📚 Exam Pass & Fail Classifier with PyTorch

📖 Overview

This project predicts student exam outcomes (Pass/Fail) using a simple neural network built with PyTorch.
It demonstrates a full machine learning pipeline from data loading to inference, including:

• 🧠 Neural Network with multiple hidden layers using ReLU activation function
• ⚖️ Binary Cross-Entropy (BCE) Loss for training
• 🚀 Adam optimizer for gradient updates
• 🔀 Mini-batch training with DataLoader
• 📊 Train/Validation/Test split for robust evaluation
• 📈 Live training & validation loss monitoring
• ✅ Sigmoid activation on the output to produce probabilities, with a threshold for Pass/Fail decision

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🧩 Libraries

• PyTorch – model, training, and inference
• pandas – data handling
• matplotlib – loss visualization
• pickle – saving/loading normalization params and trained model

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⚙️ Requirements

• Python 3.13+
• Recommended editor: VS Code

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📦 Installation

• Clone the repository

git clone https://github.com/hurkanugur/Exam-Pass-Fail-Classifier.git

• Navigate to the Exam-Pass-Fail-Classifier directory

cd Car_Price_Predictor

• Install dependencies

pip install -r requirements.txt

• Navigate to the Exam-Pass-Fail-Classifier/src directory

cd src

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🔧 Setup Python Environment in VS Code

1. View → Command Palette → Python: Create Environment
2. Choose Venv and your Python version
3. Select requirements.txt to install dependencies
4. Click OK

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📂 Project Structure

data/
└── student_exam_data.csv        # Raw dataset

model/
└── exam_classifier.pth          # Trained model (after training)

src/
├── config.py                    # Paths, hyperparameters, split ratios
├── dataset.py                   # Data loading & preprocessing
├── main_train.py                # Training & model saving
├── main_inference.py            # Inference pipeline
├── model.py                     # Neural network definition
├── visualize.py                 # Training/validation plots

requirements.txt                 # Python dependencies

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📂 Model Architecture

Input → Linear(64) → ReLU
      → Linear(32) → ReLU
      → Linear(1)  → Sigmoid(Output)

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📂 Train the Model

python main_train.py

or

python3 main_train.py

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📂 Run Predictions on Real Data

python main_inference.py

or

python3 main_inference.py