https://github.com/busradeveci/student-performance-prediction
A machine learning project to predict student exam performance based on academic, social, and personal features. Built with Python and scikit-learn.
https://github.com/busradeveci/student-performance-prediction
data-analysis kaggle linear-regression machine-learning predictive-modeling python scikit-learn student-performance
Last synced: about 1 year ago
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A machine learning project to predict student exam performance based on academic, social, and personal features. Built with Python and scikit-learn.
- Host: GitHub
- URL: https://github.com/busradeveci/student-performance-prediction
- Owner: Busradeveci
- Created: 2025-04-24T00:04:28.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-24T00:08:43.000Z (about 1 year ago)
- Last Synced: 2025-04-24T01:20:36.757Z (about 1 year ago)
- Topics: data-analysis, kaggle, linear-regression, machine-learning, predictive-modeling, python, scikit-learn, student-performance
- Language: Jupyter Notebook
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Student Exam Score Prediction 📚
This project aims to predict students' exam scores based on various academic, social, and personal features using a machine learning regression model.
## 📌 Dataset
The dataset includes features such as:
- Hours Studied
- Attendance
- Sleep Hours
- Previous Scores
- Family Income
- Internet Access
- Extracurricular Activities
- Motivation Level
- and more...
## 🧠 Model
A **Linear Regression** model was used for prediction.
Model was trained on encoded and normalized data.
## ⚙️ Tools & Libraries
- Python 🐍
- Pandas
- NumPy
- Scikit-learn
- Matplotlib / Seaborn (optional visualization)
## 📈 Results
Performance Metrics:
- **Mean Absolute Error (MAE):** 0.45
- **Mean Squared Error (MSE):** 3.25
- **R² Score:** 0.77
The model explains approximately 77% of the variance in the exam scores.
## 📁 File Structure
- `student-exam-score-prediction-model.ipynb` → Main notebook with all ML steps
- `README.md` → Project description
## ✨ Future Work
- Try other models: RandomForest, XGBoost
- Feature importance analysis
- Hyperparameter tuning