import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
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/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88return f(*args, **kwds)
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data = pd.read_csv("credit-a.csv", header=None)
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data.head(2)
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from sklearn.model_selection import train_test_split
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x = data[data.columns[:-1]]
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y = data[15].replace(-1, 0)
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x_train, x_test, y_train, y_test = train_test_split(x, y)
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from sklearn import preprocessing
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scaler = preprocessing.StandardScaler().fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
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from sklearn.svm import SVC
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/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88return f(*args, **kwds)
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model = SVC(kernel='poly', degree=3, C=5)
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model.fit(x_train, y_train)
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SVC(C=5, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False)
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model.score(x_test, y_test)
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0.8597560975609756
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model2 = SVC(kernel='rbf', gamma=0.5, C=5)
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model2.fit(x_train, y_train)
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SVC(C=5, cache_size=200, class_weight=None, coef0=0.0,decision_function_shape='ovr', degree=3, gamma=0.5, kernel='rbf',max_iter=-1, probability=False, random_state=None, shrinking=True,tol=0.001, verbose=False)
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model2.score(x_test, y_test)
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0.8170731707317073
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