Day11：K-NN案例

数据下载

data.PNG

这个数据集是某社交网络的用户信息，有Uesr ID、Gender、Age、EstimatedSalary。某汽车公司生产了新型豪华SUV，我们试图找出社交网络中的哪些用户会买这款新车。数据最后一列Purchased表示用户是否购买了这款车。我们希望通过Age和EstimatedSalary两个变量，建立一个模型，来预测用户是否会购买这款车。所以我们的特征矩阵只包含这两列，来研究Age、EstimatedSalary和是否购买之间的关系。

一、数据预处理

• 导入库

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

• 导入数据

df = pd.read_csv('D:\\data\\Social_Network_Ads.csv')
X = df.iloc[:,2:4]
Y = df.iloc[:,-1]

• 分割数据集

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25)

• 数据标准化

from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss = ss.fit(X_train)
X_train = ss.transform(X_train)
X_test = ss.transform(X_test)

二、训练K-NN模型

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2 )
knn.fit(X_train, Y_train)

三、预测测试集结果

Y_pred = knn.predict(X_test)

四、效果评估

• 混淆矩阵

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, Y_pred)

array([[60, 6],
[ 6, 28]], dtype=int64)

• 可视化
  训练集可视化

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, knn.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.5, 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('KNN (Training set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()

测试集可视化

from matplotlib.colors import ListedColormap
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, knn.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.5, 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('K-NN (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()