write in python

The dataset bdiag.csv, included several imaging details from patients that had a biopsy to test for breast cancer. The variable diagnosis classifies the biopsied tissue as M = malignant or B = benign. a. Use a KNN with k = 5 to predict Diagnosis using texture_mean and radius_mean. b. Build the confusion matrix for the classification above c. Plot the scatter plot for texture_mean and radius_mean and draw the border line for the prediction of Diagnosis based on the model in a) d. Plot the scatter plot for texture_mean and radius_mean and draw the border line for the prediction of Diagnosis based knn, k=15 The dataset bdiag.csv, included several imaging details from patients that had a biopsy to test for breast cancer. The variable diagnosis classifies the biopsied tissue as M = malignant or B = benign. a. Use a KNN with k = 5 to predict Diagnosis using texture_mean and radius_mean. b. Build the confusion matrix for the classification above c. Plot the scatter plot for texture_mean and radius_mean and draw the border line for the prediction of Diagnosis based on the model in a) d. Plot the scatter plot for texture_mean and radius_mean and draw the border line for the prediction of Diagnosis based knn, k=15