QR code is the “quick response” code designed to handle a large amount of data by decoding at high speed. It can be captured by any managed device like a mobile and easily accessed by scanning the 2D matrix code. The dataset is analyzed using machine learning approaches such as the confusion matrix score used for the performance analysis of the multinomial naive Bayes algorithm. Count vectorizer is used here to convert the text into vector form and TFID vectorizer is used here to transform the text to feature vectors which can be used as the input estimator. So that the best parameter values are searched from the grid of parameters using the GridsearchCV method. Usage of QR increased rapidly in the present market by having the benefit able results for the customers like quick, error-free access and the capacity to store a large amount of data. They are beneficial for a cashless society as well, since many merchants have signed up with various e-wallet businesses like Paytm, free charge, etc. Due to these benefits, the use of QR has spread all over the world. Finally this paper provides a clear idea of shopping through QR code.

Confusion matrix ,QR code ,Count vectorizer ,GridsearchCV ,Python tkinter