基于机器学习的收入分析.rar

from sklearn import preprocessing import random from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn import preprocessing from datetime import datetime import time import math from matplotlib import pyplot from sklearn.model_selection import train_test_split from scipy import stats, integrate import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import KFold from sklearn.metrics import classification_report from sklearn.model_selection import learning_curve from sklearn.model_selection import validation_curve from sklearn.model_selection import cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA 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 from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import AdaBoostRegressor from xgboost import XGBRegressor from sklearn.ensemble import GradientBoostingRegressor from lightgbm import LGBMRegressor from date_process import data_read_csv train_x,train_y=data_read_csv() x_train, x_test, y_train, y_test = train_test_split(np.array(train_x), np.array(train_y), test_size=0.2,shuffle=True,random_state=1) print('x_train.shape',x_train.shape) print('x_test.shape',x_test.shape) # 集成学习模型 # svm算法 from sklearn.linear_model import LassoLarsIC as LR#逻辑回归 svm = XGBRegressor(learning_rate=0.02,subsample=0.99,max_depth=1) svm.fit(x_train, y_train) svm_pred = svm.predict(x_test) from metra import metric mae, mse, rmse, mape, mspe,r2=metric(np.array(svm_pred), np.array(y_test)) print('mae, mse, rmse, mape, mspe,r2') print(mae, mse, rmse, mape, mspe,r2) # 设置Seaborn样式 import seaborn as sns import matplotlib.pyplot as plt sns.set(style="darkgrid") x = range(len(y_test)) data = pd.DataFrame({'x': x, 'y_pred': svm_pred, 'y_true': y_test}) # 绘制y_pred的折线图 sns.lineplot(x='x', y='y_pred', data=data, linewidth=1, label='y_pred') # 绘制y_true的折线图 sns.lineplot(x='x', y='y_true', data=data, linewidth=1, label='y_true') # 添加标题和标签 plt.title('Prediction vs True') plt.xlabel('Date') plt.ylabel('Values') plt.savefig('transformer10预测1.png') # 显示图形 plt.show()