Machine learning with big data

The most discussed and talked subject for the last few years in the business and industries is Machine learning and in broad aspect is AI(Artificial Intelligence). Where machine like computer/application make decision like human based on the past collected info/data.

We help our clients to do end-to-end machine learning projects, starting from data collection, data cleaning, data and feature engineering, model development and implementation.

A typical machine learning workflow: (Image source: AWS)

Machine learning process

Examples- “Steamflow Predcition”

On the first example we demonstrate an application which predicts the steamflow(water usage) by ton/hour unit to make a full paper roll in a paper machine.

Used tools:

Model:

for model building we have used XGBoost, which was best among linear regression, Random forest while texting.

Deployment:

We have used Jupyter Notebook, streamlit for developing the application(a python library for RAD of data science and ML), SHAP(SHapley Additive exPlanations) for explainability of the ML Model.  Hosting it in a Streamlit hosting server.

Link: https://share.streamlit.io/rickystanley76/bth-ml-with-streaming-data/main/app.py