/AIDI2004

Lab 2

Primary LanguageJupyter Notebook

Breast Cancer Detection Models

Introduction

This repository contains code for training and evaluating classification models for breast cancer detection tasks.

Usage

Prerequisites

  • Python 3
  • Jupyter Notebook
  • scikit-learn
  • Matplotlib (for visualization, if needed)

Installation

You can install the required packages using pip:

pip install scikit-learn numpy matplotlib jupyter

Regression Models

  1. Clone this repository:
git clone https://github.com/nikithamarythomas/AIDI2004.git

Main branch : Logistic Regression
Branch 'branch' : SVM

Launch the notebook in your preferred IDE to use the models.

Dataset

Both models are trained and evaluated on the Breast Cancer Wisconsin (Diagnostic) Dataset, available in scikit-learn's datasets module. The goal to predict whether the mass is benign or malignant.

NOTE

When working with Jupyter Notebook files (.ipynb), you can use nbdiff to visualize Git diffs more effectively.
nbdiff is a tool that provides a rich visualization of differences between Jupyter Notebooks. You can install it via pip:

pip install nbdime

Integrate nbdiff with git:

nbdime config-git --enable

After configuration, you can use git diff as usual, and nbdime diff will provide a human-readable diff: