03-08-应用拓展：薪资数据相关性分析

项目背景|项目概述|学习目标|任务实施步骤拓展实训薪资数据相关性分析【数据挖掘应用】课程负责人：陈清华01101111011010111101010000101101010100111101

熟练使用read_csv()从本地CSV文件读取数据；

熟练使用sklearn对数据进行回归分析；熟练使用matplotlib工具包展现回归分析结果。技能学习目标项目总体要求原始数据结果输出#coding:utf-8importpandas

aspdimportmatplotlib.pyplotaspltplt.rcParams['font.sans-serif']=['SimHei']plt.rcParams['axes.unicode_minus']=Falseplt.figure(figsize=(4,3))#获取工龄及薪资数据salary=pd.read_csv("salary.csv",delimiter=",")#画散点图plt.scatter(salary['Year'],salary['Salary'],color='red',marker='.')#设置柱状图的标题、轴标签plt.title("工龄与工资散点图")plt.xlabel('工龄/年')plt.ylabel('薪资/元')一、数据获取二、数据清洗#coding:utf-8salary=salary.dropna()salary=salary.drop_duplicates()plt.figure(figsize=(4,3))plt.scatter(salary['Year'],salary['Salary'],color='red')plt.title("工龄与工资图")plt.xlabel('工龄/年')plt.ylabel('薪资/元')plt.show()salary.head()三、数据筛选salary=salary[(

salary["Year"]<12

)&(

salary["Salary"]<120000

)]plt.figure(figsize=(4,3))plt.scatter(salary['Year'],salary['Salary'],color='red')plt.title("工龄与工资图")plt.xlabel('工龄/年')plt.ylabel('薪资/元')plt.show()salary.head()四、数据分布plt.boxplot(salary["Year"])plt.boxplot(salary["Salary"])五、数据统计#以int类型的变量来统计工龄salary["Year"]=salary["Year"].round()#统计每个工龄的平均工资avg=salary.groupby(["Year"],as_index=False)["Salary"].mean()avg.head()五、数据统计x=salary[["Year"]]y=salary[["Salary"]]#初始化线性回归模型regr=linear_model.LinearRegression()#拟合regr.fit(x,y)x_min=x.values.min()-0.1x_max=x.values.max()+0.1x_new=np.arange(x_min,x_max,0.05).reshape(-1,1)x_new_df=?plt.scatter(x,y,color="blue",linewidths=1,edgecolor='k',label="原始值")plt.scatter(x_new,regr.predict(x_new_df),s=2,color="red",linewidths=1,label="一元线性回归")六、线性回归分析poly=PolynomialFeatures(degree=3)#3次多项式特征生成器xt=poly.fit_transform(x)polymodel=linear_model.LinearRegression()polymodel.fit(xt,y)xt_new=poly.fit_transform(x_new)xt_new_df=?plt.scatter(x,y,color="blue")plt.scatter(x_new,regr.predict(x_new),s=2,color="red",linewidths=1,label="线性回归")plt.scatter(x_new,polymodel.predict(xt_new_df),s=2,color="black",label="多项式回归")

六、多项式回归分析degree=2六、多项式回归分析degree=4六、多项式回归分析六、数据集切分fromsklearn.model_selectionimporttrain_test_splitx_train,x_test,y_train,y_test=train_test_split(sa[['Year']],sa[['Salary']],train_size=0.7,test_size=0.3)regr=linear_model.LinearRegression()regr.fit(x_train,y_train)y_pred1=regr.predict(x_test)poly=PolynomialFeatures(degree=3)xt_train=poly.fit_transform(x_train)polymodel=linear_model.LinearRegression()polymodel.fit(xt_train,y_train)xt_test=poly.fit_transform(x_test)y_pred2=polymodel.predict(xt_test)plt.plot(range(len(y_pred1)),y_pred1,'blue',label="线性回归预测值",linestyle='--')plt.plot(range(len(y_pred2)),y_pred2,'red',label="多项式回归预测值",linestyle='-.')plt.plot(