Breast Cancer Prediction with SVM & KNN

Project Overview

This project applies supervised machine learning techniques to predict breast cancer tumors as benign or malignant. We use Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) to classify tumors based on key cellular features. The project also compares model performance and identifies the most influential features.

Team Members

• José Pablo Del Moral
• Pol Tordera
• Ferran Serramalera
• Bruno Pin
• Oscar Grau

Dataset

The dataset contains measurements of cell nuclei from breast cancer biopsies, including features like radius, texture, perimeter, area, smoothness, compactness, concavity, symmetry, and fractal dimension.

• Total entries: 569
• Target: diagnosis (0 = benign, 1 = malignant)

Methodology

1. Data Cleaning & Preprocessing: Removed irrelevant columns and encoded target labels.
2. Exploratory Data Analysis (EDA): Visualized distributions and correlations, identified top predictive features.
3. Modeling:
   • SVM: GridSearchCV to tune hyperparameters; achieved ~95.6% accuracy.
   • KNN: GridSearchCV to optimize neighbors and distance metrics; achieved ~96.5% accuracy.
4. Evaluation: Accuracy, precision, recall, f1-score, and confusion matrices used to compare models.

Key Findings

• Most important features: concave points_worst, perimeter_worst, concave points_mean.
• KNN slightly outperformed SVM in minimizing false negatives, making it preferable for this medical dataset.
• Features like symmetry and texture had minimal impact on predictions.

Conclusion

Both SVM and KNN provide high-accuracy predictions, but KNN is more effective for reducing false negatives. This demonstrates the practical use of supervised techniques in medical diagnostics.

How to Run

1. Clone this repository.
2. Load data.csv into the Jupyter Notebook Breast_Cancer_Supervised.ipynb.
3. Execute all cells to replicate preprocessing, modeling, and evaluation.

License

This project is for educational purposes.