Your Complete Guide to Multinomial Logistic Regression, a.k.a. Softmax Regression

When it comes to real-world machine learning, around 70% of the problems are classification-based, where, on the basis of the available set of features, your model tries to predict that out of a given set of categories(discrete possible outcomes), what category does your target variable might belong to. **Today, in this article, we are going to have a look at Multinomial Logistic Regression− one of the classic supervised machine learning algorithms capable of doing multi-class classification, **i.e., predict an outcome for the target variable when there are more than 2 possible discrete classes of outcomes.

This is a project-based guide, where we will see how to code an MLR model from scratch while understanding the mathematics involved that allows the model to make predictions.

For the project, we will be working on the famous UCI Cleveland Heart Disease dataset. We will create an ML model from scratch that uses multinomial logistic regression, capable of predicting the severity of heart disease within a patient.

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