Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in the medical field, where those methods are widely used in diagnosis and analysis to make decisions.
Given the details of cell nuclei taken from breast mass, predict whether or not a patient has breast cancer using the Ensembling Techniques. Perform necessary exploratory data analysis before building the model and evaluate the model based on performance metrics other than model accuracy.
The dataset consists of several predictor variables and one target variable, Diagnosis. The target variable has values 'Benign' and 'Malignant', where 'Benign' means that the cells are not harmful or there is no cancer and 'Malignant' means that the patient has cancer and the cells have a harmful effect.
● Analysing the available data and exploring relationships among given variables ● Data Pre-processing ● Training SVM classifier to predict whether the patient has cancer or not ● Assess the correctness in classifying data with respect to efficiency and effectiveness of the SVM classifier in terms of accuracy, precision, sensitivity, specificity and AUC ROC ● Tuning the hyperparameters of SVM Classifier provided by the scikit-learn library
The students will get a better understanding of how the variables are linked to each other and build an SVM model. Apart from various performance measures, they will also learn about hyperparameter tuning with cross-validation to improve these scores.