Breast Cancer Classifier Using K-Nearest Neighbors

Project Overview

This project aims to develop a machine learning model that can classify whether a tumor is benign or malignant based on features extracted from breast cancer cell nuclei. The dataset used for this project is the Breast Cancer Wisconsin (Diagnostic) Dataset from the sklearn.datasets library. The classification is achieved using the K-Nearest Neighbors (KNN) algorithm.

Dataset

The Breast Cancer Wisconsin (Diagnostic) Dataset contains the following information:

• Number of Instances: 569
• Number of Features: 30 (plus a target variable)
• Feature Information:
  • Mean radius
  • Mean texture
  • Mean perimeter
  • Mean area
  • Mean smoothness
  • And others related to the cell nuclei's properties
• Target Variable:
  • 0: Malignant
  • 1: Benign

Project Steps

1. Load Dataset: The dataset is loaded using sklearn.datasets.load_breast_cancer().
2. Data Preprocessing: The dataset is split into training and test sets using train_test_split.
3. Model Building: A K-Nearest Neighbors (KNN) model is built using the KNeighborsClassifier from sklearn.neighbors.
4. Model Evaluation: The model's performance is evaluated using accuracy scores and visualized using matplotlib plots.

Installation and Requirements

To run this project, you'll need to install the following Python packages:

• scikit-learn
• matplotlib
• pandas (optional for additional data handling)

You can install them using pip:

pip install scikit-learn matplotlib pandas

Running the Project

1. Clone the repository or download the notebook file.
2. Ensure the necessary libraries are installed.
3. Open the notebook file (Breast_Cancer_Classifier.ipynb) and run the cells step by step.

Conclusion

This project demonstrates a simple yet effective approach to classifying breast cancer using the K-Nearest Neighbors algorithm. The model provides insights into how machine learning techniques can aid in medical diagnosis tasks.