Breast Cancer Classification

Coursework project focused on classical ML pipelines, CV tuning, and model complexity tradeoffs.

Developed a full scikit-learn pipeline to classify breast cancer cases with 94.2% test accuracy. Tuned `k` using cross-validation and visualized error curves for model complexity.

Key Outcomes

94.2% test accuracy

Cross-validated hyperparameter tuning

Interpretable model complexity analysis

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Context

Problem and Context

Developed a full scikit-learn pipeline to classify breast cancer cases with 94.2% test accuracy. Tuned `k` using cross-validation and visualized error curves for model complexity.

Approach

Approach and Architecture

Scikit-learn pipeline for breast cancer classification with tuned KNN.

Implementation

Implementation Details

KNN

scikit-learn

Classification

Pipelines

Data Preprocessing

Results

Results and Tradeoffs

This project is presented as a concise technical overview rather than a full-length narrative case study.

94.2% test accuracy

Cross-validated hyperparameter tuning

Interpretable model complexity analysis

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