紧接上文回归模型我给个线性回归的和SVM回归的，大家可以比较看看
优缺点就不说了，像废话一样，直接上程序。
基于二手车估价的线性回归
'''如有问题if you want my model and word''''''小编QQ:63118384'''import numpy as npimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import SGDRegressorfrom sklearn.metrics import r2_score, mean_squared_error, mean_absolute_errordata = pd.read_csv(open('C:/Users/Tracy/Desktop/300//附件/附件1.csv'))data.fillna(0)data1 = np.array(data)X = data1[:, 1:-2]y = data.iloc[:, 37]#print("Giving dataset has {} data points with {} variables each.".format(*data.shape))#print(y)minimum_price = np.min(y)maximum_price = np.max(y)mean_price = np.mean(y)median_price = np.median(y)std_price = np.std(y)# 分析回归目标值的差异。print("The max target price is", np.max(y))print("The min target price is", minimum_price)print("The average price value is", mean_price)#print("The median price value is", median_price)#print("The std_price price value is", std_price)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)print(X_train.shape)print(X_test.shape)print(y_train.shape)print(y_test.shape)# 分别初始化对特征和目标值的标准化器。ss_X = StandardScaler()ss_y = StandardScaler()# 分别对训练和测试数据的特征以及目标值进行标准化处理。X_train = ss_X.fit_transform(X_train)X_test = ss_X.transform(X_test)y_train = ss_y.fit_transform(y_train)y_test = ss_y.transform(y_test)st = ss_y.transform(y_test)
接下来的使用默认配置初始化线性回归器LinearRegression，使用训练数据进行参数估计，对测试数据进行回归预测，使用LinearRegression模型自带的评估模块，并输出评估结果；使用r2_score模块，并输出评估结果…
基于二手车估价线性回归的程序想要全部见最下方
基于二手车估价的SVM回归算法
'''如有问题if you want my model and word''''''小编QQ:63118384'''import numpy as npimport pandas as pdfrom sklearn import svm,datasetsfrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.svm import SVCfrom sklearn.model_selection import train_test_splitclf = OneVsRestClassifier(svm.SVC(kernel='linear'))df = pd.read_table('C:/Users/Tracy/Desktop/300/MathorCup大数据竞赛赛道A/附件/附件1.txt', 'r', delimiter='\\t', header = None)x = df.iloc[:5000,:36]x1 = np.array(df.iloc[:5000,:36]).astype(str)tdf = pd.read_table('C:/Users/Tracy/Desktop/300/MathorCup大数据竞赛赛道A/附件/附件2.txt', 'r', delimiter='\\t', header = None)y = tdf.iloc[:]y1 = np.array(tdf.iloc[:5000,:36]).astype(str)x_train, x1_test, y_train, y1_test = train_test_split(x, y, test_size=3)clf.fit(x_train, y_train)y_pred = clf.predict(x1_test)rf = pd.DataFrame(list(zip(y_pred, y1_test)), columns=['predicted', 'actual'])rf['correct'] = rf.apply(lambda r:1 if r['predicted'] == r['actual'] else 0, axis=1)'''如有问题if you want my model and word''''''小编QQ:63118384'''print(rf)
