Predictive modeling & data-driven design for engineering systems to assess, analyze & validate the effectiveness & accuracy of the data using algorithms.
01 Defining a Problem
We'll specify whether to utilise classification, regression, or clustering to provide a clear picture. When you have a problem that must be handled in your business, you may decide how to approach it and what success looks like.
02 Data Processing
Once we receive the data, it is crucial to maintain it clear, clean, and in the right format for modelling. We should also add any pre-processing to make sure the data is clean and ready for modelling.
03 Data Modelling
One of the most enjoyable and interesting phases of a data science project. The format that this will take will depend on the nature of the issue and how the data was handled. Choose the models that might offer the best answer.
04 Data Modelling
Evaluating the accuracy and dependability of your model's performance on this dataset. The first is how well your model performs regarding the goal you've set, and the second is how reliably your model can achieve—or fail to achieve this goal
Choose the model's insights to change the way your business operates, whether you use the model to determine whether changes you've made were successful, or whether the model is set up somewhere to continuously receive and assess real-time data.
IIT Research Park - Amtex has tied up with reputed research institutes & organizations to bolster its Data Science application in its core offerings.
Amtex’s R&D & Data Science Ecosystem.
Dr. Gaurav Raina
Head of Data Science Application, Amtex Health.
Assoc. Professor, IIT Madras
AI & Data Science Expert,
Ph.D., Cambridge University, Srinivasa Ramanujan Fellow