Student-Performance-Prediction

🧠 Student Performance Prediction using Machine Learning

This project predicts students' math scores based on various features such as gender, parental education, lunch type, and test preparation course.

🔧 Tools Used

• Python 🐍
• pandas, numpy
• seaborn, matplotlib
• scikit-learn

📁 Dataset

• Source: Students Performance Dataset (UCI)

📊 Features

• gender
• race/ethnicity
• parental level of education
• lunch
• test preparation course
• reading score
• writing score

🎯 Target

• math score (Regression)

📈 Results

• Root Mean Squared Error (RMSE): ~9–10
• R² Score: ~0.75–0.80

📌 Steps

1. Data Cleaning and Encoding
2. Exploratory Data Analysis (EDA)
3. Linear Regression Modeling
4. Evaluation and Visualization

📷 Sample Output

• Scatter plot: Actual vs Predicted scores
• Heatmap: Feature Correlation
• Feature Importance list

🚀 How to Run

pip install pandas numpy seaborn matplotlib scikit-learn
python student_performance_prediction.py