Breast Cancer Neural Network Classifier

Project Overview

This project implements a neural network from scratch using Python and NumPy to classify breast cancer data. The data is sourced from the well-known Breast Cancer Wisconsin (Diagnostic) dataset, which is available in the sklearn library.

Features

• Data Standardization: Standardizes the features to have zero mean and unit variance.
• Neural Network Implementation: Builds a simple feedforward neural network with two hidden layers.
• Activation Function: Uses the sigmoid activation function for non-linearity.
• Backpropagation: Implements the backpropagation algorithm to update the network weights and biases.
• Performance Evaluation: Evaluates the classifier on the test set to determine its accuracy.

Dataset

The dataset used is the Breast Cancer Wisconsin (Diagnostic) dataset, which includes measurements from digitized images of a fine needle aspirate (FNA) of a breast mass. It contains features for each cell nucleus, and the objective is to classify them as malignant or benign.

Neural Network Architecture

• Input Layer: Matches the number of features (30 features from the dataset).
• Hidden Layers: Two hidden layers, each with 128 neurons.
• Output Layer: One neuron, outputting the probability of the input being malignant.

Requirements

To run this project, you will need Python and the following Python libraries installed:

• NumPy
• scikit-learn

How to Run

1. Ensure you have Python and the necessary libraries installed.
2. Download the project files to your local machine.
3. Run the script with the following command:

   python ML.ipynb