🎓 Student Performance Prediction using Machine Learning

Predicting student exam scores by analyzing study habits, attendance, academic history, and other performance-related factors — powered by Random Forest, Gradient Boosting, and Linear Regression.

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📌 Overview

Academic performance depends on far more than just study hours — attendance, teacher quality, parental involvement, and prior achievement all play a role. This project builds and compares multiple regression models to predict a student's final exam score based on a rich set of behavioral, academic, and environmental factors.

The goal is twofold:

• 📊 Explore which factors most strongly influence exam performance
• 🤖 Predict exam scores using machine learning, comparing model accuracy across algorithms

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✨ Features

• 🧹 Data Cleaning — handles missing values via mode imputation, checks for duplicates
• 📈 Exploratory Data Analysis (EDA) — visualizes score distributions and key relationships (study hours, attendance, previous scores vs. exam score)
• 🔠 Categorical Encoding — one-hot encoding for non-numeric features
• ⚖️ Feature Scaling — standardization using StandardScaler
• 🤖 Multiple ML Models — trains and compares:
  • Random Forest Regressor
  • Gradient Boosting Regressor
  • Linear Regression
• 📉 Model Evaluation — MAE, RMSE, and R² Score for each model
• 🎯 Visual Diagnostics — actual vs. predicted scores, residual plots, and model comparison bar chart

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🗂️ Project Structure

Student-Performance-Prediction-using-Machine-Learning/
│
├── project_outputs/              # Generated plots & visualizations
│   ├── Distribution-of-Exam-Scores.png
│   ├── Hours-Study-VS-Exam-Score.png
│   ├── Attendance-VS-Exam-Score.png
│   ├── Previous-Score-VS-Exam-Score.png
│   ├── Actual-VS-Model-Prediction.png
│   ├── Residual-Plot.png
│   └── Model-Comparison.png
│
├── StudentPerformanceFactors.csv # Dataset
├── main.py                       # Main script (EDA + model training + evaluation)
├── requirements.txt              # Project dependencies
├── LICENSE                       # MIT License
└── README.md                     # Project documentation

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🧠 Dataset

The dataset (StudentPerformanceFactors.csv) includes features such as:

Feature	Description
Hours_Studied	Weekly study hours
Attendance	Attendance percentage
Previous_Scores	Prior academic performance
Teacher_Quality	Rating of teaching quality
Parental_Education_Level	Highest education level of parents
Distance_from_Home	Distance between home and school
...	Additional academic & lifestyle factors
Exam_Score	🎯 Target variable — final exam score

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⚙️ Installation

# Clone the repository
git clone https://github.com/talha-siddiqui137/Student-Performance-Prediction-using-Machine-Learning.git
cd Student-Performance-Prediction-using-Machine-Learning

# (Optional) Create a virtual environment
python -m venv venv
source venv/bin/activate      # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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▶️ Usage

Run the main script to perform EDA, train the models, and view evaluation results:

python main.py

This will:

1. Load and clean the dataset
2. Generate exploratory visualizations
3. Encode categorical variables and scale features
4. Train Random Forest, Gradient Boosting, and Linear Regression models
5. Print a comparison table of MAE, RMSE, and R² Score
6. Display actual vs. predicted and residual plots

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📊 Results

Model	MAE	RMSE	R² Score
Random Forest	1.23	2.35	0.912
Gradient Boosting	1.35	2.46	0.906
Linear Regression	1.46	2.57	0.899

Model Comparison

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🛠️ Tech Stack

• Language: Python 3.10+
• Data Handling: Pandas, NumPy
• Visualization: Matplotlib
• Machine Learning: scikit-learn (Random Forest, Gradient Boosting, Linear Regression)

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🚀 Future Improvements

• Add hyperparameter tuning (GridSearchCV / RandomizedSearchCV)
• Try additional models (XGBoost, LightGBM)
• Add cross-validation for more robust evaluation
• Build a simple web app (Streamlit/Flask) for live predictions
• Feature importance analysis for interpretability

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page or submit a pull request.

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📄 License

This project is licensed under the MIT License — feel free to use, modify, and distribute it.

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👤 Author

Talha Siddiqui GitHub: @talha-siddiqui137

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⭐ If you found this project useful, consider giving it a star on GitHub!