《Python机器学习及实践》----良/恶性乳腺癌肿瘤预测
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本文博客参考了《Python机器学习及实践》一书中提供的实例代码，并对其中的所有代码都已经在本地进行了编译并成功运行。其中数据集来源于该书中指定的百度网盘资源。
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
df_train = pd.read_csv('D:\Source Code\machinelearn\ breast-cancer-train.csv')
df_test = pd.read_csv('D:\Source Code\machinelearn\ breast-cancer-test.csv')
df_test_negative = df_test.loc[df_test['Type'] == 0][['Clump Thickness','Cell Size']]
df_test_positive = df_test.loc[df_test['Type'] == 1][['Clump Thickness','Cell Size']]
plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')
plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
intercept = np.random.random([1])
coef = np.random.random([2])
lx = np.arange(0,12)
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx,ly,c='yellow')
plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')
plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
lr = LogisticRegression()
lr.fit(df_train[['Clump Thickness','Cell Size']][:10],df_train['Type'][:10])
print 'Testing accuracy (10 training samples):',lr.score(df_test[['Clump Thickness','Cell Size']],df_test['Type'])
intercept = lr.intercept_
coef = lr.coef_[0,:]
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx,ly,c='green')
plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')
plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
lr = LogisticRegression()
lr.fit(df_train[['Clump Thickness','Cell Size']],df_train['Type'])
print 'Testing accuracy (All training samples):',lr.score(df_test[['Clump Thickness','Cell Size']],df_test['Type'])
intercept = lr.intercept_
coef = lr.coef_[0,:]
ly = (-intercept - lx * coef[0]) / coef[1]
plt.plot(lx,ly,c='green')
plt.scatter(df_test_negative['Clump Thickness'],df_test_negative['Cell Size'],marker='o',s=200,c='red')
plt.scatter(df_test_positive['Clump Thickness'],df_test_positive['Cell Size'],marker='x',s=150,c='black')
plt.xlabel('Clump Thickness')
plt.ylabel('Cell Size')
plt.show()
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