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🎓 Student Performance Predictor (GPA)

Streamlit App Model Domain

🚀 Live Demo

The application is deployed and active! Click the button below to test it:

👉 Launch Student GPA Predictor

📋 Project Overview

Academic performance is influenced by a complex mix of environmental, behavioral, and demographic factors. This project utilizes Machine Learning to predict a student's GPA based on habits such as weekly study time, absences, parental support, and extracurricular activities.

The goal is to provide a predictive tool for Educators and Parents to identify "at-risk" students early and intervene before grades suffer.

📊 Key Insights (EDA)

Exploratory analysis of the dataset revealed distinct patterns affecting student success:

  • The Attendance Factor: A strong negative correlation exists between Absences and GPA. Students with >15 absences rarely achieve a GPA above 2.5.
  • Study Habits: Students studying 10+ hours/week consistently maintain a GPA above 3.0.
  • Support Systems: Higher levels of Parental Support and participation in Extracurriculars correlate positively with academic stability.

🤖 The Model & Logic

We trained a Linear Regression model using Scikit-Learn to predict the continuous GPA variable (0.0 - 4.0).

Input Features

The model considers 12 key factors:

  1. Academic: Study Time Weekly, Absences, Tutoring.
  2. Support: Parental Support, Parental Education Level.
  3. Activities: Sports, Music, Volunteering, Extracurriculars.
  4. Demographics: Age, Gender, Ethnicity.

🛠️ Tech Stack

  • Python: Core programming.
  • Streamlit: Cloud-based frontend deployment.
  • Scikit-Learn: Model training and Standardization (StandardScaler).
  • Pandas & Seaborn: Data manipulation and EDA visualization.