The Classes in Python are the main tool used for Object-Oriented Programming (OOP). A Class is a coding structure and a specific tool to implement new kinds of objects in Python that supports inheritance. OOP offers a very different and effective way of programming. We can use OOP for Machine Learning also to build Models.

The use of OOP is entirely optional in Machine Learning as we already have libraries like Scikit-learn and TensorFlow from where we can easily use algorithms. So learning Object-Oriented Programming for Machine Learning is not that necessary, but as a programmer, you should not limit yourself. So in this article, I will take you through how you can use OOP for Machine Learning to build models.

Object-Oriented Programming (OOP) for Machine Learning

To illustrate the process of using OOP for Machine Learning, I will use a dataset which is based on Weather, can be easily downloaded from here. As we are using Object Oriented Programming so we don’t need any of the machine learning packages except pandas, which we will use to read the data, and Scikit-Learn to use a Machine Learning Algorithm and Numpy for Numerical Python. So let’s import pandas and get started with OOP for Machine Learning.

#by aman kharwal