行车风险数据集分享行车风险数据集分享

import numpy as np import pandas as pd from sklearn.metrics import confusion_matrix from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.svm import SVR from sklearn.neighbors import KNeighborsRegressor from sklearn.metrics import mean_squared_error,mean_absolute_error # 评价指标 from sklearn.linear_model import LogisticRegression # 基于机器学习svm knn 线性回归 的行车风险系数预测 效果很好 from sklearn.linear_model import LogisticRegression,LinearRegression date=pd.read_excel("date.xlsx") # 处理缺失值 date = date.fillna(0) # 选择重要的特征 print(date.columns) # 对样本进行随意切割，得到训练集和验证集 print(np.array(date[['半径', '直线长度']].values)) print(np.array(date['行车风险系数'].values)) x_train, x_test, y_train, y_test = train_test_split(np.array(date[['半径', '直线长度']].values), np.array(date['行车风险系数'].values), test_size=0.1) # knn算法 knn = KNeighborsRegressor() knn.fit(x_train, y_train) test_pred = knn.predict(x_test) # 进行预测 print("knn算法 ") print(test_pred[:10]) print(y_test[:10]) print('mean_squared_error:', mean_squared_error(y_test, test_pred)) # mse print("mean_absolute_error:", mean_absolute_error(y_test, test_pred)) # mae # svm算法 svm = SVR() svm.fit(x_train, y_train) test_pred = svm.predict(x_test) print("svm算法 ") print(test_pred[:10]) print(y_test[:10]) print('mean_squared_error:', mean_squared_error(y_test, test_pred)) # mse print("mean_absolute_error:", mean_absolute_error(y_test, test_pred)) # mae # 线性回归算法 lr = LinearRegression() lr.fit(x_train, y_train) test_pred = lr.predict(x_test) print("逻辑回归算法 ") print(test_pred[:10]) print(y_test[:10]) print('mean_squared_error:', mean_squared_error(y_test, test_pred)) # mse print("mean_absolute_error:", mean_absolute_error(y_test, test_pred)) # mae