Course Content
Stock Prices Prediction Project
Introduction
Note
To make it easier for you to go through the project, it would be nice to know the following topics:
The
prophetlibrary.P.S. Even without knowledge of these topics, you can complete the project.
The stock market is a complex and ever-changing landscape that can be difficult to navigate. In this analysis project, we will take a deep dive into the performance of a specific stock or group of stocks in order to gain a better understanding of the factors that drive their performance. We will examine historical data such as stock prices, trading volume, and financial statements, as well as external factors such as economic conditions and industry trends.
By analyzing this data, we hope to gain valuable insights that can inform investment decisions and help us make more informed predictions about future market trends.
In this notebook, we will analyze data from technology stocks such as Apple, Amazon, Google, and Microsoft. We will use yfinance to retrieve stock information and visualize it using seaborn and matplotlib. We will examine methods for assessing the risk of a stock based on past performance and use an LSTM method to predict future stock prices.
Let's get it started!
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Note
To make it easier for you to go through the project, it would be nice to know the following topics:
The
prophetlibrary.P.S. Even without knowledge of these topics, you can complete the project.
The stock market is a complex and ever-changing landscape that can be difficult to navigate. In this analysis project, we will take a deep dive into the performance of a specific stock or group of stocks in order to gain a better understanding of the factors that drive their performance. We will examine historical data such as stock prices, trading volume, and financial statements, as well as external factors such as economic conditions and industry trends.
By analyzing this data, we hope to gain valuable insights that can inform investment decisions and help us make more informed predictions about future market trends.
In this notebook, we will analyze data from technology stocks such as Apple, Amazon, Google, and Microsoft. We will use yfinance to retrieve stock information and visualize it using seaborn and matplotlib. We will examine methods for assessing the risk of a stock based on past performance and use an LSTM method to predict future stock prices.
Let's get it started!
Switch to desktop for real-world practiceContinue from where you are using one of the options below