Stock Price/Trend Prediction Web Application This is a web application that provides stock price and trend prediction using the FB Prophet tool, LSTM, and Linear Regression. The user can input the stock symbol and select the desired model, and the web app will display the predicted stock prices and trends for the next period.
Installation To run this application, you need to have Python 3 installed on your machine. You can clone this repository and install the dependencies using pip. First, navigate to the project directory and run:
bash Copy code git clone https://github.com/yourusername/stock-prediction-webapp.git cd stock-prediction-webapp pip install -r requirements.txt Usage To run the application, navigate to the project directory and run the following command:
Copy code streamlit run app.py This will open the web application in your default browser. You can enter the stock symbol and select the desired model from the dropdown list. Click on the "Predict" button to generate the predictions. The predicted prices and trends will be displayed in a line chart.
Models The web application provides three models for stock price and trend prediction:
FB Prophet LSTM Linear Regression FB Prophet Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
LSTM Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is capable of learning long-term dependencies. It has been successfully applied to various sequence prediction problems, including stock price prediction.
Linear Regression Linear regression is a simple approach for modeling the relationship between a dependent variable and one or more independent variables. It can be used for stock price prediction by modeling the relationship between the stock price and various economic and financial factors.
Data The web application uses stock price data from Yahoo Finance. It retrieves the historical prices for the selected stock symbol and uses them to train the selected model. The data is updated daily and covers the last 5 years of trading.
Acknowledgments This web application was developed by [Jaymin Shah]. It is based on the FB Prophet, Keras, and scikit-learn libraries.