Скачать или смотреть Building a Complete Machine Learning Pipeline for Breast Cancer Diagnosis | End-to-End Project

From Data to Diagnosis: A Deep Dive into ML for Healthcare

In this end-to-end project walkthrough, I build a interpretable Machine Learning system designed to classify breast tumors as malignant or benign using the classic Wisconsin Breast Cancer Dataset. This isn't just a model: it's a full, production-ready pipeline following industry best practices and the ML project checklist.

What's Inside:

00:00 - Introduction: The problem in a clinical context and project goals.

01:45 - Project Architecture & Code Structure: Overview of the OOP design and modular pipeline.

04:30 - Data Acquisition & Preprocessing: Automated data loading, validation splitting, and ensuring no data leakage.

08:15 - Exploratory Data Analysis (EDA): Advanced visualizations to understand feature distributions, correlations, and class balance.

14:50 - Feature Engineering & Scaling: Creating meaningful new features and preparing data for algorithms.

19:05 - Model Training & Selection: Comparing 7+ algorithms using cross-validation.

24:40 - Hyperparameter Tuning: Using RandomizedSearchCV to optimize the top-performing models.

28:20 - Ensemble Learning: Combining models into a powerful Voting Classifier.

30:45 - Model Evaluation & Interpretation: Analyzing results with confusion matrices, ROC curves, and feature importance.

32:10 - Deployment Readiness: Discussing serialization, APIs, and monitoring for a real-world clinical setting.

33:10 - Conclusion & Key Lessons Learned: Recap of the project and perspectives.

Results:
The final ensemble model achieved outstanding performance, crucial for a medical application:

Accuracy: 99.12%

Recall: 98.61% (Minimizing false negatives is critical!)

ROC AUC: 99.57%

Code & Tech Stack:

Language: Python

Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

Concepts: Cross-Validation, Hyperparameter Tuning, Ensemble Methods, Model Interpretation

This project is a template for building trustworthy, explainable, and effective ML systems, especially in high-stakes fields like healthcare.

Kaggle Notebook [Code]: [https://www.kaggle.com/code/rafaeldom...]

Dataset Reference [Article]: [https://www.semanticscholar.org/paper...]

Let's connect and discuss!
LinkedIn: [  / rafaelpassosdomingues  ]