🧠 Breast Cancer Classifier - Machine Learning with k-NN

This project applies a k-Nearest Neighbors (k-NN) classifier to the Breast Cancer Wisconsin Diagnostic Dataset to predict whether a tumor is malignant or benign.

Built as part of "Machine Learning" assignment, this project focuses on:

• Data preprocessing and understanding
• Model training with KNeighborsClassifier
• Accuracy evaluation
• Hyperparameter tuning (k value)
• Visualization of training vs. test accuracy

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📊 Accuracy Plot

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🚀 How it Works

• Input: 30 real-valued features (e.g., mean radius, texture, perimeter, etc.)
• Output: Binary classification (0 = malignant, 1 = benign)

We split the dataset:

• 75% → Training
• 25% → Testing

Used Scikit-learn’s KNeighborsClassifier and evaluated model performance with .score().

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📁 Project Folder Structure

Breast-Cancer-Classifier/
├── Knn_Breast_Cancer_Classifier.ipynb     ✅ Python completed notebook
├── breast_cancer_data.csv                 ✅ Dataset in CSV format
├── knn_accuracy_plot.png                  ✅ Accuracy vs. k plot
├── README.md                              ✅ Project summary file
└── LICENSE                                ✅ MIT open source license

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📁 Files

• Knn_Breast_Cancer_Classifier.ipynb: Full notebook with clean, commented code
• breast_cancer_data.csv: Dataset in CSV format
• knn_accuracy_plot.png: Accuracy vs. k plot
• README.md: This file

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🛠 Tech Stack

• Python 3.9
• Jupyter Notebook
• Scikit-learn
• Matplotlib
• Pandas

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✅ Result

Achieved ~93% test accuracy using k-NN with k=8.
The plot reveals how different k values affect model performance and generalization.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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📬 Connect

• 🔗 LinkedIn
• 📧 mukeshthenraj@gmail.com

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👨‍💻 Author Mukesh Thenraj