机器学习项目一：利用随机森林回归算法预测黄金价格

import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

 

#读取数据
gold_data = pd.read_csv('data/gld_price_data.csv')
gold_data.head() 

# 划分特征和标签
X = gold_data.drop(columns=['Date','GLD'],axis=1)
y = gold_data['GLD']

划分数据集
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state =2)
model = RandomForestRegressor(n_estimators=100)
model.fit(X_train,y_train)

#模型预测
pred = model.predict(X_test)

#结果可视化
plt.plot(y_test,color = 'blue',label = 'Actual Value')
plt.plot(pred,color = 'green',label = 'Pedicition Value')
plt.legend()
plt.title('Actual Predicition VS Pedicition Price')
plt.xlabel('number of values')
plt.ylabel('GLD price')

工作流程：

 可视化预测值与实际值：