Student Performance Prediction

This project uses machine learning to predict whether a student will pass or fail based on various factors such as attendance, study habits, and parental support. A Random Forest Classifier is used to perform the classification, and evaluation is done using confusion matrix, accuracy, precision, and recall.

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Dataset

The dataset includes features like:

• Absences
• Weekly Study Time
• Tutoring
• Parental Support
• Extracurricular Activities
• Sports, Music, Volunteering
• Parental Education
• Age
• GPA (used to derive the target label)

Target variable: Pass (1 = GPA ≥ 2.0, 0 = GPA < 2.0)

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Methodology

1. Data Preprocessing

   • Created a binary target column (Pass) based on GPA.
   • Selected relevant features for prediction.
   • Split data into training (80%) and testing (20%) sets.

2. Model

   • Used a Random Forest Classifier from scikit-learn.
   • Evaluated using confusion matrix, accuracy, precision, and recall.

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How to Run

1. Upload the dataset
2. Install required libraries:

pip install pandas seaborn scikit-learn matplotlib