項目背景|項目概述|學習目標|任務實施步驟拓展實訓薪資數據相關性分析【數據挖掘應用】課程負責人：陳清華01101111011010111101010000101101010100111101

熟練使用read_csv()從本地CSV文件讀取數據；

熟練使用sklearn對數據進行回歸分析；熟練使用matplotlib工具包展現回歸分析結果。技能學習目標項目總體要求原始數據結果輸出#coding:utf-8importpandas

aspdimportmatplotlib.pyplotaspltplt.rcParams['font.sans-serif']=['SimHei']plt.rcParams['axes.unicode_minus']=Falseplt.figure(figsize=(4,3))#獲取工齡及薪資數據salary=pd.read_csv("salary.csv",delimiter=",")#畫散點圖plt.scatter(salary['Year'],salary['Salary'],color='red',marker='.')#設置柱狀圖的標題、軸標簽plt.title("工齡與工資散點圖")plt.xlabel('工齡')plt.ylabel('薪資')一、數據獲取二、數據清洗#coding:utf-8salary=salary.dropna()salary=salary.drop_duplicates()plt.figure(figsize=(4,3))plt.scatter(salary['Year'],salary['Salary'],color='red')plt.title("工齡與工資圖")plt.xlabel('工齡')plt.ylabel('薪資')plt.show()salary.head()三、數據篩選salary=salary[(salary["Year"]<12)&(salary["Salary"]<120000)]plt.figure(figsize=(4,3))plt.scatter(salary['Year'],salary['Salary'],color='red')plt.title("工齡與工資圖")plt.xlabel('工齡')plt.ylabel('薪資')plt.show()salary.head()x=salary[["Year"]]y=salary[["Salary"]]#初始化線性回歸模型regr=linear_model.LinearRegression()#擬合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)plt.scatter(x,y,color="blue",linewidths=1,edgecolor='k',label="原始值")plt.scatter(x_new,regr.predict(x_new),s=2,color="red",linewidths=1,label="一元線性回歸")四、線性回歸分析poly=PolynomialFeatures(degree=3)#3次多項式特征生成器xt=poly.fit_transform(x)polymodel=linear_model.LinearRegression()polymodel.fit(xt,y)xt_new=poly.fit_transform(x_new)plt.scatter(x,y,color="blue")plt.scatter(x_new,regr.predict(x_new),s=2,color="red",linewidths=1,label="線性回歸")plt.scatter(x_new,polymodel.predict(xt_new),s=2,color="black",label="多項式回歸")

四、多項式回歸分析五、數據集切分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="線性回歸預測值",linestyle='--')plt.plot(range(len(y_pred2)),y_pred2,'red',label="多項式回歸預測值",linestyle='-.')plt.plot(range(len(