【11%】100小时机器学习——KNN实验

总目录链接

前言

本节进行knn算法的实验部分。
前情回顾： K近邻法（K-NN，k-NearestNeighbor）

Step 0：数据准备

Social_Networt_Ads

User ID,Gender,Age,EstimatedSalary,Purchased
15624510,Male,19,19000,0
15810944,Male,35,20000,0
15668575,Female,26,43000,0
15603246,Female,27,57000,0
15804002,Male,19,76000,0
15728773,Male,27,58000,0
15598044,Female,27,84000,0
15694829,Female,32,150000,1
15600575,Male,25,33000,0
15727311,Female,35,65000,0
... ...

Step 1：数据集处理

导入相关库

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

划分训练集和测试集

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.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)

Step2：训练预测

使用K-NN对训练集数据进行训练

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

对测试集进行预测

y_pred = classifier.predict(X_test)

生成混淆矩阵

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