Stock Price Predictor Web App

Project Overview

Welcome to the Stock Price Predictor Web App, where the synergy of machine learning and real-time data delivers captivating insights into stock market trends. Explore its innovative features for dynamic and interactive predictions of stock prices. Here's a closer look at what the app offers:

Key Features

• User-Friendly Interface: Built with Streamlit, the app offers a seamless and interactive user experience, allowing users to input their desired stock symbols and view predictions.
• Historical Data Analysis: Fetches historical stock data from Yahoo Finance, enabling users to visualize past performance.
• Real-Time Predictions: Utilizes a pre-trained LSTM (Long Short-Term Memory) model to predict future stock prices.
• Visual Insights: Provides detailed plots comparing original and predicted stock prices, along with future price projections.

How It Works

1. Input Stock Symbol: Users can enter any stock symbol (default is Bitcoin - BTC-USD).
2. Data Fetching: The app retrieves historical stock data from Yahoo Finance, dating back ten years.
3. Model Prediction: Using the LSTM model, the app predicts future stock prices based on historical data.
4. Visualization: The app generates various plots to visualize historical, predicted, and future stock prices.

Technical Details

• Streamlit: An open-source app framework for creating and sharing custom web apps for machine learning and data science.
• Keras: A powerful deep learning library used for training the LSTM model.
• Yahoo Finance API: Provides reliable and up-to-date stock market data.
• Matplotlib: A plotting library used to create static, interactive, and animated visualizations.

Visualizations

• Historical Data: Displays the historical closing prices of the selected stock.
• Predicted vs. Actual Prices: Compares the model's predictions with actual stock prices to assess accuracy.
• Future Price Predictions: Projects future stock prices based on the model's predictions.

Future Enhancements

• Model Improvements: Continuously refine the model for better accuracy and performance.
• User Feedback Integration: Incorporate user feedback to improve the app's functionality and usability.

How to Use

1. Clone the repository:

   git clone https://github.com/Surajkumar4-source/stock-price-predictor-app.git
   cd stock-price-predictor-app
   

2. Install the necessary libraries:

   pip install streamlit keras yfinance pandas streamlit numPy matplotlib datetime sklearn(Scikit-learn):
   

3.Run the app:

DEMO

Stock.Price.Predictor.1.mp4