DataHour: Understanding Logistic Regression

About the DataHour:

Logistic Regression is one of the most popular and basic algorithms used to solve a classification problem. It is named ‘Logistic Regression’ because its underlying technology is quite similar to Linear Regression. Whereas the term “Logistic” is taken from the Logit function that is used in this method of classification where the independent variables are continuous in nature and the dependent variable is in categorical form i.e. in classes like positive class and negative class.

In this DataHour, Spandan will share his enriching experience in the field of data science and answer questions like why to use the ‘Logistic Regression’ technique ahead of linear regression. We will also be exploring the logistic regression algorithm including the explanation of the standard logistic function. 

Prerequisites: A basic understanding of Python programming language and Jupyter notebook.

Who is this DataHour for?

• Students & Freshers who want to build a career in the Data-tech domain.
• Working professionals who want to transition to the Data-tech domain.
• Data science professionals who want to accelerate their career growth

Speaker:

Spandan Mitra

Senior Lead Data Scientist at Infosys

Spandan is a seasoned analytics professional with approximately 9 years of work experience in the field of business consulting, solutions development and implementation, strategy, and planning. He has also worked in several other domains like– Insurance, Banking, Retail, and Utilities. Spandan is currently working as a Senior Lead Data Scientist at Infosys BPM and has worked with some other esteemed organizations including the likes of Prudential Global Services Private Limited, HSBC, WNS Global Services and TATA Consultancy Services (TCS).

Connect with him on Linkedin