Unveiling Precision in Breast Cancer Prediction with Random Forest and Decision Trees

Among women worldwide, breast cancer is the most typically occurring and fatal cancer with many various types, risk factors, and therapy challenges. Early identification is essential for both therapy outcomes and survival rates to be raised. This research study employs Random Forest and Decision Tree approaches to increase the detection accuracy of breast cancer by means of the Breast Cancer Wisconsin data. These methods investigate thirty features taken from digital images of tiny needle aspirates in order to obtain minute cell nuclei properties. After data collecting, analysis, visualization, and model deployment follows hyperparameter tuning via GridsearchCV. Although the Random Forest classifier had remarkable accuracy of $93 \%$, indicating resilience in managing complex data, the Decision Tree classifier resulted in $91 \%$ accuracy. These results show how well machine learning techniques might be applied to improve the diagnosis of breast cancer, therefore providing doctors more precise tools for early identification and better patient treatment.