Predict Stock Prices with Python & Machine Learning

Table of Contents

1. Introduction
2. Setting Up the Environment
3. Understanding LSTM
4. Preprocessing the Data
5. Building the LSTM Model
6. Training the Model
7. Evaluating the Model
8. Visualizing the Results
9. Predicting the Closing Stock Price
10. Conclusion

Introduction

Welcome to this tutorial on predicting the closing stock price of Apple Inc. using an artificial neural network. In this video, I will Show You how to use the Python programming language and machine learning to build and train an LSTM model that can accurately predict stock prices. Throughout this tutorial, we will cover various topics, including setting up the environment, understanding LSTM, preprocessing the data, building the LSTM model, training the model, evaluating the model, visualizing the results, and finally, predicting the closing stock price.

Setting Up the Environment

Before we dive into the details of building the LSTM model, let's first set up our programming environment. We will be using Google's online Python programming platform called Colab. With Colab, you don't have to install Python on your computer as you can write and run your code online. Here are the steps to get started:

1. Go to the Google Colab Website at colelab.research.google.com.
2. Log in using your Google account.
3. Click on "File" and then click on "New Python3 notebook" to Create a new notebook.

Now that we have our environment set up, let's proceed to the next section.

Understanding LSTM

LSTM stands for Long Short-Term Memory, and it is an artificial recurrent neural network architecture used in the field of deep learning. Unlike standard feed-forward neural networks, LSTM has feedback connections that allow it to process entire sequences of data, such as time series data. LSTM models are widely used for sequence prediction problems and have proven to be extremely effective.

Preprocessing the Data

In this section, we will preprocess the stock price data to prepare it for training the LSTM model. We will first import the necessary libraries, such as Pandas and NumPy, to handle the data. Then, we will retrieve the stock quote data from Yahoo Finance for a specific time period, in this case, from January 2012 to December 2019.

After obtaining the data, we will Visualize it to gain insights into the stock's price history. We will plot the closing price over time using the Matplotlib library and observe any trends or Patterns in the data.

Once we have a good understanding of the data, we will Scale it using MinMaxScaler from the Scikit-learn library. Scaling the data is essential to bring all the values into a specific range, usually between 0 and 1. This step ensures that the input data is uniform and does not bias the model.

Building the LSTM Model

Now that we have preprocessed the data, it's time to build our LSTM model. We will use the Keras library, specifically the Sequential API, to build the model. The Sequential API allows us to build neural network models layer by layer.

First, we will add an LSTM layer to our model with 50 neurons. We will set "return_sequences" to True since we plan to add another LSTM layer. The "input_shape" parameter will be set to (60, 1) since we will be using the past 60 days' stock prices as input for predictions.

Next, we will add a Second LSTM layer with 50 neurons, again setting "return_sequences" to False, as this will be the last LSTM layer in our model.

We will then add a few more layers, including a dense layer with 25 neurons and another dense layer with just one neuron. These layers help further refine the model's predictions.

Training the Model

With our LSTM model built, it's time to train it on the preprocessed data. We will use the fit() function from Keras to train the model. The fit() function takes in the training data (X_train and y_train), the batch size (usually set to 1), and the number of epochs (the number of times the entire dataset is passed through the network). Training the model involves fine-tuning its weights and biases to minimize the error between the predicted outputs and the actual outputs.

During the training process, we can monitor the model's performance by evaluating the loss function and other metrics. In our case, we will calculate the root mean squared error (RMSE), which measures the difference between the actual stock prices and the predicted stock prices. A lower RMSE indicates a better fit.

Evaluating the Model

Once the model is trained, we need to evaluate its performance on a separate validation dataset. We will use the last 60 days of the stock price data as the validation dataset. By comparing the model's predicted prices with the actual prices, we can assess its accuracy. We will calculate the RMSE for the validation dataset to determine how well the model performs on unseen data.

Visualizing the Results

To better understand the performance of our model, we will visualize the predicted and actual closing stock prices. Using Matplotlib, we will plot the predicted prices in orange/yellow and the actual prices in Blue. This visualization allows us to compare the trends and patterns in the predicted and actual data and get a visual representation of the model's accuracy.

Predicting the Closing Stock Price

Finally, we will use our trained LSTM model to predict the closing stock price for a specific date, in this case, December 18, 2019. We will retrieve the stock quote for that date from Yahoo Finance and preprocess it just like we did with the training data. Then, we will feed the preprocessed data into our model and obtain the predicted price. We will compare this predicted price with the actual price obtained from Yahoo Finance to assess the accuracy of our model's prediction.

Conclusion

In this tutorial, we have learned how to predict the closing stock price of Apple Inc. using an LSTM model in Python. We started by setting up the environment and understanding the concept of LSTM. Then, we preprocessed the data, built and trained the model, evaluated its performance, and visualized the results. Finally, we used our trained model to predict the closing stock price for a specific date and compared it with the actual price. LSTM models are a powerful tool for predicting time series data, and with the knowledge gained from this tutorial, you can Apply them to various other applications as well.

Stay tuned for more tutorials on Python programming, machine learning, and other related topics. If you found this tutorial helpful, please share it with others who might benefit from it. Thank you for watching, and happy coding!