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svm.py
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svm.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jan 7 02:33:12 2019
@author: hp
"""
import numpy as np
from sklearn import preprocessing, cross_validation, neighbors, svm
import pandas as pd
df = pd.read_csv('breast-cancer-wisconsin.txt')
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)
#print( df.head() )
X = np.array( df.drop(['class'],1) )
y = np.array( df['class'] )
X_train , X_test, y_train, y_test = cross_validation.train_test_split(X,y, test_size=0.2 )
clf = svm.SVC(n_jobs =-1)
clf.fit( X_train , y_train )
accuracy = clf.score(X_test, y_test)
#print( accuracy )
example_measures = np.array([2,7,10,10,7,10,4,9,4])
example_measures = example_measures.reshape(len( example_measures ),-1)
predection = clf.predict(example_measures)
print( predection )