• About the Project.
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact

• Python
• matplotlib
• sklearn
• flask

├── SVR
├── linear_regression
├── random_forests
├── keras
├── KNN
├── decision_trees
├── elastic_net
├── LSTM_model

1. Clone the repo

git clone https://github.com/Zeeshanahmad4/Stock-Prices-Prediction-ML-Flask-Dashboard.git

2. Install python packages

pip install matplotlib
pip install sklearn
pip install flask
pip install KNN

This program predicts the price of GOOG stock for a specific day using the Machine Learning algorithm called Support Vector Regression (SVR) Linear Regression. Importing flask module in the project is mandatory An object of Flask class is our WSGI application.

├── app.py
├── GOOG_30_days.csv
├── train_models.py
├── utils.py
├── GOOG_30_days.csv

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.