stock_price_prediction

This repository contains code for predicting stock prices using Long Short-Term Memory (LSTM) neural networks.

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory". It is applicable to classification, processing and predicting data based on time series, such as in handwriting, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare.

Requirements -

1. Python 3.x
2. TensorFlow
3. Pandas
4. NumPy
5. Matplotlib

Usage

1. Clone the repository: git clone https://github.com/keshav-gupta-07/stock_price_prediction.git
2. pip install -r requirements.txt
3. After training, you can use the trained model for prediction by running the following command on your terminal: python main.ipynb