Tutorial Playlist

Machine Learning Tutorial: A Step-by-Step Guide for Beginners


An Introduction To Machine Learning

Lesson - 1

What is Machine Learning and How Does It Work?

Lesson - 2

The Complete Guide to Understanding Machine Learning Steps

Lesson - 3

Top 10 Machine Learning Applications in 2022

Lesson - 4

An Introduction to the Types Of Machine Learning

Lesson - 5

Supervised and Unsupervised Learning in Machine Learning

Lesson - 6

Everything You Need to Know About Feature Selection

Lesson - 7

Linear Regression in Python

Lesson - 8

Everything You Need to Know About Classification in Machine Learning

Lesson - 9

An Introduction to Logistic Regression in Python

Lesson - 10

Understanding the Difference Between Linear vs. Logistic Regression

Lesson - 11

The Best Guide On How To Implement Decision Tree In Python

Lesson - 12

Random Forest Algorithm

Lesson - 13

Understanding Naive Bayes Classifier

Lesson - 14

The Best Guide to Confusion Matrix

Lesson - 15

How to Leverage KNN Algorithm in Machine Learning?

Lesson - 16

K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases

Lesson - 17

PCA in Machine Learning - Your Complete Guide to Principal Component Analysis

Lesson - 18

What is Cost Function in Machine Learning

Lesson - 19

The Ultimate Guide to Cross-Validation in Machine Learning

Lesson - 20

An Easy Guide to Stock Price Prediction Using Machine Learning

Lesson - 21

What Is Reinforcement Learning? The Best Guide To Reinforcement Learning

Lesson - 22

What Is Q-Learning? The Best Guide to Understand Q-Learning

Lesson - 23

The Best Guide to Regularization in Machine Learning

Lesson - 24

Everything You Need to Know About Bias and Variance

Lesson - 25

The Complete Guide on Overfitting and Underfitting in Machine Learning

Lesson - 26

Mathematics for Machine Learning - Important Skills You Must Possess

Lesson - 27

A One-Stop Guide to Statistics for Machine Learning

Lesson - 28

Embarking on a Machine Learning Career? Here’s All You Need to Know

Lesson - 29

How to Become a Machine Learning Engineer?

Lesson - 30

Top 45 Machine Learning Interview Questions and Answers for 2022

Lesson - 31

Explaining the Concepts of Quantum Computing

Lesson - 32

Supervised Machine Learning: All You Need to Know

Lesson - 33
An Easy Guide to Stock Price Prediction Using Machine Learning

Stock price analysis has been a critical area of research and is one of the top applications of machine learning. This tutorial will teach you how to perform stock price prediction using machine learning and deep learning techniques. Here, you will use an LSTM network to train your model with Google stocks data.

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What is the Stock Market?

A stock market is a public market where you can buy and sell shares for publicly listed companies. The stocks, also known as equities, represent ownership in the company. The stock exchange is the mediator that allows the buying and selling of shares. 


Importance of Stock Market

  • Stock markets help companies to raise capital.
  • It helps generate personal wealth.
  • Stock markets serve as an indicator of the state of the economy.
  • It is a widely used source for people to invest money in companies with high growth potential.

Stock Price Prediction

Stock Price Prediction using machine learning helps you discover the future value of company stock and other financial assets traded on an exchange. The entire idea of predicting stock prices is to gain significant profits. Predicting how the stock market will perform is a hard task to do. There are other factors involved in the prediction, such as physical and psychological factors, rational and irrational behavior, and so on. All these factors combine to make share prices dynamic and volatile. This makes it very difficult to predict stock prices with high accuracy. 

Understanding Long Short Term Memory Network

Here, you will use a Long Short Term Memory Network (LSTM) for building your model to predict the stock prices of Google.

LTSMs are a type of Recurrent Neural Network for learning long-term dependencies. It is commonly used for processing and predicting time-series data. 


From the image on the top, you can see LSTMs have a chain-like structure. General RNNs have a single neural network layer. LSTMs, on the other hand, have four interacting layers communicating extraordinarily.

LSTMs work in a three-step process.

  • The first step in LSTM is to decide which information to be omitted from the cell in that particular time step. It is decided with the help of a sigmoid function. It looks at the previous state (ht-1) and the current input xt and computes the function.
  • There are two functions in the second layer. The first is the sigmoid function, and the second is the tanh function. The sigmoid function decides which values to let through (0 or 1). The tanh function gives the weightage to the values passed, deciding their level of importance from -1 to 1.
  • The third step is to decide what will be the final output. First, you need to run a sigmoid layer which determines what parts of the cell state make it to the output. Then, you must put the cell state through the tanh function to push the values between -1 and 1 and multiply it by the output of the sigmoid gate.

With this basic understanding of LSTM, you can dive into the hands-on demonstration part of this tutorial regarding stock price prediction using machine learning.

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Google Stock Price Prediction Using LSTM

1. Import the Libraries.


2. Load the Training Dataset.

The Google training data has information from 3 Jan 2012 to 30 Dec 2016. There are five columns. The Open column tells the price at which a stock started trading when the market opened on a particular day. The Close column refers to the price of an individual stock when the stock exchange closed the market for the day. The High column depicts the highest price at which a stock traded during a period. The Low column tells the lowest price of the period. Volume is the total amount of trading activity during a period of time. 


3. Use the Open Stock Price Column to Train Your Model.


4. Normalizing the Dataset.


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5. Creating X_train and y_train Data Structures.



6. Reshape the Data.


7. Building the Model by Importing the Crucial Libraries and Adding Different Layers to LSTM.



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8. Fitting the Model.


9. Extracting the Actual Stock Prices of Jan-2017.


10. Preparing the Input for the Model.


11. Predicting the Values for Jan 2017 Stock Prices.


12. Plotting the Actual and Predicted Prices for Google Stocks.


As you can see above, the model can predict the trend of the actual stock prices very closely. The accuracy of the model can be enhanced by training with more data and increasing the LSTM layers.

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The stock market plays a remarkable role in our daily lives. It is a significant factor in a country's GDP growth. In this tutorial, you learned the basics of the stock market and how to perform stock price prediction using machine learning. 

Do you have any questions related to this tutorial on stock prediction using machine learning? In case you do, then please put them in the comments section. Our team of experts will help you answer your questions. To learn more, watch this video: Stock Price Prediction.

If you are interested in learning further about Machine Learning, including the various ML applications across industries, do explore Simplilearn’s Post Graduate Program in AI and Machine Learning in partnership with Purdue University, and in collaboration with IBM. This comprehensive 12-month program covers everything from Statistics, Machine Learning, Deep Learning, Reinforcement Learning, to Natural Language Programming and more. You get to learn from global experts and at the end of the program walk away with great endorsements from industry and academic leaders and a skillet that is today the most in-demand in organizations across the world.

Happy learning!

About the Author

Avijeet BiswalAvijeet Biswal

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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