Breast Cancer Predictive Model

This machine learning project focused on developing predictive models for breast cancer detection using the Wisconsin Breast Cancer Dataset. Working in a team, we implemented multiple ML algorithms including logistic regression, decision trees, and ensemble methods.

Through careful feature engineering, hyperparameter tuning, and model ensemble techniques, we achieved 89% accuracy in predicting malignant vs benign tumors. The project emphasized the importance of medical AI applications and ethical considerations in healthcare technology.

The comprehensive analysis included data preprocessing, exploratory data analysis with visualizations, and rigorous model evaluation using cross-validation techniques.

• Achieved 89% prediction accuracy using ensemble ML techniques
• Implemented multiple algorithms: logistic regression, decision trees, SVM
• Performed extensive feature engineering and selection
• Created data visualizations using Matplotlib and Seaborn
• Conducted cross-validation and hyperparameter optimization
• Collaborated in team environment using Git version control