![]() ![]() ![]() ![]() In this step, we are defining the Long Short-Term Memory model. Image by author Min-Max Scaler scaler=MinMaxScaler(feature_range=(0,1)) data.index=data.Date data.drop(“Date”,axis=1,inplace=True) final_data = data.values train_data=final_data valid_data=final_data scaler=MinMaxScaler(feature_range=(0,1)) scaled_data=scaler.fit_transform(final_data) x_train_data,y_train_data=, for i in range(60,len(train_data)): x_train_data.append(scaled_data) y_train_data.append(scaled_data) LSTM Model After running this method, we can also see that our data is sorted by the date index. When you call the head method on the dataframe, it displays the first five rows of the dataframe. The first thing we’ll do to get some understanding of the data is using the head method. Read Data import pandas as pd df = pd.read_csv('aapl_stock_1yr.csv') Head Method The dataframe that we will be using contains the closing prices of Apple stock of the last one year (SSept 15, 2020). After walking through with me on this project, you will learn some skills that will give you the ability to practice yourself using different datasets. I thought Apple would be a good one to go with. This helps us to understand that we have the right data and to get some insights about it.Īs mentioned earlier, for this exercise we will be using historical data of Apple. Secondly, we will start loading the data into a dataframe, it is a good practice to take a look at it before we start manipulating it. import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline from matplotlib.pylab import rcParams rcParams=20,10 from keras.models import Sequential from keras.layers import LSTM,Dropout,Dense from sklearn.preprocessing import MinMaxScaler Matplotlib is already included in Python that’s why we can import it without installing it. We can install these libraries using Pip library manager: pip install pandas numpy keras tensorflowĪfter the installation is completed, let’s import them into our code editor. It also has extensive documentation and developer guides. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. Keras is an API designed for human beings, not machines. Tensorflow has to be installed so that keras can work. Here is a list of the libraries we will install: pandas, numpy, keras, and tensorflow. I can’t wait to see our prediction accuracy results, let’s get started! Table of Contents:įirst things first, we have to install some libraries so that our program works. On this website, you can also find stock data for different companies and practice your skills using different datasets. The Apple stock data can be downloaded from here. The NASDAQ (National Association of Securities Dealers Automated Quotations) is an electronic stock exchange with more than 3,300 company listings. The stock data is available on NASDAQ official website. If you are wondering is it free to get that data, the answer is absolutely yes. As mentioned in the subtitle, we will be using Apple Stock Data. Then, we will start working on our prediction model. First, for those who are new to python, I will introduce it to you. If we want a machine to make predictions for us, we should definitely train it well with some data. This is a great project of using machine learning in finance. In this post, I will show you how to build a program that can predict the price of a specific stock. ![]()
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