🎯 Student Exam Performance Indicator

This project aims to predict students' math scores based on various factors such as gender, ethnicity, parental education, lunch type, and test preparation course. 📊 By analyzing the dataset and training machine learning models, the project provides insights into how these factors influence student performance.

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❓ Problem Statement

The goal of this project is to understand how students' performance (test scores) is affected by other variables such as:

• 👦👧 Gender
• 🌎 Ethnicity
• 🎓 Parental level of education
• 🍱 Lunch type
• 📚 Test preparation course

It also aims to build a machine learning model to predict math scores based on these factors.

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📂 Dataset

• Source: Kaggle - Students Performance in Exams
• Description: The dataset contains 1000 rows and 8 columns:
  • gender: Gender of the student (Male/Female)
  • race/ethnicity: Ethnicity of the student (Group A, B, C, D, E)
  • parental level of education: Parents' final education level
  • lunch: Type of lunch before the test (Standard/Free or Reduced)
  • test preparation course: Completion status of the test prep course
  • math score: Math test score
  • reading score: Reading test score
  • writing score: Writing test score

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🔁 Project Workflow

1. 🧪 Exploratory Data Analysis (EDA)

• Cleaned data, visualized patterns, and gathered insights:
  • ✅ Standard lunch improves performance
  • 👩‍🎓 Female students perform better overall, except in math
  • ✅ Test prep course boosts scores

2. 🧼 Data Preprocessing

• Encoded categorical features with OneHotEncoder
• Scaled numerical data with StandardScaler
• Split the data into training and testing sets

3. 🤖 Model Training

• Trained multiple regression models:
  • Linear Regression
  • Decision Tree Regressor
  • Random Forest Regressor 🌲
  • XGBoost, CatBoost, AdaBoost
• Evaluated using:
  • MAE, RMSE, and R² Score 📈

4. 🚀 Deployment

• Built a Flask web app for predictions
• Deployed on AWS Elastic Beanstalk 🌐

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🛠️ Installation

git clone https://github.com/your-username/student-performance-indicator.git
cd student-performance-indicator

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

pip install -r requirements.txt
python app.py

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🌍 Usage

1. Open your browser at http://127.0.0.1:5000
2. Fill in the form with student details
3. Click "Predict Your Math Score" to get results 🎯

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📁 Project Structure

mlprojects/
├── app.py
├── src/
│   ├── pipeline/
│   │   ├── predict_pipeline.py
│   │   └── train_pipeline.py
│   ├── utils.py
│   └── exception.py
├── notebook/
│   ├── 1. EDA STUDENT PERFORMANCE.ipynb
│   ├── 2. MODEL TRAINING.ipynb
├── templates/
│   ├── index.html
│   ├── home.html
├── artifacts/
├── data/
│   └── stud.csv
├── requirements.txt
└── README.md

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🏆 Results

• Best Model: Random Forest Regressor 🌲
• R² Score: 0.92 on test data 🚀

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🧰 Technologies Used

• Language: Python 🐍
• Libraries:
  • 📊 Pandas, NumPy
  • 📈 Matplotlib, Seaborn
  • 🤖 Scikit-learn, XGBoost, CatBoost
• Web: Flask 🌐
• Deployment: AWS Elastic Beanstalk ☁️

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🔮 Future Enhancements

• Add more features to boost accuracy ⚙️
• Dockerize and deploy with Kubernetes 🐳
• Try more ML models and ensembles

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👤 Author

Vijayakumar
💬 Feel free to reach out for questions or suggestions!

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📝 License

This project is licensed under the MIT License. See the LICENSE file for details.