Introduction

Welcome to Stock Price Prediction with Machine Learning! My website, powered by linear regression and a Django App, provides real-time data of stock prices on the home page. To predict stock prices, simply navigate to the prediction page, enter a valid ticker value and the number of days you want to predict, and click the predict button. This page displays the predicted stock price along with the details of the searched ticker. We also generate a unique QR Code for easy access to the predicted results. On the prediction page, you'll find two graphs: the left graph shows the real-time stock price of the searched ticker for the past day, while the right graph displays the predicted stock price for the specified number of days. Additionally, our Ticker Info page provides comprehensive details about all the valid tickers accepted by the application.

Aim

Title: Stock Price Prediction with Machine Learning

Aim: To predict stock prices according to real-time data values fetched from API.

Objective

Develop a web application for stock price prediction based on real-time data.

Scope

The project is applicable to any business organization, providing users with stock price prediction capabilities and comprehensive summary data.

Technology Used

• Languages: HTML, CSS, JavaScript, Python
• Framework: Bootstrap, Django
• Machine Learning Algorithms: Multiple Linear Regression
• ML/DL Libraries: NumPy, Pandas, scikit-learn
• Database: SQLite
• APIs: Yahoo Finance API, REST API
• IDE: VS Code, Jupyter Notebook

Project Installation:

STEP 1: Clone the repository from GitHub.

  git clone https://github.com/vishal815/-Stock-market-Prediction-with-Machine-Learning-Django.git

STEP 2: Change the directory to the repository.

  cd FolderName

STEP 3: Create a virtual environment (For Windows)

  python -m venv virtualenv

STEP 4: Activate the virtual environment. (For Windows)

  virtualenv\Scripts\activate

STEP 5: Install the dependencies.

  pip install -r requirements.txt          (already text attached in the project)

STEP 6: Migrate the Django project. (For Windows)

  python manage.py migrate

STEP 7: Run the application. (For Windows)

  python manage.py runserver

Output Screen-shots:

Home page displaying real-time data of stock prices

Prediction page where users enter valid ticker value and number of days

Prediction page displaying predicted stock price, ticker details, and QR Code.

Left graph shows real-time stock price for past day, right graph shows predicted stock price for specified days

Ticker Info page displaying details of valid tickers

Overview of code section

Conclusion:

Our Stock Price Prediction with Machine Learning website, utilizing linear regression and Django, enables users to predict stock prices based on real-time data. With easy-to-use interfaces and insightful graphs, users can make informed investment decisions. We provide comprehensive ticker information and ensure accurate predictions through our machine learning algorithms.

Report PDF of Project.

Final Report.pdf

Thank you!