https://github.com/prakashjha1/stock-trend-prediction

Deep learning for predicting stock market prices and trends has become even more popular than before. I have used yahoo finance to collect the data and LSTM to build the stock trend model.
https://github.com/prakashjha1/stock-trend-prediction

deep-learning keras lstm lstm-neural-networks machine-learning numpy pandas pandas-datareader prediction python rnn tensorflow

Last synced: 2 days ago
JSON representation

Deep learning for predicting stock market prices and trends has become even more popular than before. I have used yahoo finance to collect the data and LSTM to build the stock trend model.

• Host: GitHub
• URL: https://github.com/prakashjha1/stock-trend-prediction
• Owner: prakashjha1
• Created: 2022-10-19T14:52:28.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-19T15:19:29.000Z (about 2 years ago)
• Last Synced: 2023-09-16T10:42:23.126Z (about 1 year ago)
• Topics: deep-learning, keras, lstm, lstm-neural-networks, machine-learning, numpy, pandas, pandas-datareader, prediction, python, rnn, tensorflow
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Stock-Trend-Prediction

Deep learning for predicting stock market prices and trends has become even more popular than before. I have used yahoo finance to collect the data and LSTM to build the stock trend model. I have streamlit to build web UI. 

# Python Modules used in this project

- numpy

- pandas

- matplotlib

- pandas_datareader

- scikit-learn

- tensorflow

- datetime

- streamlit

# Steps to train the Model

- fetch the data from start date to end date.

- choose the target variable (High or Low or Open or Close)

- create dataframe with input and variable. 

- input is previous 10 days or 5 days stock price based on user choice.

- output is next day stock price.

- split the data into training and testing set.

- Scale the data input data.

- Build the LSTM model using keras module of tensorflow.

- Train the model using training data set.

- Save the model.

# Step for Prediction

- Take user input (previous 10 days or 5 days stock price data).

- Scale the data.

- Load Saved model.

- Predict the Ouput