Python has evolved to become engineers’ choice for predictive modeling. It is open source, easy to learn, has a robust community, and has many libraries for data mining and deep learning tasks. One of the most common predictive model algorithms used is logistic regression. Performing logistic regression in Python is easy and scalable. In this article, you will learn how to build an end-to-end logistic regression model in Python.
Logistic Regression is an algorithm that works in a supervised learning setup where it solves binary classification problems. Learn about the types, purpose, and how logistic regression functions with examples and use cases.
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Data science and the use of predictive models have impacted many industries. One such industry is finance. Credit Bureaus, banks, and other institutions involved in money lending have developed credit scoring models.
Embarking on a journey into data science, particularly in the dynamic realms of machine learning and deep learning, introduces us to many new concepts and terminology. Gripping the essence of various terms, among which “parameters” hold significant importance, is fundamental to this exploration.
Today’s digital world is based on advancements in mathematics, computer science, data science, and artificial intelligence. Almost everything involving technology, from search engines to self-driving cars, depends on these disciplines. However, algorithms are at the core of these fields.
