The aim of this article is to fit and interpret a Multiple Linear Regression and Binary Logistic Regression using Statsmodels python package similar to statistical programming language R.
The dataset contains several parameters which are considered important during the application for Masters Programs. The parameters included are as follows:
I: independent variable; O: outcome variable
This dataset was built with the purpose of helping students in shortlisting universities with their profiles . The predicted output gives them a fair idea about their chances for a particular university.
Dataset Link: https://www.kaggle.com/mohansacharya/graduate-admissions
The aim of this article is to fit and interpret a Multiple Linear Regression and Binary Logistic Regression using Statsmodels python package similar to statistical programming language R. Here we will predict student admission in masters’ degree. Additionally, we will learn how we could interpret the coefficients obtained from both modelling approaches.
Linear Regression VS Logistic Regression (MACHINE LEARNING). Linear Regression and Logistic Regression are two algorithms of machine learning and these are mostly used in the data science field.
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Logistic Regression is a type of supervised learning problem where the output values are discrete i.e. the output is a fixed number of classes.