weixin_38764818的博客

# 工作年限与收入之间的散点图# 导入第三方模块import pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns# 导入数据集income = pd.read_csv('Salary_Data.csv')# 绘制散点图sns.lmplot(x = 'YearsExperience', y = 'Salary', data = income, ci = None)# 显示图形plt.show()# 获取样本的数

鸢尾花识别:from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifier#声明一个鸢尾花的类对象iris = load_iris()# 获取样本数据iris_data = iris.data# 获取样本标记值iris_target = iris.targetprint

from sklearn.datasets import make_blobsfrom sklearn.neighbors import KNeighborsClassifierimport matplotlib.pyplot as pltimport numpy as np#中心点centers = [[-2,2],[2,2],[0,4]]x,y = make_blobs(n_samples=60,centers=centers,cluster_std=0.6)plt.figure(figs

from numpy import *import operator # 运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数def createDataSet(): group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) labels = ['A', 'A', 'B', 'B'] return ...

线性回归，体重预测from sklearn import datasets, linear_model # 引用 sklearn库，主要为了使用其中的线性回归模块# 创建数据集，把数据写入到numpy数组import numpy as np # 引用numpy库，主要用来做科学计算import matplotlib.pyplot as plt # 引用matplotlib库，主要用...