Stock Price Analysis with RNN LSTMs

Overview This repository contains a deep learning project focused on predicting stock prices for prominent companies such as Apple, Google, and Amazon. The project leverages Recurrent Neural Networks (RNN) with Long Short-Term Memory networks (LSTMs) to model the temporal dependencies in stock price data and make future price predictions.

Key Features

Stock Data: The project utilizes historical stock price data for companies including Apple, Google, and Amazon.

RNN LSTMs: We implement deep learning models based on RNN LSTMs to capture patterns and trends in stock prices.

Data Preprocessing: Rigorous data preprocessing techniques have been applied to clean and prepare the stock price data for model training.

Technologies Used:

Programming Language: Python Libraries: TensorFlow, Keras, Pandas, NumPy, Matplotlib, yfinance (for fetching stock data) Model Architecture: Recurrent Neural Networks (RNN) with Long Short-Term Memory networks (LSTMs).