https://github.com/arya920/stockpriceforecasting
The project seamlessly melds diverse technologies, including Numpy, Seaborn, Matplotlib, Keras, and more, to seamlessly integrate data manipulation, visualization, and machine learning.
https://github.com/arya920/stockpriceforecasting
data-visualization keras-tensorflow lstm-neural-networks modelling neural-network stock-market stock-price-prediction streamlit-webapp webapp
Last synced: about 1 year ago
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The project seamlessly melds diverse technologies, including Numpy, Seaborn, Matplotlib, Keras, and more, to seamlessly integrate data manipulation, visualization, and machine learning.
- Host: GitHub
- URL: https://github.com/arya920/stockpriceforecasting
- Owner: Arya920
- Created: 2023-08-20T06:33:52.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-20T19:49:26.000Z (almost 3 years ago)
- Last Synced: 2025-01-31T12:30:35.604Z (over 1 year ago)
- Topics: data-visualization, keras-tensorflow, lstm-neural-networks, modelling, neural-network, stock-market, stock-price-prediction, streamlit-webapp, webapp
- Language: Jupyter Notebook
- Homepage: https://stockpriceforecasting.streamlit.app/
- Size: 9.32 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Stock Price Analysis & Prediction Web Application
This web application allows you to analyze and predict stock prices using various techniques and machine learning models.
## *[Direct Link:](https://stockpriceforecasting.streamlit.app/)*
## Table of Contents
- [Overview](#overview)
- [Getting Started](#getting-started)
- [Requirements](#requirements)
- [Technologies Used](#technologies-used)
- [Project Explanation](#project-explanation)
- [Contributing](#contribution)
- [Credits](#credits)
## Overview
This Streamlit web application has been developed by [Arya Chakraborty](https://www.linkedin.com/in/arya-chakraborty-95a8411b2/) & [Rituparno Das](linkedin.com/in/rituparno-das-473a01198). The application allows users to analyze historical stock data, perform technical & statistical analysis, and even predict future stock prices using deep learning models.
## Getting Started
To run this project on your local machine, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/your-username/stock-price-analysis-app.git
cd stock-price-analysis-app
2. Install the required dependencies:
```bash
pip install -r requirements.txt
3. Run the Streamlit APP:
```bash
streamlit run app.py
*Access the app in your web browser at http://localhost:8501*
## Requirements
- *`Python 3.7 or higher`*
- *`Streamlit`*
- *`Numpy`*
- *`Seaborn`*
- *`Matplotlib`*
- *`Keras`*
- *`Pandas`*
- *`datetime`*
- *`pandas_datareader`*
- *`yfinance`*
- *`PIL`*
*NS: Install these dependencies using the provided requirements.txt file*
## Technologies Used
- Streamlit: Framework for building interactive web applications using Python.
- Numpy, Seaborn, Matplotlib: Data visualization libraries for creating informative plots.
- Keras: Used for loading the LSTM model for stock price prediction.
- Pandas: Data manipulation library for handling and analyzing data.
- pandas_datareader, yfinance: Used for fetching stock data from Yahoo Finance.
- PIL: Python Imaging Library for working with images.
## Project Explanation
### The project is organized as follows:
The Stock Price Analysis & Prediction Web Application, conceived by[Arya Chakraborty](https://www.linkedin.com/in/arya-chakraborty-95a8411b2/) & [Rituparno Das](linkedin.com/in/rituparno-das-473a01198), is an innovative platform harnessing cutting-edge technologies to empower users with an enhanced understanding of stock market dynamics. By amalgamating advanced analytics, machine learning, and data visualization, the application grants users a multifaceted toolkit to analyze, forecast, and interpret stock market trends.
Built using Streamlit, a Python framework designed for creating dynamic web applications, the platform encapsulates an array of functionalities. Leveraging historical stock data, it furnishes users with an interactive dashboard to inspect trends across temporal horizons. The application's core technical prowess lies in its implementation of the Long Short-Term Memory (LSTM) neural network model, harnessed for predictive analytics. By employing deep learning techniques, it prognosticates future stock prices, empowering users to foresee potential investment opportunities.
The project seamlessly melds diverse technologies, including Numpy, Seaborn, Matplotlib, Keras, and more, to seamlessly integrate data manipulation, visualization, and machine learning. Furthermore, the platform's user-friendly interface ensures accessibility for both novice investors and seasoned analysts, enabling them to validate and refine their trading strategies with a granular understanding of market behaviors.
In summation, the Stock Price Analysis & Prediction Web Application is an exemplar of how technological innovation can amplify financial insights. By encapsulating data-driven intelligence and predictive modeling, it transcends traditional investment analysis paradigms, propelling users toward informed and confident decision-making in the complex world of finance.
## Contribution
If you're interested in contributing to this project, please feel free to fork the repository, make changes, and submit a pull request. Your contributions are greatly appreciated!
## Credits
