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description

Microsoft-stock-prediction. Apply the deep learning RNN models to predict series data, buidling a web-application to predict input data and you can also create your own model for predict your custom dataset .In this application the values in previous time become the input for next prediction.

msft_yahoo

  • Application functional hierarchy diagram

app_func_diagram

  • Appication apidocs

apidocs

  • You can also test your request with swagger-flassger apidocs GUI

post_form

  • Get the 5 lastest day's values

predict_gif

✳ Notes-machine_learning

description default value
input dataset X input of model (number_of_samples,seq_len,input_dim) (545,5,1)
label dataset Y label of these input (number_of_samples,output_dim) (545,1)
number_of_samples how many sample in your dataset () 545
seq_len how many previous samples you want to look back 5
input_dim your input dimension 1
output_dim your output dimension 1

✳ Notes-application

  • run this app: python app.py

  • check api docs: http://127.0.0.1:5000/apidocs/

release-note

version description date
v0.1 . building model ☑
. predict function ☑
. flasgger-apidocs ☑
June,14th,22
v0.2 . train your own model ❌ ...

tools

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1 sublime-text code editor
2 jupyter-notebook build machine learning model
3 diagrameditor drawing UML
4 pixlr design banner
5 github cloud platform for manage your project
6 git control your repository
7 ezgif ezgif maker
8 symbolcopy get sympol character
9 shields create your own git badges

references

Stock Price Prediction & Forecasting with LSTM Neural Networks in Python

Stock Price Prediction & Forecasting with LSTM Neural Networks in Python-colab

LSTM Time Series Forecasting Tutorial in Python

Flask Application for Uploading Excel/CSV Files

Uploading CSV/Excel file and Obtaining Plots inside Python Flask

Upload CSV File with SQLite Database Using Flask | Tamil

Predicting the Stock Market with Machine Learning (Part 1) - Data Preparation