add new

Author: zhouwei713

.idea/data_combat.iml

@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+# 乳腺癌诊断分类
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+from sklearn import metrics
+from sklearn.preprocessing import StandardScaler
+
+# 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})
+
+# 将肿瘤诊断结果可视化
+sns.countplot(data['diagnosis'],label="Count")
+plt.show()
+# 用热力图呈现features_mean字段之间的相关性
+corr = data[features_mean].corr()
+plt.figure(figsize=(14,14))
+# annot=True显示每个方格的数据
+sns.heatmap(corr, annot=True)
+plt.show()
+
+
+# 特征选择
+#features_remain = ['radius_mean','texture_mean', 'smoothness_mean','compactness_mean','symmetry_mean', 'fractal_dimension_mean'] 
+features_remain = data.columns[1:31]
+print(features_remain)
+print('-'*100)
+# 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))