Training a process where we create a model using our datasets. Every prediction query goes to Model, not to the dataset.
Let’s assume we have 100 records. We are giving 70% of data to our machine as the Training data and rest of the 30% data as the test data.
Got confused what is train and test data. So let’s assume you are a student and your teacher have a book with 100 questions with answers. But he taught you 70 questions and answers.
After that, the teacher asked you rest of the 30 question and you gave the answer as 80% correctly. That is your accuracy.
Same with the machine we need to check the accuracy of our ML also. Now we also have 80% accuracy with the machine. (Why 80%? , it depends on data and how deep we trained our model)
Now we give a record to our machine. So the machine will put this record in prediction model and will provide you predicted the output.
Same with you, Now teacher asked a new question and you replied the answer basis on the 70 questions and that 80% accuracy, which you got from 30 questions.
So machine learning is all about data and data.