Logistic Regression Example in Python (Source Code Included)

Logistic Regression Example in Python (Source Code Included) (For transparency purpose, please note that this posts contains some paid referrals) Howdy folks! 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. Logistic Regression Formulas: The logistic regression formula is derived from the standard linear equation for a straight line. As you may recall from grade school, that is y=mx + b . Using the Sigmoid function (shown below), the standard linear formula is transformed to the logistic regression formula (also shown below). This logistic regression function is useful for predicting the class of a binomial target feature. The Sigmoid Function. Logistic Regression Formula. Logistic Regression Assumptions. Any logistic regression example in Python is incomplete without addressing model assumptions in the analysis. The important assumptions of the logistic regression model include: Target variable is binary Predictive features are interval (continuous) or categorical Features are independent of one another Sample size is adequate – Rule of thumb: 50 records per predictor. So, in my logistic regression example in Python, I am going to walk you through how to check these assumptions in our favorite programming language. Pro Tip : Need to work on your software development environment from anywhere from multiple devices? Switch to desktops in the cloud by CloudDesktopOnline.com . Take a free trial from a Desktop-as-a-Service provider – http://www.Apps4Rent.com. Uses for Logistic Regression. One last thing before I give you the logistic regression example in Python / Jupyter Notebook… What awesome result can you ACHIEVE USING LOGISTIC REGRESSION. Well, a few things you can do with logistic regression include: You can use logistic regression to predict whether a customer will convert (READ: buy or sign-up) to an offer. (will not convert – 0 / will convert – 1) You can use logistic regression to predict and preempt customer churn. (will not drop service – 0 / will drop service – 1) You can use logistic regression in clinical testing to predict whether a new drug will cure the average patient. (will not cure – 0 / will cure -1) If you’re looking for a more customized, private training experience to learn the ins-and-outs of programming in Python, The Training Advisors have you covered. Take a look and see what they can do for you!! The nice thing about logistic regression is that it not only predicts an outcome, it also provides a probability of that prediction being correct.