https://github.com/nneji123/stock-price-predictor-web-app-with-flask

A simple web app to predict stock prices(USD) made with Flask(python).
https://github.com/nneji123/stock-price-predictor-web-app-with-flask

Last synced: 5 months ago
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A simple web app to predict stock prices(USD) made with Flask(python).

• Host: GitHub
• URL: https://github.com/nneji123/stock-price-predictor-web-app-with-flask
• Owner: Nneji123
• License: mit
• Created: 2022-05-26T15:26:44.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-06-06T11:03:38.000Z (over 3 years ago)
• Last Synced: 2025-05-04T21:57:11.189Z (5 months ago)
• Language: Jupyter Notebook
• Size: 624 KB
• Stars: 6
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Stock Price Predictor Web App 

[![Language](https://img.shields.io/badge/HTML-black.svg?style=flat&logo=html5&logoColor=white)](https://www.html.com)

[![Language](https://img.shields.io/badge/CSS-red.svg?style=flat&logo=css3&logoColor=white)](https://www.css.org)

[![Language](https://img.shields.io/badge/Python-yellow.svg?style=flat&logo=python&logoColor=white )](https://www.python.org)

[![Framework](https://img.shields.io/badge/Flask-brightgreen.svg?style=flat&logo=Flask&logoColor=white )](http://www.pygame.org/news.html)

![hosted](https://img.shields.io/badge/Heroku-430098?style=flat&logo=heroku&logoColor=white)

![build](https://img.shields.io/badge/build-passing-brightgreen.svg?style=flat)

In this project I developed a machine learning model and a web app with flask for the backend and html, css for the frontend to predict stock prices. 

![stock gif](https://user-images.githubusercontent.com/101701760/170535722-7ea40c38-3d72-4a92-b9aa-ae4f7b7d7a76.gif)

## Data

The data used was gotten from Yahoo Finance using the yfinance api. To use the api run the following:

```py

pip install yfinance

import yfinance as yf

data = yf.download("Name of Stock","Starting date", "Ending Date", auto_adjust = true)

```

- The name of stock I used was USD

- Starting date: 2010-01-01

- Closing date: 2022-04-30

## Algorithm Used

In this project I tested 3 different algorithms namely:

1. Linear Regression

2. Lasso Regression

3. Ridge Regression

4. Support Vector Machine

The final model used for the flask app was the linear regression model which had a r2 score of 0.999.

## Requirements

To run a demo do the following:

1. Clone the repository.

2. Install the requirements from the requirements.txt file:

```

pip install -r requirements.txt

```

3. Then from your command line run:

```

python app.py

```

Then you can view the site on your local server: http://127.0.0.1:5000/ 

## Deployment

The site can be deployed to heroku and can also be viewed here: https://stock-price-predictors.herokuapp.com/