c24cfd2 Sep 8, 2018
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### 步骤1 | 数据预处理

#### 导入库

```import numpy as np
import matplotlib.pyplot as plt
import pandas as pd```

#### 导入数据集

```dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:,4].values```

#### 将数据集分成训练集和测试集

```from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)```

#### 特征缩放

```from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)```

### 步骤2 | 逻辑回归模型

#### 将逻辑回归应用于训练集

```from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)```

### 步骤3 | 预测

#### 预测测试集结果

`y_pred = classifier.predict(X_test)`

### 步骤4 | 评估预测

#### 生成混淆矩阵

```from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)```

#### 可视化

```from matplotlib.colors import ListedColormap
X_set,y_set=X_train,y_train
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)

plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

X_set,y_set=X_test,y_test
X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01),
np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01))

plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(),X1.max())
plt.ylim(X2.min(),X2.max())
for i,j in enumerate(np. unique(y_set)):
plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1],
c = ListedColormap(('red', 'green'))(i), label=j)

plt. title(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()```

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