Student Performance Prediction

Overview

This project predicts student academic performance (pass/fail) based on multiple socio-academic factors such as study time, absences, and parental education.
It uses a Decision Tree Classifier trained on the UCI Student Performance Dataset.

Dataset

UCI Student Performance dataset:

• student-mat.csv → Mathematics students
• student-por.csv → Portuguese students

Model Params

Max Depth vs Accuracy

-> max_depth = 6

Feature Importance

-> We've Choosen only these features for deployment

• failures - number of past class failures (numeric: n if 1<=n<3, else 4)
• absences - number of school absences (numeric: from 0 to 93)
• goout - going out with friends (numeric: from 1 - very low to 5 - very high)
• freetime - free time after school (numeric: from 1 - very low to 5 - very high)
• internet - Internet access at home (binary: yes or no)
• schoolsup - extra educational support (binary: yes or no)
• paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
• Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
• age - student's age (numeric: from 15 to 22)
• famsize - family size (binary: "LE3" - less or equal to 3 or "GT3" - greater than 3)
• Mjob - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
• guardian - student's guardian (nominal: "mother", "father" or "other")
• health - current health status (numeric: from 1 - very bad to 5 - very good)
• traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
• Pstatus - parent's cohabitation status (binary: "T" - living together or "A" - apart)

How to run

Two ways to run the Streamlit app.

Option 1 - Local (pip + Streamlit)

1. Create and activate a virtual environment:
   • python3 -m venv .venv
   • source .venv/bin/activate
2. Install dependencies:
   • pip install -r requirements.txt
   • If streamlit is not in requirements.txt run pip install streamlit
3. Run the app (replace app.py with your script path if different, e.g. src/app.py):
   • streamlit run app.py --server.port 8501 --server.address 0.0.0.0

Option 2 - Docker

1. Build the image from the repository root:
   • docker build -t student-performance .
2. Run the container:
   • docker run --rm -p 8501:8501 student-performance
3. If your Streamlit script is not app.py, override the script path:
   • docker run --rm -p 8501:8501 -e STREAMLIT_APP=src/app.py student-performance

Troubleshooting:

• If Streamlit reports "File does not exist", inspect the repo files inside the project to find the correct entrypoint, e.g. ls or git ls-files | grep -E 'app|streamlit|main' and update the command or STREAMLIT_APP accordingly.