Breast Cancer Classification

A machine learning project for classifying breast tumors as malignant or benign using the Breast Cancer Wisconsin dataset available in Scikit-learn.

📌 Project Overview

This project demonstrates a complete machine learning workflow, including:

• Loading the Breast Cancer dataset
• Creating a Pandas DataFrame for data exploration
• Exploratory Data Analysis (EDA)
• Data visualization using scatter plots
• Correlation analysis with a heatmap
• Feature selection based on correlation
• Model training and evaluation

📂 Dataset

The project uses the Breast Cancer Wisconsin Diagnostic Dataset provided by Scikit-learn through the load_breast_cancer() function.

Dataset characteristics:

• 569 samples
• 30 numerical features
• Binary classification
  • Malignant
  • Benign

🛠️ Technologies Used

• Python
• Scikit-learn
• NumPy
• Pandas
• Matplotlib
• Seaborn

📦 Installation

Clone the repository:

git clone https://github.com/python-is-life2022/breast-cancer-classification.git
cd breast-cancer-classification

Install the required packages:

pip install -r requirements.txt

▶️ Run the Project

Open the Jupyter Notebook or Google Colab notebook and run the cells in order.

📊 Project Workflow

1. Load the dataset
2. Explore the dataset
3. Visualize the data
4. Analyze feature correlations
5. Select important features
6. Train the machine learning model
7. Evaluate the model

📁 Project Structure

breast-cancer-classification/
│
├── breast_cancer_classification.ipynb
├── requirements.txt
├── README.md

📜 License

This project is intended for educational purposes.