🎓 Student Performance Prediction

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📌 Project Overview

This project predicts students' math scores based on demographic and academic features using Linear Regression.

It demonstrates a complete Machine Learning workflow including data exploration, preprocessing, feature engineering, model training, prediction, and performance evaluation.

This project is part of my Artificial Intelligence learning journey, where I build practical AI projects while studying Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, and Generative AI.

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🚀 Features

• 📂 Data Loading
• 🧹 Data Cleaning
• 📊 Exploratory Data Analysis (EDA)
• 📈 Data Visualization
• ⚙️ Feature Encoding
• 🔀 Train/Test Split
• 🤖 Linear Regression
• 📉 Model Evaluation
• 🎯 Prediction Analysis

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🛠️ Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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📂 Project Structure

Student-Performance-Prediction/
│
├── data/
│   └── StudentsPerformance.csv
│
├── notebooks/
│   └── Student_Performance_Prediction.ipynb
│
├── images/
│   ├── math_score_distribution.png
│   ├── correlation_heatmap.png
│   └── actual_vs_predicted.png
│
├── README.md
├── requirements.txt
└── .gitignore

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📊 Dataset

The project uses the Students Performance Dataset, containing information about:

• Gender
• Race / Ethnicity
• Parental Level of Education
• Lunch Type
• Test Preparation Course
• Math Score
• Reading Score
• Writing Score

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🔄 Machine Learning Workflow

• Data Loading
• Data Exploration
• Exploratory Data Analysis (EDA)
• Data Preprocessing
• Feature Engineering
• Feature Encoding
• Train/Test Split
• Linear Regression
• Model Training
• Model Evaluation
• Prediction

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📷 Project Visualizations

📈 Distribution of Math Scores

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🔥 Correlation Heatmap

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🎯 Actual vs Predicted Scores

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📈 Model Performance

Metric	Score
RMSE	5.39
R² Score	0.8804

Interpretation

• The model achieved an RMSE of 5.39, indicating a relatively low prediction error.
• The R² Score of 0.8804 shows that the model explains approximately 88% of the variance in students' math scores.
• These results demonstrate that Linear Regression provides a strong baseline model for this prediction task.

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💡 Future Improvements

As I continue learning Artificial Intelligence, I plan to enhance this project by experimenting with:

• Decision Tree Regression
• Random Forest Regression
• XGBoost
• Feature Selection
• Hyperparameter Tuning
• Cross Validation
• Deep Learning Models

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▶️ Getting Started

Clone the repository

git clone https://github.com/Ai-MAFlutter/Student-Performance-Prediction.git

Install dependencies

pip install -r requirements.txt

Launch Jupyter Notebook

jupyter notebook

Open:

notebooks/Student_Performance_Prediction.ipynb

Run all notebook cells.

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👩‍💻 Author

Marina Wahid

AI Student | Flutter Developer | Python Developer

🔗 GitHub: https://github.com/Ai-MAFlutter

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⭐ Support

If you found this project helpful, consider giving it a ⭐ on GitHub.