DeepMarket Insight: Indian Stock Market Predictor

DeepMarket Insight is a Streamlit-based web application designed to predict trends in the Indian stock market using basic machine learning techniques. The application leverages historical stock data to provide predictions for future stock prices.

Features

• Real-time Stock Data: Fetches up-to-date stock data using the Yahoo Finance API.
• Customizable Date Range: Users can select the date range for analysis.
• Visualization: Displays historical closing prices of the selected stock.
• Machine Learning Prediction: Provides a simple linear regression model to predict the future stock price for the next 30 days.
• Interactive Interface: Users can easily select stocks, choose date ranges, and visualize both historical data and predictions.

Installation

To run this application locally, follow these steps:

Prerequisites

Make sure you have the following installed:

• Python 3.7 or higher
• pip (Python package manager)

Installation Steps

1. Clone the repository:

   git clone https://github.com/yourusername/deepmarket-insight.git
   cd deepmarket-insight

2. Install the required Python packages:

   pip install -r requirements.txt

3. Run the Streamlit application:

   streamlit run app.py

Usage

1. Stock Selection:

   • Enter the stock ticker symbol in the sidebar (e.g., RELIANCE.NS for Reliance Industries).
   • Default stock symbol is set to RELIANCE.NS.

2. Select Date Range:

   • Choose the start and end dates for which you want to analyze the stock data.
   • The default start date is set to 2023-01-01, and the end date is set to today's date.

3. View Stock Data:

   • The application will display the most recent stock data in a table format.

4. Visualize Stock Trend:

   • A plot of the stock's closing prices over the selected date range will be displayed.

5. Predict Future Prices:

   • Click the "Predict Future Price" button to generate and display predictions for the next 30 days based on a linear regression model.

File Structure

• app.py: Main application file containing the Streamlit code.
• requirements.txt: Lists the Python packages required to run the application.
• README.md: This file, providing an overview of the project.

Dependencies

The following Python libraries are required to run this application:

• streamlit: For creating the web interface.
• yfinance: To fetch stock data from Yahoo Finance.
• pandas: For data manipulation and analysis.
• numpy: For numerical operations.
• matplotlib: For plotting data.
• scikit-learn: For the linear regression model.

Future Improvements

• Advanced Machine Learning Models:
  Incorporate more sophisticated models like LSTM or ARIMA to enhance prediction accuracy.

• Multiple Stock Comparison:
  Enable users to compare predictions for multiple stocks simultaneously.

• Technical Indicators:
  Add support for technical analysis indicators such as RSI, MACD, and Bollinger Bands.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.