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import csv

import numpy

import scipy

import matplotlib.pyplot as plt

from sklearn import preprocessing

from sklearn.cross_validation import train_test_split

from sklearn import metrics

from sklearn import neighbors

import knnplots

from sklearn.naive_bayes import GaussianNB

from sklearn import cross_validation

from sklearn.grid_search import GridSearchCV

#Code common to all modeles from module 3 onwards

##NB. The X and yTransformed variables come from the preprocessing in the previous module.

fileName = "wdbc.csv"

fileOpen = open(fileName, "rU")

csvData = csv.reader(fileOpen)

dataList = list(csvData)

dataArray = numpy.array(dataList)

X = dataArray[:,2:32].astype(float)

y = dataArray[:, 1]

le = preprocessing.LabelEncoder()

le.fit(y)

yTransformed = le.transform(y)

XTrain, XTest, yTrain, yTest = train_test_split(X, yTransformed)

knnK3 = neighbors.KNeighborsClassifier(n_neighbors = 3)

knnK15 = neighbors.KNeighborsClassifier(n_neighbors = 15)

nbmodel = GaussianNB()