Stock market analysis based on ML/AI

Similarity analysis "finance, real estate, etc..."

Setup

pip install networkx pip install -U pip python3 -m pip install streamlit

Run

streamlit run app_streamlit.py

Debug

/Library/Frameworks/Python.framework/Versions/3.8/Resources/Python.app/Contents/MacOS/Python /Library/Frameworks/Python.framework/Versions/3.8/bin/streamlit run app_streamlit.py

How to build docker

1. build base docker image with all dependencies docker build --target ml_similarity_base -t ml-similarity-base:latest .
2. Build container with app docker build --target ml_similarity_app -t ml_similarity_app:latest .

With docker-compose

docker-compose up docker-compose stop docker-compose up -d --build

Run the Docker container using the command shown below.

docker run -p 8501:8501 ml_similarity_app http://localhost:8501/

Docker run and attach into container

docker run --entrypoint "/bin/sh" -it ml-similarity-base

Docker attach

https://phase2.github.io/devtools/common-tasks/ssh-into-a-container/ docker exec -it /bin/bash

Remove

1. images: docker rmi
2. containers: docker rm $(docker ps -aq) docker system prune

Setup python

ls -l /usr/local/bin/python*

MARKS

https://kanoki.org/2020/07/04/create-interactive-dashboard-in-python-using-streamlit/

STEPS

1. Similarities detection
2. Features importance detection https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e#:~:text=using%20SelectKBest%20class-,2.,feature%20towards%20your%20output%20variable. HEATMAP - (or heatmap) is a data visualization technique that shows magnitude of a phenomenon as color in two dimensions.

Feature engineering

https://www.kaggle.com/willkoehrsen/start-here-a-gentle-introduction https://towardsdatascience.com/formatting-tips-for-correlation-heatmaps-in-seaborn-4478ef15d87f