> Note
>
> *To make it easier for you to go through the project, it would be nice to know the following topics*: 
>
> - [Python basics](https://codefinity.com/courses/2f60173b-9829-4c84-8548-85bf65183f71); 
>
> - [Introduction to pandas
](https://codefinity.com/courses/a43d24b6-df61-4e11-9c90-5b36552b3437);
>
> - [Intermediate pandas](https://codefinity.com/courses/4bf24830-59ba-4418-969b-aaf8117d522e);
>
> - [Visualization in Python with matplotlib](https://codefinity.com/courses/ed80401e-2684-4bc4-a077-99d13a386ac7);
>
> - The `prophet` library.
>
> *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!

In this project, we are going to predict stock prices using ML Models.