A comprehensive machine learning project that aims to detect lung cancer using patient diagnostic data through various ML models (Logistic Regression, Random Forest, and XGBoost). The project covers t
he complete ML pipeline from data preparation to model evaluation and suggests practical deployment in healthcare settings.
Reasons to Read -- Learn:
how to build a complete end-to-end machine learning pipeline for healthcare applications, from data preprocessing to model deployment
implementing and comparing multiple machine learning models (Logistic Regression, Random Forest, XGBoost) for binary classification in a real-world medical context
practical data preprocessing techniques including handling categorical variables, outlier detection using z-score, and feature engineering in a healthcare dataset
publisher: @bhatadithya54764118
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