Stock Price Prediction Application

Overview

This Streamlit application predicts stock prices using a pre-trained LSTM model. Users can input a stock symbol, select a date range, and choose the number of days to forecast. The app will fetch historical stock data, make predictions, and display the results.

App Link

Click here for the app: Stock Price Prediction App

Features

• Predict Stock Price:

  • Input a stock symbol (e.g., AAPL for Apple).
  • Select a date range for historical data.
  • Choose the number of days for which to forecast stock prices.
  • The app fetches historical data, makes predictions using an LSTM model, and displays both actual and predicted prices.
  • Provides a side-by-side view of the predicted prices and the stock’s logo.
  • Displays a plot comparing actual vs predicted prices.

• About:

  • Provides information about the application and its functionality.

Code Explanation

Page Configuration

Sets the title of the web page to "Stock Price Prediction Application."

Model Loading

Loads the pre-trained LSTM model from a lstm_model.pkl file using joblib.

Function to Get Stock Image URL

A placeholder function that should return the image URL for a given stock symbol. This function needs to be implemented based on the actual image source.

Streamlit Application Main Function

1. Title: Displays the title of the app.

2. Sidebar Navigation:

   • Uses the streamlit_option_menu to create a sidebar with options for "Predict Stock Price" and "About."

3. Predict Stock Price:

   • Inputs:
     • ticker: Enter the stock symbol.
     • start_date and end_date: Choose the date range for fetching historical data.
     • forecast_periods: Slider to select the number of days to forecast.
   • Data Fetching:
     • Retrieves historical stock data using the yfinance library.
   • Data Preparation:
     • Scales the closing prices and prepares the data for prediction.
   • Forecasting:
     • Uses the LSTM model to forecast future stock prices.
     • Inverse scales the predicted prices to their original scale.
   • Display Results:
     • Shows a table with predicted prices and dates.
     • Displays the stock logo (if implemented) and the predicted prices in a Plotly chart.

4. About Section:

   • Provides a brief description of the app's purpose and usage.

Requirements

• Streamlit
• Pandas
• NumPy
• Plotly
• yFinance
• joblib
• scikit-learn
• datetime
• streamlit_option_menu

Ensure all required packages are installed before running the app.

---

Feel free to customize this README to better fit your project's needs or provide additional details about the app.