Stock Price Prediction Using Python & Machine Learning

Computer Science

21 Dec 201949:47

EducationalLearning

32 Likes 10 Comments

TLDRThis video tutorial demonstrates how to use a Long Short-Term Memory (LSTM) neural network to predict the closing stock price of Apple Inc. The presenter guides viewers through setting up a Python environment on Google's Colab, importing necessary libraries, and acquiring Apple's stock data from Yahoo Finance. The script explains the process of creating and training the LSTM model with the past 60 days of stock prices, scaling the data, and evaluating the model's performance using the root mean squared error (RMSE). The tutorial concludes with a prediction for Apple's stock price on December 18, 2019, and a comparison with the actual closing price.

Takeaways

🚀 The video is a tutorial on using Python and machine learning to predict the closing stock price of Apple Inc.
🌐 The demonstration takes place on Google's Colab platform, which allows for Python programming without installing it locally.
🤖 An artificial neural network, specifically an LSTM (Long Short-Term Memory) model, is used for the prediction task.
📈 The model is trained on 60 days of historical stock price data to predict future closing prices.
📊 Data is sourced from Yahoo Finance using the pandas_datareader library.
📉 The stock data is visualized to understand trends and prepare for model training.
🔄 Data preprocessing includes scaling the closing prices to a range of 0 to 1 for optimal neural network performance.
🧠 The LSTM model architecture consists of multiple layers, including additional dense layers for complex pattern recognition.
🏋️‍♂️ The model is compiled with the 'adam' optimizer and 'mean_squared_error' loss function.
📝 The model's performance is evaluated using the root mean squared error (RMSE) metric.
🔮 Predictions are made for a specific future date, and the model's accuracy is compared to the actual stock price.

Q & A

What is the main focus of the video?
-The main focus of the video is to demonstrate how to predict the closing stock price of Apple Inc. using an artificial neural network, specifically an LSTM (Long Short-Term Memory) model in Python.
Which website is used to run the Python code without installing Python on the computer?
-Google's Colab (colab.research.google.com) is used to run Python code without the need to install Python on the computer.
What type of neural network is used in the video for predicting stock prices?
-A recurrent neural network called LSTM (Long Short-Term Memory) is used for predicting stock prices in the video.
How does the LSTM model handle sequence prediction problems effectively?
-LSTMs are effective for sequence prediction problems because they can store past information that is important and forget information that is not, allowing them to process sequences of data like stock prices over time.
What is the source of the stock data used in the video?
-The stock data used in the video is sourced from Yahoo Finance through the pandas_datareader library.
What is the time frame of the Apple stock data used in the video?
-The Apple stock data used in the video spans from January 3, 2012, to December 17, 2019.
How is the data scaled before training the LSTM model?
-The data is scaled using the MinMaxScaler from the sklearn.preprocessing library, which transforms the data to have values between 0 and 1.
What is the purpose of reshaping the x_train and x_test datasets?
-The x_train and x_test datasets are reshaped to a three-dimensional format to match the input requirements of the LSTM model, which expects the shape of (number of samples, number of time steps, number of features).
How is the model's performance evaluated in the video?
-The model's performance is evaluated using the root mean squared error (RMSE), which measures the standard deviation of the residuals and indicates the accuracy of the model's predictions.
What is the actual closing price of Apple stock on December 17, 2019, according to the video?
-According to the video, the actual closing price of Apple stock on December 17, 2019, was $280.41004 USD.
How does the video demonstrate the prediction of the closing stock price for a future date?
-The video demonstrates the prediction of the closing stock price for a future date by using the most recent 60 days of closing prices, scaling and reshaping this data, and then using it as input for the trained LSTM model to generate a prediction.

Outlines

00:00

🚀 Introduction to Python and Machine Learning

The video begins with a welcome to a tutorial on Python programming and machine learning, focusing on predicting the closing stock price of Apple Inc. using an artificial neural network. The host is on Google's Colab website, which allows for easy Python programming without installing Python on a local computer. The first steps involve creating a new Python3 notebook and writing a description of the program, which will use a Long Short-Term Memory (LSTM) network to predict stock prices based on the past 60 days of data.

05:00

📊 Apple Stock Information and Visualization

The host proceeds to retrieve and display Apple's stock information from 2012 to 2019, noting the start date discrepancy due to stock market closure. The video then moves on to visualize the closing price history of Apple's stock, highlighting a general increasing trend over the years. The host makes an observation that buying Apple stock between 2013 and 2014 and selling it between 2019 and 2020 could have resulted in significant profit. The actual closing price for December 17, 2019, is revealed to be $280.41004 USD.

10:01

🔢数据处理: Scaling and Preparing Data for LSTM

The video script details the process of preparing the data for the LSTM model. This includes scaling the data to values between 0 and 1, creating the training dataset by selecting about 80% of the data, and splitting it into features (X_train) and target variables (y_train). The data is then converted to a numpy array and reshaped to fit the LSTM model's input requirements of being three-dimensional.

15:02

🤖 Building and Compiling the LSTM Model

The host guides the audience through building the LSTM model by adding layers, including two LSTM layers with 50 neurons each and a dense layer with 25 neurons. The model is compiled with the 'adam' optimizer and 'mean_squared_error' loss function. The video script also includes the process of training the model using the training datasets and setting up the testing datasets for evaluation.

20:04

📈 Evaluating and Plotting the Model's Predictions

After training the model, the host evaluates its performance by calculating the root mean squared error (RMSE) and plots the model's predictions against the actual closing prices for visualization. The script outlines the process of creating a new data frame with the last 60 days of closing prices, scaling and reshaping the data, and using the model to predict the closing price for a specific future date. The actual and predicted prices for December 18, 2019, are compared, showing a slight discrepancy.

Mindmap

Keywords

💡Python

Python is a high-level programming language known for its readability and ease of use. In the video, Python is the chosen language for creating a machine learning model to predict stock prices. It's mentioned as the tool that allows users to write code without needing to install it on their computers, thanks to platforms like Google's Colab.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the use of statistical models and algorithms to enable systems to learn from and make predictions or decisions based on data. In the video, the focus is on using machine learning to predict the closing stock price of Apple Inc., demonstrating how this technology can be applied to financial data analysis.

💡Artificial Neural Network (ANN)

An artificial neural network is a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In the context of the video, an ANN is used to model and predict complex time-series data, such as the stock price of Apple Inc.

💡Long Short-Term Memory (LSTM)

Long Short-Term Memory, or LSTM, is a type of recurrent neural network that is particularly effective in learning long-term dependencies and is commonly used in sequence prediction problems. The video demonstrates how LSTM can be applied to predict future stock prices by learning from historical stock price data.

💡Stock Price Prediction

Stock price prediction involves the use of various techniques, including machine learning, to forecast future prices of stocks based on historical data and other relevant factors. The video focuses on using Python and LSTM to predict the closing stock price of Apple Inc. as a demonstration of machine learning in finance.

💡Google Colab

Google Colab is a cloud-based platform that allows users to write and execute Python code in their web browser, providing a free and accessible way to run Python programs, including those requiring significant computing resources. In the video, the presenter uses Google Colab to demonstrate how to write Python code for stock price prediction without the need to install Python on a local machine.

💡Data Preprocessing

Data preprocessing is the process of cleaning and transforming raw data into a format that is suitable for analysis or modeling. In the context of the video, preprocessing involves scaling the stock price data to a range of 0 to 1, which helps the neural network model to converge more effectively during training.

💡Sequence Data

Sequence data refers to a series of data points arranged in a specific order, typically over time. In the video, sequence data is represented by the historical stock prices of Apple Inc. The LSTM model is particularly adept at handling sequence data because it can capture temporal dependencies between data points.

💡Training Data

Training data is the dataset used to teach a machine learning model how to make predictions or decisions. It typically consists of input features and corresponding output labels. In the video, the training data is comprised of 80% of the available stock price history, which is used to train the LSTM model to predict future closing prices.

💡Model Evaluation

Model evaluation is the process of assessing the performance of a machine learning model by comparing its predictions to actual data. It's crucial for understanding how well the model has learned from the training data and how accurately it can make future predictions. In the video, model evaluation is demonstrated using the root mean squared error (RMSE) to measure the difference between the predicted and actual stock prices.

💡Data Visualization

Data visualization is the graphical representation of information and data, which makes it easier to understand patterns, trends, and correlations. In the video, data visualization is used to display the historical closing prices of Apple Inc. and to compare the model's predictions with the actual stock prices.

Highlights

The video demonstrates how to predict the closing stock price of Apple Inc using an artificial neural network, specifically an LSTM (Long Short-Term Memory) model.

Google's Colab platform is used for its ease of use in programming Python without needing to install it on your computer.

The LSTM model is chosen for its ability to process sequences of data, making it suitable for time series prediction like stock prices.

The program uses the past 60 days of stock price data to predict the closing price of Apple Inc.

The video provides a brief explanation of LSTMs, highlighting their ability to store and forget information based on importance.

The Python libraries used in the program include pandas, numpy, sklearn, keras, and matplotlib for data manipulation, model creation, and visualization.

The stock data for Apple Inc is fetched from Yahoo Finance using the pandas_datareader library.

The data is visualized to observe the trend in Apple's closing stock price over the years, showing a significant increase.

The data is preprocessed by scaling the closing prices to a range of 0 to 1 for optimal neural network performance.

The training dataset is created by selecting 80% of the total data and structuring it for the LSTM model's input requirements.

The LSTM model is built with Keras, consisting of two LSTM layers followed by two dense layers.

The model is compiled using the 'adam' optimizer and 'mean_squared_error' loss function.

The model is trained on the training dataset with a batch size of 1 and an epoch count of 1.

A testing dataset is created from the remaining 20% of the data to evaluate the model's performance.

The model's predictions are compared to the actual stock prices using the root mean squared error (RMSE) as a performance metric.

The video concludes with a visualization of the model's predictions against the actual closing prices of Apple Inc's stock.

The video also demonstrates how to predict the closing stock price for a future date, December 18, 2019.

The actual closing price for December 18, 2019 is compared with the model's prediction, showing a slight discrepancy.

Transcripts

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