Thesis using logistic regression

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Learn about Springboard. Data analytics professionals form the backbone of decision making at every organisation. One of the most common problems any data professional solves in their career lies around determining the probability of an event occurring. With the information available, analysts often perform Logistic Regression techniques to help determine the most possible outcome. In this article, we will be taking a real life example of a situation that automobile companies like Hero Moto Corp, Bajaj Auto, Royal Enfield etc would be facing.

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Logistic Regression: Scikit Learn vs Statsmodels - Cross Validated

Prerequisite: Understanding Logistic Regression Logistic regression is the type of regression analysis used to find the probability of a certain event occurring. It is the best suited type of regression for cases where we have a categorical dependent variable which can take only discrete values. The dataset : In this article, we will predict whether a student will be admitted to a particular college, based on their gmat, gpa scores and work experience. The dependent variable here is a Binary Logistic variable , which is expected to take strictly one of two forms i. Builiding the Logistic Regression model : Statsmodels is a Python module which provides various functions for estimating different statistical models and performing statistical tests. Logit ytrain, Xtrain. Skip to content.

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A binomial logistic regression often referred to simply as logistic regression , predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable based on one or more independent variables that can be either continuous or categorical. If, on the other hand, your dependent variable is a count, see our Poisson regression guide. Alternatively, if you have more than two categories of the dependent variable, see our multinomial logistic regression guide.

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