This project analyzes and predicts stock prices of technology companies (Apple, Amazon, Google, Microsoft) using historical stock data. It uses yfinance for data retrieval, Seaborn and Matplotlib for visualization, and LSTM neural networks for prediction.

The first step is to get the data and load it to memory. We will get our stock data from the Yahoo Finance website. Yahoo Finance is a rich resource of financial market data and tools to find compelling investments. To get the data from Yahoo Finance, we will be using yfinance library which offers a threaded and Pythonic way to download market data from Yahoo. Check this article to learn more about yfinance: Reliably download historical market data from with Python

Analyze stock price changes over time.

Calculate and visualize average daily returns.

Determine moving averages.

Analyze the correlation between different stocks.

Assess Value at Risk (VaR).

Predict future stock prices using LSTM.

Clone the repository: git clone https://github.com/ahmedburale/stock-market-analysis-prediction.git