Breast-Cancer-Classification-Using-Machine-Learning

We will be going through the steps of building a model to predict whether a tumor is benign or malignant. This involves importing tools like NumPy, Pandas, and scikit-learn which are commonly used for data manipulation and machine learning tasks. Used a dataset from scikit-learn, which likely contains features (like cell characteristics) used to identify cancer. Used a logistic regression model, a common technique for binary classification problems (benign or malignant in this case). Split the data set into two sets: training data and testing data. The training data is used to train the machine learning model, and the testing data is used to evaluate the performance of the model. Used accuracy score to evaluate the model's performance. This metric indicates how many predictions the model makes correctly. After everything is set up, the model is trained on the provided data. Once trained, the model can be used to predict if a new, unseen tumor is benign or malignant based on its features.