Stock Price Prediction Project:

Machine learning has significant applications in the stock price prediction. In this machine learning project, we will be talking about predicting the returns on stocks. This is a very complex task and has uncertainties(This project is inspired from one of my previous project:https://github.com/subhamChakraborty23/NSE_Stock_Analysis_and_price_prediction_usin_XGboost). We will develop this project into two parts:

• First, we will predict stock price using the LSTM neural network.
• Then we will build a dashboard using Plotly dash for stock analysis.

Datasets:

• To build the stock price prediction model, we will use the NSE TATA GLOBAL dataset. This is a dataset of Tata Beverages from Tata Global Beverages Limited, National Stock Exchange of India: Tata Global Dataset
• To develop the dashboard for stock analysis we will use another stock dataset with multiple stocks like Apple, Microsoft, Facebook: Stocks Dataset.

Dependencies:

• Keras
• Plotly
• Tensorflow
• Scikit learn
• Pandas,Numpy,Matplotlib
• Dash(for dashboard creation)

Run this app with stock_app.py and

visit http://127.0.0.1:8050/ in your web browser.

Observations:

References:

• https://keras.io/api/layers/recurrent_layers/lstm
• https://dash.plotly.com/layout
• https://nsepy.readthedocs.io/en/latest/
• https://towardsdatascience.com/lstm-for-google-stock-price-prediction-e35f5cc84165
• https://towardsdatascience.com/predicting-stock-price-with-lstm-13af86a74944