🎓 Student Performance Predictor using Machine Learning

An end-to-end Machine Learning web application that predicts student performance (Pass/Fail) based on various academic and behavioral factors. This project covers everything from data preprocessing and model training to deployment with a modern Flask-based interface.

---

📌 Table of Contents

• Overview
• Demo
• Project Structure
• Features
• Tech Stack
• How to Run
• Model Performance
• Future Improvements
• Connect with Me

---

📖 Overview

This classification project predicts whether a student will pass based on key attributes like:

• Study hours per week

• Attendance rate (%)

• Previous academic grades

• Participation in extracurricular activities

• Parent education level

The project uses a Random Forest model trained on a labeled dataset and is integrated into a sleek Flask-based frontend for real-time predictions.

---

🎥 Demo

Watch the full walkthrough video on YouTube

Screenshots

---

📁 Project Structure

Student Performence/
├── app.py                              # Flask web application
├── train_model.py                      # Model training script
├── student_performance_model_optimized.joblib  # Optimized model (0.26 MB)
├── student_performance_prediction.csv   # Dataset
├── requirements.txt                    # Dependencies
├── Student_Performance_Predictor_Workflow.docx  # Documentation
└── .git/                              # Git repository

---

🚀 Features

• Predicts student performance (Pass/Fail) using Random Forest

• Handles categorical encoding and missing value imputation

• Clean and interactive frontend with progress tracking and form validation

• Real-time predictions via Flask web application

• Model and encoders saved using Joblib for efficient reuse

---

🧠 Tech Stack

• Languages: Python
• Libraries: pandas, numpy, scikit-learn, joblib, flask
• Frontend: HTML5, CSS3, JavaScript
• Model: Random Forest Classifier

---

⚙️ How to Run

1. Clone the repository

git clone https://github.com/yourusername/student-performance-predictor.git
cd student-performance-predictor

2. Install dependencies

pip install -r requirements.txt

3. Train the model

python train_model.py

4. Run the Flask app

python app.py

5. Open in your browser

http://127.0.0.1:5000/

---

📈 Model Performance

Metric	Score
Accuracy	~85%
Model Size	~0.2 MB
Algorithm	Random Forest Classifier

---

🔧 Future Improvements

• Deploy app on Render or Hugging Face Spaces

• Add charts to visualize student insights

• Expand dataset with more features (e.g., sleep, internet access)

• Add login/auth system for multi-user access

---

🤝 Connect with Me

• 💼 LinkedIn

• 🧠 YouTube

• 💻 Portfolio