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利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2)

利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2)

利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2)

利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2)

利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2)

利用Python进行企业销售分析与预测 #导入Python库 import pandas as pd import numpy as np plt.rcParams['font.sans-serif'] = ['simhei'] ______ from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.linear_model import LinearRegression #获取数据 df=pd.read_excel('企业销售分析与预测/价格预测数据_清洗后.xlsx') print(df.head(n=1)) #数据预处理 column_name = X.columns ______ #数据建模 X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=123) lr = LinearRegression() ______ Y_pred = lr.predict(X_test) mse_value = metrics.mean_squared_error(Y_test, Y_pred) print('均方误差MSE: ', mse_value) r2_score_value = metrics.r2_score(Y_test, Y_pred) print('决定系数R2: ', r2_score_value) ______ x_index = range(1, len(Y_test) + 1) ______ plt.plot(x_index, Y_pred, linestyle='-', marker='o', color='dodgerblue', label='预测值', linewidth=2) plt.legend(loc='best') plt.xticks([]) plt.savefig('预测值与测试值的关系.png') plt.show() df_weight = pd.DataFrame({'特征':column_name,'系数':np.round(lr.coef_,3)}) df_weight.to_excel('回归系数.xlsx', index=False, encoding='utf-8-sig') print(df_weight) #模型预测 df = pd.read_excel('企业销售分析与预测/价格预测数据_下期因素数据.xlsx') print(df) data = df[['国内市场铁精粉价格','下游钢材产量','下游钢材价格','政策影响']] price_pred = lr.predict(data) print(df) df.to_excel('价格预测数据结果.xlsx', index=False, encoding='utf-8-sig') (缺图)