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# Stock Price Prediction App

Welcome to the Stock Price Prediction App! This app allows you to visualize stock price data, explore technical indicators, and make short-term price predictions using machine learning models.

Created and designed by [Vikas Sharma](https://www.linkedin.com/in/vikas-sharma005/).

## Table of Contents

- [Description](#description)
- [Features](#features)
- [Setup](#setup)
- [Usage](#usage)
- [Technologies](#technologies)
- [License](#license)

## Description

The Stock Price Prediction App is a Streamlit-based web application that provides users with tools to analyze historical stock price data, visualize technical indicators, and make short-term price predictions using different machine learning models.

## Features

- **Visualize Technical Indicators**: Explore various technical indicators such as Bollinger Bands, MACD, RSI, SMA, and EMA to gain insights into stock price trends.

- **Recent Data Display**: View the most recent data of the selected stock, including the last 10 data points.

- **Price Prediction**: Predict future stock prices using machine learning models including Linear Regression, Random Forest Regressor, Extra Trees Regressor, KNeighbors Regressor, and XGBoost Regressor.

## Setup

1. Clone the repository:
   ```sh
   git clone https://github.com/vikasharma005/Stock-Price-Prediction.git
   ```

2. Navigate to the project directory:
   ```sh
   cd stock-price-prediction-app
   ```

3. Install the required Python packages using pip:
   ```sh
   pip install -r requirements.txt
   ```

## Usage

1. Run the Streamlit app:
   ```sh
   streamlit run app.py
   ```

2. The app will open in your default web browser. Use the sidebar to choose options for visualization, recent data display, or making price predictions.

3. Follow the on-screen instructions to input the stock symbol, select a date range, and choose technical indicators or prediction models.

## Technologies

- Python
- Streamlit
- pandas
- yfinance
- ta (Technical Analysis Library)
- scikit-learn
- XGBoost

  ## Author

<div id="header" align="center">
  <img src="https://media.giphy.com/media/M9gbBd9nbDrOTu1Mqx/giphy.gif" width="100"/>
</div>

<h3 align="center">Hi there 👋, I'm Vikas</h3>
<h4 align="center">Just learning New Skills😀</h4>

<div id="socials" align="center">
  <a href="https://www.linkedin.com/in/vikas-sharma005">
    <img src="https://user-images.githubusercontent.com/76098066/186728913-a66ef85f-4644-4e3a-b847-98309c8cff42.svg">
  </a>
  <a href="https://www.instagram.com/_thisisvikas">
    <img src="https://user-images.githubusercontent.com/76098066/186728908-f1a9919a-f4b2-4262-9515-683e77f8aabf.svg">
  </a>
  <a href="https://twitter.com/hitechvikas05">
    <img src="https://user-images.githubusercontent.com/76098066/186728901-a4d90f01-2cdf-45c1-a1b3-73467c3d2698.svg">
  </a>
</div>

You can find more about me and my projects on my [GitHub profile](https://github.com/vikasharma005).

## License

This project is licensed under the [MIT License](LICENSE).

---