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stock-market-prediction-web-app-using-machine-learning-and-sentiment-analysis

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

https://github.com/kaushikjadhav01/stock-market-prediction-web-app-using-machine-learning-and-sentiment-analysis

Science Score: 67.0%

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    DOI references
    Found 1 DOI reference(s) in README
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    Links to: scholar.google, zenodo.org
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    Low similarity (10.2%) to scientific vocabulary

Keywords

alphavantage arima flask keras linear-regression lstm machine-learning python sentiment-analysis stock-market stock-price-prediction tensorflow tweepy twitter wordpress yfinance
Last synced: 6 months ago · JSON representation ·

Repository

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Basic Info
  • Host: GitHub
  • Owner: kaushikjadhav01
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 36.7 MB
Statistics
  • Stars: 751
  • Watchers: 28
  • Forks: 236
  • Open Issues: 26
  • Releases: 1
Topics
alphavantage arima flask keras linear-regression lstm machine-learning python sentiment-analysis stock-market stock-price-prediction tensorflow tweepy twitter wordpress yfinance
Created over 5 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

DOI License Code Coverage GitHub contributors Documentation Status GitHub release (latest by date) GitHub issues GitHub closed issues GitHub Repo Size GitHub last commit GitHub language count Commit Acitivity Code Size GitHub forks GitHub stars GitHub watchers

Stock-Market-Prediction-Web-App-using-Machine-Learning

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall.

Table of Contents
  1. System Description and Functions
  2. Built With
  3. Installation
  4. Authors
  5. Links

System Description and Functions

Demo Video:


Screenshots:

Admin Creds:
Username: admin
Email Address: stockpredictorapp@gmail.com
Password: Samplepass@123

There are two roles in the system: Admin and User.

Users can:

  1. Register and Login
  2. Check Real Time stock prices
  3. Read recent news about different stocks
  4. Currency Converter
  5. Edit or delete their own profile
  6. Educate the user about stocks
  7. Download list of stock tickers
  8. Predict Stock prices for the next 7 days for all NASDAQ and NSE stocks

Admin can:

  1. Create, Retrieve, Update Delete Users.
  2. Manually trigger emails.
  3. Register and Login
  4. Check Real Time stock prices
  5. Read recent news about different stocks
  6. Currency Converter
  7. Edit or delete their own profile
  8. Educate the user about stocks
  9. Download list of stock tickers
  10. Predict Stock prices for the next 7 days for all NASDAQ and NSE stocks

Built With

Python Javascript django nodejs react html css bootstrap jquery wordpress Keras Numpy pandas

Installation

  1. Install XAMPP server
  2. Download wordpress zip folder from this link.
  3. Extract the downloaded zip into htdocs folder of XAMPP.
  4. Open the wp-config.php file from the extracted folder and add your phpmyadmin username and password.
  5. Go to phpmyadmin, create a new database called wordpress.
  6. Select this database, go to Operations tab and Import the wordpress.sql file into this created databse.
  7. Clone the repo, cd into it
  8. Run pip install -r requirements.txt
  9. Run python main.py to start server.
  10. Go to localhost/wordpress to access the app.

Find more screenshots in the screenshots folder Or click here

Authors

Kaushik Jadhav

  • Github: https://github.com/kaushikjadhav01
  • Medium: https://medium.com/@kaushikjadhav01
  • LinkedIn: https://www.linkedin.com/in/kaushikjadhav01/
  • Portfolio: http://kajadhav.me/
  • Linked In: https://www.linkedin.com/in/kajadhav/
  • Dev.to: https://dev.to/kaushikjadhav01
  • Codesignal: https://app.codesignal.com/profile/kaushik_j_vtc
  • Google Scholar: https://scholar.google.com/citations?user=iRYcFi0AAAAJ
  • Daily.dev: https://app.daily.dev/kaushikjadhav01
  • Google devs: https://developers.google.com/profile/u/kaushikjadhav01
  • Stack Overflow: https://stackoverflow.com/users/21890981/kaushik-jadhav

Links

Owner

  • Name: Kaushik Jadhav
  • Login: kaushikjadhav01
  • Kind: user
  • Location: Raleigh, North Carolina, USA
  • Company: @microsoft, @ncstate-university, @browserstack

Incoming Cloud & AI SWE Intern @microsoft | MS CS Fall 2022 @ncstate-university | Ex-Software Engineer @browserstack | Applying my engineering skills to solve

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Jadhav"
  given-names: "Kaushik"
orcid: "https://orcid.org/0000-0000-0000-0000"
title: "Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis"
version: 2.0.0
doi: 10.5281/zenodo.10498988
date-released: 2023-03-29
url: "https://github.com/kaushikjadhav01/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis"

GitHub Events

Total
  • Issues event: 3
  • Watch event: 156
  • Pull request event: 1
  • Fork event: 45
Last Year
  • Issues event: 3
  • Watch event: 156
  • Pull request event: 1
  • Fork event: 45

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Rohith-kumar42 (1)
  • gil5033 (1)
  • vishwambhar2 (1)
Pull Request Authors
  • 23Madhavii (1)
  • Hemanthlakkakula2053 (1)
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Dependencies

requirements.txt pypi
  • alpha_vantage ==2.3.1
  • flask ==1.1.2
  • keras ==2.4.3
  • matplotlib ==3.2
  • nltk ==3.5
  • numpy ==1.19.5
  • pandas ==1.2.2
  • scikit_learn ==0.24.1
  • seaborn ==0.11.1
  • statsmodels ==0.12.2
  • streamlit ==0.52.1
  • tensorflow ==2.4.1
  • textblob ==0.15.3
  • tweepy ==3.10.0
  • yfinance ==0.1.54