🎯 Student Math Score Prediction - Machine Learning Project

This project aims to predict the math score of students based on various academic and demographic features. It showcases a full machine learning workflow, from data preprocessing and EDA to feature engineering and model evaluation.

📂 Dataset Description

Column	Description
gender	Student's gender (male / female)
race_ethnicity	Student group based on race/ethnicity
parental_level_of_education	Parent's highest education level
lunch	Type of lunch (standard / free/reduced)
test_preparation_course	Completion of test preparation course
reading_score	Reading test score
writing_score	Writing test score
math_score	Target variable - score in mathematics

📝 Sample Dataset

gender	race_ethnicity	parental_level_of_education	lunch	test_preparation_course	reading_score	writing_score	math_score
female	group B	bachelor's degree	standard	none	72	74	72
female	group C	some college	standard	completed	90	88	69
female	group B	master's degree	standard	none	95	93	90
male	group A	associate's degree	free/reduced	none	57	44	47
male	group C	some college	standard	none	78	75	76

🎯 Project Objectives

• 📊 Conduct Exploratory Data Analysis (EDA) to understand distributions and correlations.
• 🧹 Preprocess data with encoding, scaling, and transformation techniques.
• 🧠 Train and evaluate multiple regression models to predict math_score.
• 📈 Evaluate models using R², MAE, and RMSE metrics.

🧪 Machine Learning Techniques Applied

• Categorical Encoding: Label & One-Hot Encoding
• Feature Engineering: Derived features & normalization
• Multiple Regression Models: Tried various ML regressors
• Evaluation Metrics: R² Score, MAE, RMSE

📈 Key Insights

• 📚 Students who completed the test preparation course generally scored higher.
• ✍️ Reading and writing scores are strongly correlated with math scores.
• 🎓 Parental education and lunch type influence student performance.

💻 Technologies Used

• Python (Jupyter Notebook)
• Pandas, NumPy – Data manipulation
• Matplotlib, Seaborn – Visualizations
• Scikit-learn – Machine learning
• Flask – Web app deployment (optional)

▶️ How to Run

1. Clone the repository or download the notebook file.
2. Install dependencies:

   pip install -r requirements.txt

3. Run the notebook step-by-step in Jupyter.
4. Optional: Start Flask app using:

   python app.py

📬 Feedback & Contributions

Feel free to fork the repo, contribute, or share your suggestions!

📜 License

This project is licensed under the MIT License.