parental education, test preparation, and previous scores. This tool assists educators and institutions in identifying students who need additional support, ultimately improving academic outcomes.

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📚 Table of Contents

• Description
• Features
• Installation
• Usage
• Project Snapshots
• Contributing

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🔥 Features

✅ Predict student performance based on demographic and academic factors.
✅ Utilizes machine learning models for accurate score prediction.
✅ Preprocessing pipeline to handle raw input data efficiently.
✅ Custom input support for real-time predictions.

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⚙️ Installation

Clone this repository to your local machine:

git clone https://github.com/CosmicVayu/mlproject.git

Navigate to the project directory:

cd mlproject

Set up a virtual environment (optional but recommended):

python -m venv venv  
source venv/bin/activate  # On macOS/Linux  
venv\Scripts\activate  # On Windows  

Install required dependencies:

pip install -r requirements.txt

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🛠️ Usage

Load the model and preprocessor:

• The trained model and preprocessing pipeline are stored in the artifacts/ directory.

Input student data for prediction:

• Features include:
  • Gender
  • Race/Ethnicity
  • Parental Education
  • Lunch Type
  • Test Preparation Course
  • Reading Score
  • Writing Score

Receive the predicted performance score:

• Input the data and get an accurate prediction of the student's performance.

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📊 Project Snapshots

======= 📌

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🤝 Contributing

Want to contribute? Follow these steps:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature/your-feature-name

3. Make changes and commit them:

   git commit -m "Add new feature"

4. Push to your branch:

   git push origin feature/your-feature-name

5. Open a pull request to the main branch for review.

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