import pandas as pd;
import numpy as np;
# 导入数据，路径中要么用\\或/或者在路径前加r
dataset = pd.read_csv('D:\\petrol_consumption.csv');
# 输出数据预览
print(dataset.head());
# 准备训练数据
# 自变量：汽油税、人均收入、高速公路、人口所占比例
# 因变量：汽油消耗量
X = dataset.iloc[:, 0:4].values;
y = dataset.iloc[:, 4].values;
# 将数据分为训练集和测试集
from sklearn.model_selection import train_test_split;
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0);
# 根据需要，可以进行属性量纲缩放。
from sklearn.preprocessing import StandardScaler;
sc = StandardScaler();
X_train = sc.fit_transform(X_train);
X_test = sc.transform(X_test);
# 随机森林建立回归模型
from sklearn.ensemble import RandomForestRegressor;
regressor = RandomForestRegressor(n_estimators=200, random_state=0);
regressor.fit(X_train, y_train);
y_pred = regressor.predict(X_test);
# 模型评价
from sklearn import metrics;
print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred));
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred));
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)));
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