In every algorithm of machine learning, there is an approach that is unique yet easily interpretable. Logistic regression is one such algorithm with an easy and unique approach. It is very often used in the credit and risk industry for its easy intuition on predicting the chances of default and risk cases. It is indeed quite a challenge to break down most of the algorithms due to their black-box nature and their hard to find parameters, but logistic regression outperforms all. So it is time to break down the entire algorithm and draw some inferences.

In this section, we will try to implement a very simple example of logisitic regression. For this example, we will try to predict the result(pass/fail) of a student based on his 2 test scores. Notice that the predicted result is a value between 0 and 1, it can be interpreted as the probability that the result is a positive (y 1). In our example, for test scores 60 and 75, there is a 87% probability that the student passes. An additional condition like the following can be used inside the predict function for binary output.

It's been a long time since I did a coding demonstrations so I thought I'd put one up to provide you a logistic regression example in Python! Admittedly, this is a cliff notes version, but I hope you'll get enough from what I have put up here to at least feel comfortable with the mechanics of doing logistic regression in Python (more specifically; using scikit-learn, pandas, etc…). This logistic regression example in Python will be to predict passenger survival using the titanic dataset from Kaggle. Before launching into the code though, let me give you a tiny bit of theory behind logistic regression. The logistic regression formula is derived from the standard linear equation for a straight line.

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