Breast Cancer Prediction Project

This project focuses on predicting breast cancer diagnosis using the Wisconsin Breast Cancer Diagnostic dataset. The project includes data analysis, visualization, preprocessing, and predictive modeling.

Project Structure

breast-cancer-prediction/
├── config/                 # Hydra configuration files
├── data/                   # Data storage
├── notebooks/             # Jupyter notebooks for analysis
├── src/                   # Source code
│   ├── data/             # Data loading and preprocessing
│   ├── features/         # Feature engineering
│   ├── models/           # Model training and evaluation
│   └── visualization/    # Visualization utilities
└── results/              # Model results and artifacts

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Run the project:

python src/main.py

Components

• Data Loading: Fetches the Wisconsin Breast Cancer dataset using UCI ML Repository
• Data Preprocessing: Handles missing values, feature scaling, and feature selection
• Visualization: Creates various plots for data analysis
• Modeling: Implements and evaluates different ML models
• Configuration: Uses Hydra for configuration management

Features

The dataset includes 30 features related to breast cancer diagnosis:

• radius, texture, perimeter, area
• smoothness, compactness, concavity
• concave points, symmetry, fractal dimension
• (each measured for 3 different regions)

Target

The target variable is 'Diagnosis' indicating whether the tumor is malignant or benign.