https://github.com/bhuvan588/microsoft-stock-price-prediction-using-lstm

Univariate Stock price prediction using LSTM (Long Short Term Memory).
https://github.com/bhuvan588/microsoft-stock-price-prediction-using-lstm

deep-learning keras lstm rnn stock-price-prediction tensorflow

Last synced: 5 days ago
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Univariate Stock price prediction using LSTM (Long Short Term Memory).

• Host: GitHub
• URL: https://github.com/bhuvan588/microsoft-stock-price-prediction-using-lstm
• Owner: Bhuvan588
• Created: 2024-07-29T13:52:15.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-07-29T15:22:05.000Z (3 months ago)
• Last Synced: 2024-10-10T08:23:12.400Z (29 days ago)
• Topics: deep-learning, keras, lstm, rnn, stock-price-prediction, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 357 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Microsoft-Stock-Price-prediction-using-LSTM

In this mini project I explored LSTM (Long Short Term Memory) and its use cases . The aim was to predict stock prices for a company's stock (here Micosoft)

![image](https://github.com/user-attachments/assets/298b5cc6-c561-4e3e-a3fc-f220786a14e5)

## Project Need

LSTM can be used to predict stock prices, classify fake news and even generate text.

A short overview about LSTM

LSTM was used to overcome the vanishing gradients problem of RNN which meant RNN couldn't capture the context of any word for long sentences. 

Even after using DropOut and BatchNormalization the problem couldn't be solved so LSTM was proposed. 

It uses 3 types of gates - forget, input and output for deciding what info must be forgot, what must info must be added to cell state and what will be the output respectively.