🎓 Student Pass / Fail Prediction using Decision Tree

Python Developer • Machine Learning • Explainable AI • Streamlit

An interactive Streamlit web application that predicts whether a student will PASS or FAIL based on daily study hours using a Decision Tree classifier.

This project demonstrates an explainable machine learning workflow, combining real-time prediction, confidence scoring, and interpretable model visualization.

🚀 Live Demo

👉 https://huggingface.co/spaces/Siddhartha001/Student_Pass_Fail_Prediction

🧠 Model Overview

Algorithm: Decision Tree Classifier Objective: Binary Classification (Pass / Fail)

Key Characteristics

Interpretable decision logic

Controlled depth to prevent overfitting

Visual explanation using decision tree structure

Input Feature

Study hours per day

Output

PASS or FAIL prediction

Confidence score

Decision tree visualization

✨ Features

Interactive slider for selecting study hours

Real-time prediction using Decision Tree model

Prediction confidence percentage

Clean, uncluttered decision tree visualization

Lightweight explainable ML demo suitable for beginners and education

⚙️ Tech Stack

Python

Streamlit

NumPy

Scikit-learn

Matplotlib

📁 Project Structure

Student_Pass_Fail_Prediction/
├── app.py              # Streamlit application
├── requirements.txt    # Dependencies
└── README.md           # Documentation

▶️ Run Locally

pip install -r requirements.txt
streamlit run app.py

🎯 Learning Objectives

This project highlights:

Explainable Machine Learning concepts

Decision Tree model interpretability

Interactive ML visualization using Streamlit

Simple end-to-end deployment workflow

👤 Author

K. Siddhartha Python Developer | AI / Machine Learning Projects

🔗 GitHub: https://github.com/k-siddhartha-ai

🤗 Hugging Face: https://huggingface.co/Siddhartha001

⭐ Notes

This project uses a small synthetic dataset for educational demonstration purposes and focuses on model interpretability rather than production-scale prediction.