Machine learning with big data

Machine learning, and AI(Artificial Intelligence) in general, has been the most discussed and talked about topic in business and industry for the previous several years . Where a machine, such as a computer or application, makes decisions in the same way as a human does based on previously obtained information or data.

Starting with data collection, data cleansing, data and feature engineering, model construction, and deployment, we assist our clients with end-to-end machine learning projects.

The following is an example of a typical machine learning workflow:
(Image courtesy of Amazon Web Services)


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:


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


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.