Deep Learning Week 1 - Student Pass/Fail Classification

This repository contains the implementation of a simple perceptron algorithm for classifying students as pass or fail based on their study hours and sleep hours.

📁 Project Structure

DL-Week1/
├── SampleDataGenerator.ipynb    # Data generation using sklearn
├── Week1_StudentPassFail.ipynb  # Main perceptron implementation
└── README.md                    # This file

🎯 Project Overview

This project demonstrates the implementation of a Perceptron algorithm, one of the fundamental building blocks of neural networks. The goal is to classify students as either "Pass" (1) or "Fail" (0) based on two features:

• Study Hours: Number of hours spent studying
• Sleep Hours: Number of hours of sleep

📊 Dataset

The training data consists of 6 students with the following characteristics:

Student	Study Hours	Sleep Hours	Outcome
1	1	3	Fail
2	2	2	Fail
3	3	6	Pass
4	4	3	Pass
5	5	2	Pass
6	1	6	Fail

🧠 Algorithm Implementation

Perceptron Algorithm

The perceptron is a simple binary classifier that learns a linear decision boundary. The algorithm:

1. Initialization: Random weights and bias
2. Training Loop:
   • Forward pass: z = w₁x₁ + w₂x₂ + b
   • Activation: output = 1 if z > 0 else 0
   • Error calculation: error = actual - predicted
   • Weight update: w = w + learning_rate * error * input
   • Bias update: b = b + learning_rate * error

Key Components

• Learning Rate: 0.1
• Training Epochs: 10
• Activation Function: Step function (binary threshold)

📈 Results

After training, the model learns:

• Weights: [1.18470524, -0.4348575]
• Bias: -0.7

The decision boundary is visualized showing how the perceptron separates pass and fail students based on their study and sleep patterns.

🛠️ Files Description

SampleDataGenerator.ipynb

• Demonstrates data generation using scikit-learn's make_blobs
• Creates synthetic 2D data with 2 clusters
• Useful for testing classification algorithms

Week1_StudentPassFail.ipynb

• Main implementation of the perceptron algorithm
• Complete training and visualization pipeline
• Includes:
  • Data preparation
  • Model training
  • Prediction function
  • Decision boundary visualization

🚀 Getting Started

Prerequisites

pip install numpy matplotlib scikit-learn

Running the Notebooks

1. Open Week1_StudentPassFail.ipynb in Jupyter Notebook or Google Colab
2. Run all cells to see the perceptron training process
3. The final cell will display the decision boundary visualization

📚 Learning Objectives

This project covers fundamental concepts in deep learning:

• Linear Classification: Understanding how linear boundaries separate data
• Gradient Descent: Weight updates based on prediction errors
• Activation Functions: Step function for binary classification
• Decision Boundaries: Visualizing how the model separates classes

🔍 Key Insights

The perceptron learns that:

• Students who study more tend to pass (positive weight for study hours)
• Students who sleep more might have mixed effects (negative weight for sleep hours)
• The model finds a linear boundary that best separates pass/fail students

🎓 Educational Value

This implementation serves as an excellent introduction to:

• Neural network fundamentals
• Supervised learning concepts
• Binary classification problems
• Linear separability
• Training algorithms

📝 Future Enhancements

Potential improvements for this project:

• Implement other activation functions (sigmoid, ReLU)
• Add more features (attendance, previous grades)
• Implement multi-layer perceptron
• Add regularization techniques
• Cross-validation for model evaluation

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Note: This is a simplified educational implementation. Real-world applications would require more sophisticated preprocessing, larger datasets, and advanced neural network architectures.