In machine learning, while building a predictive model we follow several different steps. We first do exploratory data analysis to understand the data well and do the required preprocessing. After the data gets ready we do modelling and develop a predictive model. This model is then used to compute prediction on the testing data and the results are evaluated using different error metrics. But what to do next? Do you know how you can use this model and check real-time predictions? It is said you can validate the model performance when you compute prediction in real-time. As we have already seen how we can do model deployment using flask.

In this article, we will be exploring Tkinter – python GUI programming tool. We will explore how we can deploy a machine learning model and check real-time predictions using Tkinter. For this experiment, we will be using the Pima Indians Diabetes Data set that is available on Kaggle. We will first build a classification model that will classify whether a patient is diabetic or not. Then we will make a GUI using Tkinter and will check predictions on new data points.

#developers corner #machine learning #model deployment #tkinter #tkinter gui