Cancer remains a significant global health challenge, underscoring the urgent need for personalized therapeutic solutions. One promising avenue in cancer research involves leveraging large-scale genomic data for predictive purposes. This project seeks to develop robust machine learning models for predicting cancer outcomes using the UCI cancer dataset. By amalgamating genomic information with clinical data, our models aim to uncover subtle yet meaningful patterns and associations. Ultimately, these insights will facilitate precise categorization of cancer subtypes and enable tailored treatment recommendations.
Machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines, Random Forest, and Logistic Regression will be employed to analyze the dataset. The project's motivation lies in exploring various algorithms and models, training them on gene expression data, and classifying the presence of cancer types.