Problem Description. You are given a set of $569$ instances, each with information about a patient$$s

cell, that is or is not cancerous. The information includes the patient$$s ID $($which should not be relevant

to his$/$her diagnosis$)$ and a diagnosis $($malignant or benign$),$ as well as a set of $30$ attributes that are

usually used for diagnosis. You are asked to design, implement, and train a machine learning model,

and assess its quality in terms of $($a$)$ accuracy of diagnosis $($of instances not used for training$)$ and $($b$)$

computational efficiency and scalability of your solution. Your program shall accept data from a file of

instances. It is up to you to correctly use these instances $($or a large enough subset of them$)$ to train

and test $($validate$)$ one machine learning $($ML$)$ algorithm of your choice $($such as an EA or kNN or a

Decision Tree$),$ one that you think is appropriate for the problem at hand, one that is distinguished by:

$(1)$ Instances with features that include rational $($numerical$)$ values, and a target variable whose

value is M or B;

$(2)$ A large search space due to the large number of features;

$(3)$ A complex relationship between the values of the features and the value of the target variable.

Description of Ideal Solution. Use N $=40,140,240,340,440,$ the ratio N$/$T must be maintained at

$4/1.$ An instance used for training, in a given train$-$and$-$test run, cannot be used for testing. You must

retrain the model prior to each test. Further, the $($N$+$T$)$ instances must be chosen at random from the

complete set. Though this is not the case in medical practice$*,$ implement accuracy as the percentage

of test instances $($T$)$ that are correctly predicted by your trained model $($i$.$e$.,$ predicted diagnosis $=$

actual diagnosis$).$ For running time, just use actual execution time $($of testing, not training$),$ while

running your program on the same computer, under identical conditions