mickey
# encoding=utf-8
from sklearn import svm
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# 加载数据集,你需要把数据放到目录中
data = pd.read_csv("./data.csv")
# 数据探索
# 因为数据集中列比较多,我们需要把dataframe中的列全部显示出来
pd.set_option('display.max_columns', None)
#print(data.columns)
#print(data.head(5))
#print(data.describe())
# 将特征字段分成3组
features_mean= list(data.columns[2:12])
features_se= list(data.columns[12:22])
features_worst=list(data.columns[22:32])
# 数据清洗
# ID列没有用,删除该列
data.drop("id",axis=1,inplace=True)
# 将B良性替换为0,M恶性替换为1
data['diagnosis']=data['diagnosis'].map({'M': 1, 'B': 0})
# 特征选择
features_remain = ['radius_mean', 'texture_mean', 'perimeter_mean',
'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
'fractal_dimension_se', 'radius_worst', 'texture_worst',
'perimeter_worst', 'area_worst', 'smoothness_worst',
'compactness_worst', 'concavity_worst', 'concave points_worst',
'symmetry_worst', 'fractal_dimension_worst']
# 抽取30%的数据作为测试集,其余作为训练集
train, test = train_test_split(data, test_size = 0.3)# in this our main data is splitted into train and test
# 抽取特征选择的数值作为训练和测试数据
train_X = train[features_remain]
train_y=train['diagnosis']
test_X= test[features_remain]
test_y =test['diagnosis']
# 采用Z-Score规范化数据,保证每个特征维度的数据均值为0,方差为1
ss = StandardScaler()
train_X = ss.fit_transform(train_X)
test_X = ss.transform(test_X)
# 创建SVM分类器
model = svm.LinearSVC()
# 用训练集做训练
model.fit(train_X,train_y)
# 用测试集做预测
prediction=model.predict(test_X)
print('准确率: ', metrics.accuracy_score(prediction,test_y))
准确率: 0.9707602339181286