Nothing has a certainty of being always correct, but certainty can be estimated. The science of estimating certainty is called Data Science. Actually it’s quite
difficult to pin down a specific definition of data science. But, it is easy to see and feel it’s impact. It’s application in different fields can lead to incredible insights.
Data is not the fuel, it’s the GPS that gets company going. In today’s competitive
world, high profit margins demand quick reactions to customer demands. A
company that is spread all across the globe will have to figure out patterns in it’s
customers behavior to react quickly to the changing market situations. Now, to
deal with data of this volume, a very strong system which is capable of handling
data from all demographics, domains and most importantly of all types is needed.
How we bring solutions and insight to your company:
- We study your data using latest analytics and machine learning models
- We provide you an inference model
- We retest multiple times until you are satisfied with the cost value proposition
- We set up meeting with companies to understand business strategies and work procedures
- We understand your business problems and risks related to it
- We carry out brainstorming sessions to figure out the root cause of the problem.
- According to the problem our team of Data Science Engineers ask for Data related to businesses.
The initial meeting between us consists of the protection and understanding of your data and business
We prepare your data for model ingestion
We find the latest artificial intelligence models to give you powerful inferential and pattern analysis
Cost value proposition is attached to the model
We repeat steps 1 - 4 until the best cost value proposition is given to your company via statement of work
Training and Testing Accuracies
After the model is developed and train and test accuracy results are out, it could be validated based on difference between Train accuracy and Test accuracy. We understand this, and make sure our models are not overfitted or underfitted.
Our assessment and validation metrics to delivery high quality model
Sensitivity is to check how sensitive the model is to the ‘Real world data’. This is crucial because real world data is often messed up and has enormous randomness in it. Therefore, depending on the domain of project it becomes important to control the sensitivity of the model.
Specificity is nothing but True negative rate. So, depending on the hypothesis we are trying to prove it is important to decide whether, Specificity is vital for project or sensitivity is vital for project.