🎓 Student Performance Predictor

A Machine Learning model that predicts whether a student will pass or fail based on study habits, attendance and previous scores — built with Python, Scikit-Learn and Seaborn.

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📌 About this project

Every year thousands of students struggle academically without early warning. This project builds a Machine Learning model that can predict a student's result early — giving teachers and institutions a chance to help before it is too late.

I built this project to apply Logistic Regression to a real education problem, inspired by my own experience as a Computer Science student.

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🎯 Problem Statement

Given:
  → Daily study hours
  → Attendance percentage
  → Previous exam score

Predict:
  → Will the student PASS or FAIL?

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🗂️ Dataset

I created a realistic dataset of 30 students with the following features:

Feature	Type	Description
study_hours	int	Hours studied per day
attendance_pct	int	Attendance percentage (0–100)
prev_score	int	Previous exam score (0–100)
passed	int	1 = Passed, 0 = Failed

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🔄 Project Workflow

Create Dataset
      ↓
Explore and Understand Data
      ↓
Visualize with Charts
      ↓
Split Train and Test (80/20)
      ↓
Train Logistic Regression Model
      ↓
Evaluate with Accuracy and Confusion Matrix
      ↓
Predict New Student Results

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📊 Model Results

Metric	Result
Algorithm	Logistic Regression
Accuracy	~100% on test set
Train/Test Split	80% / 20%

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🖼️ Visualizations included

• 📉 Study Hours vs Previous Score — colored by Pass/Fail
• 📊 Attendance Distribution — Pass vs Fail comparison
• 📈 Pass vs Fail bar chart
• 🔥 Confusion Matrix heatmap

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💡 Sample predictions

# Student who studies well
predict_student(study_hours=6, attendance=85, prev_score=70)
# Pass Probability : 95.3%
# Result : ✅ PASS

# Student who does not study
predict_student(study_hours=1, attendance=45, prev_score=35)
# Pass Probability : 4.1%
# Result : ❌ FAIL

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🛠️ Tech stack

Tool	Purpose
Python	Programming language
NumPy	Numerical computation
Pandas	Data handling
Matplotlib	Charts
Seaborn	Heatmap visualization
Scikit-Learn	ML model training and evaluation
Jupyter Notebook	Development environment

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🚀 How to run

On Google Colab — no installation needed

1. Open the .ipynb file in this repository
2. Click Open in Colab
3. Click Runtime → Run all

On local machine

pip install numpy pandas matplotlib seaborn scikit-learn jupyter
jupyter notebook Student_Performance_Predictor.ipynb

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📁 Repository structure

Student-Performance-Predictor/
├── Student_Performance_Predictor.ipynb   # main notebook
└── README.md                             # project documentation

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🔮 What I plan to add next

• Use a real dataset from Kaggle with 1000+ students
• Add more features — sleep hours, part time job, internet access
• Compare with Decision Tree and Random Forest models
• Build a simple web app using Flask or Streamlit
• Send early warning alert to teacher if student is at risk

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🧠 What I learned

This project taught me how Logistic Regression works for classification problems. Unlike Linear Regression which predicts numbers, Logistic Regression predicts categories — pass or fail, yes or no. I also learned how to read a Confusion Matrix and understand precision and recall, which are important for evaluating real world ML models.

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🔗 Related repositories

• 📂 ML-Practice — ML learning and practice notebooks
• 📂 House-Price-Prediction — Linear Regression project
• 📂 Python-Documentation — 30-class Python training course

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🎓 Rayhan Uddin · Computer Science Student · Bangladesh

"Education data is powerful. If we can predict who needs help, we can help them before it is too late."