机器学习项目实战机器学习预测股价

　　机器学习项目实战系列机器学习预测股价
　　目录 机器学习项目实战系列 机器学习预测股价
　　一、概述
　　二、分析数据
　　1.导入
　　2.数据导入
　　3.分析股票尾市数据
　　4.构建模型
　　5.测试模型
　　6.展示预测结果一、概述
　　根据上一年的数据预测股票市场的未来价格
　　数据集：股票价格预测数据集：Two Sigma: Using News to Predict Stock Movements | Kaggle
　　源代码：股票价格预测项目：Just a moment...
　　二、分析数据1.导入import pandas as pd import numpy as np   import matplotlib.pyplot as plt %matplotlib inline   from matplotlib.pylab import rcParams rcParams[＂figure.figsize＂]=20,10 from keras.models import Sequential from keras.layers import LSTM,Dropout,Dense     from sklearn.preprocessing import MinMaxScaler2.数据导入
　　3.分析股票尾市数据df[＂Date＂]=pd.to_datetime(df.Date,format=＂%Y-%m-%d＂) df.index=df[＂Date＂]   plt.figure(figsize=(16,8)) plt.plot(df[＂Close＂],label=＂Close Price history＂)
　　4.构建模型import math #Create a new dataframe with only the Close column data = df.filter([＂Close＂]) #Convert the dataframe to a numpy array dataset = data.values   #Get the number of rows to train the model training_data_len = math.ceil( len(dataset) * .8) training_data_len
　　#Scale the data scaler=MinMaxScaler(feature_range=(0,1)) scaled_data=scaler.fit_transform(dataset)   scaled_data
　　#Create the training data set #Create the scaled training data set train_data = scaled_data[0:training_data_len , :] #Split the data into x_train and y_train data sets x_train = [] y_train = []   for i in range(60,len(train_data)):     x_train.append(train_data[i-60:i,0])     y_train.append(train_data[i,0])     if i<= 60:         print(x_train)         print(y_train)         print()
　　#Build the LSTM model model = Sequential() model.add(LSTM(50,return_sequences=True,input_shape=(x_train.shape[1],1))) model.add(LSTM(50,return_sequences=False)) model.add(Dense(25)) model.add(Dense(1))
　　5.测试模型#Create the testing data set #Create a new array containing scaled values from index 1543 to 2003 test_data = scaled_data[training_data_len - 60: , :] #Create the data sets x_test and y_test x_test = [] y_test = dataset[training_data_len: , :] for i in range(60, len(test_data)):     x_test.append(test_data[i-60:i,0])
　　#Get the models predicted price values predictions = model.predict(x_test) predictions = scaler.inverse_transform(predictions)
　　6.展示预测结果#Plot the data train = data[:training_data_len] valid = data[training_data_len:] valid[＂Predictions＂] = predictions #Visualize the data plt.figure(figsize=(16,8)) plt.title(＂Model＂) plt.xlabel(＂Date＂, fontsize=18) plt.ylabel(＂Close Prise USD ($)＂, fontsize=18) plt.plot(train[＂Close＂]) plt.plot(valid[[＂Close＂, ＂Predictions＂]]) plt.legend([＂Train＂,＂Val＂,＂Predictions＂], loc=＂lower right＂) plt.show()