Classification

Overview

A comprehensive machine learning classification demonstration using multiple algorithms on the Breast Cancer Wisconsin dataset.

Prerequisites

• Python 3.9+
• Poetry

Installation

# Install Poetry (if not already installed)
pip install poetry

# Clone the repository
git clone https://github.com/yourusername/classification.git
cd classification

# Install dependencies
poetry install

Running the Demo

# Activate the virtual environment
poetry shell

# Run the main script
poetry run classification

Project Structure

• classification/: Source code
• tests/: Unit tests
• notebooks/: Jupyter notebooks for interactive exploration

Algorithms Demonstrated

• lightgbm

Learning Objectives

• Understanding classification techniques
• Model evaluation
• Performance visualization
• Feature importance analysis

## Jupyter Notebook (notebooks/demo_notebook.ipynb)
<antArtifact identifier="demo-notebook" type="application/vnd.ant.code" language="python" title="Jupyter Notebook Demo">
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Demo Notebook\n",
    "\n",
    "Interactive exploration of machine learning classification techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from classification_demo.classifier import ClassificationDemo\n",
    "\n",
    "# Create demo instance\n",
    "demo = ClassificationDemo()\n",
    "\n",
    "# Train and evaluate\n",
    "results = demo.train_and_evaluate()\n",
    "\n",
    "# Print and visualize results\n",
    "demo.visualize_results(results)\n",
    "demo.plot_feature_importance()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}