🧠 Single Layer Neural Network for Binary Classification

This repository contains a Jupyter Notebook implementation of a single-layer neural network (perceptron) built entirely from scratch using NumPy.
It demonstrates how a neuron can learn to classify binary data through forward propagation, cost computation, and gradient descent optimization.

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🚀 Features

• Pure NumPy implementation — no TensorFlow / PyTorch / Scikit-learn
• Step-by-step explanation of:
  • Forward propagation
  • Cost function (Binary Cross-Entropy)
  • Gradient descent optimization
• Visualizations of cost reduction over iterations
• Works for any binary classification dataset

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🧩 File Structure

File	Description
Single_layer_neuron_model_for_binary_classification.ipynb	Main notebook implementing the binary classification model step-by-step

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