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Stock Market Prediction Web App Using Machine Learning And Sentiment Analysis

A Python web app that forecasts NASDAQ and NSE stock prices for seven days using ARIMA, LSTM and linear regression, then combines the forecasts with tweet sentiment to recommend whether prices may rise or fall.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stock Market Prediction Web App Using Machine Learning And Sentiment Analysis — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to explore a combined forecast-and-sentiment workflow for NASDAQ or NSE stocks, or to examine a codebase for a financial research prototype.

Who it is for

  • People exploring stock-analysis tools that combine price forecasts with tweet sentiment.
  • Developers reviewing or adapting an MIT-licensed Python stock-analysis web app.

Documented uses

  • Enter a NASDAQ or NSE stock and obtain a seven-day price forecast.
  • View a directional recommendation that combines forecasts with sentiment analysis of tweets.
  • Review project code and follow its documented local WordPress and Python setup.
Before you choose

Requirements & limits

The project is a public GitHub repository identified as MIT licensed. Its README documents local installation and user registration and login.

  • Forecast horizon is seven days, and the described stock inputs cover NASDAQ and NSE.
  • The recommendation combines forecasts from three named algorithms with tweet sentiment.
  • The documented local setup uses XAMPP, WordPress, a database import and Python dependencies.
  • The project description notes that API keys are included in the code.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the app locally

Prepare the documented WordPress and Python environment and open the app at the local WordPress address.

Before you start

  • XAMPP server
  • WordPress zip folder
  • PHPMyAdmin credentials
  • The project repository
  • Python and its pip package installer

Steps

  1. Install XAMPP and extract the WordPress zip folder into its htdocs directory.

  2. Edit wp-config.php with the PHPMyAdmin username and password, then create a database named wordpress.

  3. Import wordpress.sql into that database.

  4. Clone the repository, enter its directory, install requirements with pip install -r requirements.txt, and run python main.py.

  5. Open localhost/wordpress.

Expected outcome

The documented local setup sequence leads to the app at localhost/wordpress.

Guide limitations

  • The README does not provide a troubleshooting procedure.
  • The project description says API keys are included in the code.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
5 November 2026
Original website title
kaushikjadhav01/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis · Official homepage title · retained in its source language
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