Interview Question: Implement Logistic Regression on the Heart dataset

The logistic regression algorithm is probably one of the first algorithms that a student of data science learns because of its simplicity.

Logistic regression is a supervised machine learning algorithm that accomplishes binary classification tasks by predicting the probability of an outcome, event, or observation. The model delivers a binary or dichotomous outcome limited to two possible outcomes: being yes/no, 0/1, or true/false.

It is possible to use logistic regression to determine the probability of someone having a heart attack. With the help of a logistic model, medical practitioners can determine the relationship between variables such as the weight, exercise, etc., of an individual and use it to predict whether the person will suffer from a heart attack or any other medical complication.

It is because logistic regression can be used to determine whether a person is likely to have a heart attack that such a question could very well come up at a data science interview. A typical interview question would be to implement logistic regression on the heart dataset where the dependent variable is ‘HeartDisease’ and the independent variable is ‘Age’.

The Python code below, written in Google Colab, illustrates how to implement logistic regression to…