🎓 Student Performance Prediction Using Deep Learning

Deep Learning • Education Analytics • Data Visualization
Predicting student performance with TensorFlow neural networks and data-driven insights.

Visual summary of the project: predicting student performance using TensorFlow neural networks and educational analytics.

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📑 Table of Contents

• 🎓 Overview
• 🔍 Objectives
• ⚡ Key Features
• 📁 Repository Structure
• 🧪 Technologies Used
• 🧠 Model Architectures
  • 🔹 Regression Model — Predicting Exam Scores
  • 🔹 Classification Model — Performance Tier Prediction
• 📈 Results & Evaluation
  • 📉 Regression Results
  • 📊 Classification Results
• 🚀 Installation
• 🖥️ Usage
• 📊 Visualizations
• 🔮 Future Improvements
• 📄 License
• 🙏 Acknowledgments
• 👤 Author

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📘 Overview

This project applies Deep Learning to predict student performance from real-world data.
It covers the full machine learning lifecycle — data exploration, exploratory data analysis, preprocessing, modeling, and evaluation — to extract actionable insights that enhance learning outcomes.

🔍 Objectives

• Analyze and visualize student performance patterns
• Build predictive models using Artificial Neural Networks (ANNs)
• Evaluate results using key metrics for regression and classification

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⚡ Key Features

✅ Complete data science pipeline (EDA → Modeling → Evaluation)
✅ Built with TensorFlow/Keras for scalable deep learning
✅ Interactive Jupyter Notebook walkthrough
✅ Beautiful data visualizations for insights and interpretability
✅ Reproducible and modular — ideal for research or education projects

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📂 Repository Structure

Path	Description
.gitignore	Git configuration to exclude unnecessary files
LICENSE	MIT license for open-source distribution
README.md	Project overview, methodology, and visualizations
requirements.txt	Python dependencies for running the project
StudentPerformanceFactors.csv	Dataset containing student performance features
student_performance_dl_analysis.ipynb	Main notebook with EDA, preprocessing, modeling, and evaluation
assets/	Folder contains visual assets used in the README (plots, thumbnails, etc.)

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🧪 Technologies Used

Category	Tools / Libraries
Language	Python 3.10+
Data Processing	NumPy, Pandas, Scikit-learn
Visualization	Matplotlib, Seaborn
Modeling	TensorFlow, Keras
Environment	Jupyter Notebook

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🧠 Model Architectures

This project features two deep learning models built with TensorFlow/Keras: one for regression and one for classification. Both models share a clean, interpretable architecture and are optimized for educational data.

🔹 Regression Model — Predicting Exam Scores

Layer	Configuration
Input Layer	Receives preprocessed feature vector
Hidden Layer 1	Dense(256), ReLU activation
Dropout Layer	Dropout(0.3)
Hidden Layer 2	Dense(128), ReLU activation
Hidden Layer 3	Dense(64), ReLU activation
Output Layer	Dense(1)

• Loss Function: Mean Squared Error (MSE)
• Optimizer: Adam
• Evaluation Metric: Mean Absolute Error (MAE)

This model predicts continuous exam scores based on behavioral and academic features.

🔹 Classification Model — Performance Tier Prediction

Layer	Configuration
Input Layer	Receives preprocessed feature vector
Hidden Layer 1	Dense(128), ReLU activation
Hidden Layer 2	Dense(64), ReLU activation
Output Layer	Dense(3), Softmax activation

• Loss Function: Categorical Crossentropy
• Optimizer: Adam
• Evaluation Metric: Accuracy

This model classifies students into three performance tiers: Low, Medium, and High

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📈 Results & Evaluation

Model performance was evaluated using key metrics and visual diagnostics from the notebook.

📉 Regression Results

• MSE: ~4.48
• MAE: ~0.89
• R² Score: ~0.69

Evaluation Visuals:

• Predicted vs. Actual grade scatter plot

• Training vs Validation Loss Curve

• Residual Distribution Plot

📊 Classification Results

• Training Accuracy: 100%
• Validation Accuracy: ~98%

Evaluation Visuals:

• Training vs Validation Accuracy

The results indicate that both models generalize well, with stable learning curves and limited overfitting due to dropout and early stopping.

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🚀 Installation

1. Clone the repository

git clone https://github.com/ArianJr/student-performance-deep-learning.git
cd student-performance-deep-learning

2. Install dependencies

pip install -r requirements.txt

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🖥️ Usage

Open the Jupyter notebook to explore the analysis and models:

jupyter notebook student_performance_dl_analysis.ipynb

Follow the notebook to:

• 📊 Explore data distributions
• 🧠 Build and train models
• 📈 Evaluate predictive performance

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📊 Visualizations

Visual insights play a key role in understanding student performance:

• Correlation Heatmap: Reveals relationships between features
• Class Distribution: Shows balance of target labels
• Model Metrics: Visualizes loss and accuracy trends

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🔮 Future Improvements

• 🧩 Feature Engineering: Explore polynomial or interaction features
• 🧠 Hyperparameter Tuning: Use KerasTuner or Optuna for optimal architectures
• 🧾 Cross-Validation: Add k-fold validation to reduce variance
• 📊 Explainability: Incorporate SHAP or LIME for feature importance visualization
• ⚙️ Deployment: Wrap models in a Flask API or Streamlit dashboard for real-time prediction
• 📈 Data Expansion: Include demographic and attendance trends for improved accuracy

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📄 License

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

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🙏 Acknowledgments

• Dataset Source: Kaggle - Student Performance
• Libraries: TensorFlow, Keras, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• Inspired by educational data mining techniques and performance analytics research

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👤 Author

Arian Jr
📧 Contact Me • 🌐 GitHub Profile

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Made with ❤️ by ArianJr

_{⭐ If you found this project useful, please consider giving it a star! It helps others discover it and supports my work.}

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