Python Tutorial : Using the logistic regression model

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-predictive-analytics-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you know how to construct a logistic regression model, it is time to learn how to make predictions with the logistic regression model. In the programming exercise, you constructed a model that predicts who will donate using three predictors. The formula derived is given here. Assume you want to predict for a female donor aged 72 that donated 120 days ago, how likely it is that she will donate for the new campaign. Recall that a logistic regression model is a linear regression formula wrapped in a logit function. So all you need to do is replace the predictors with the given values, and then put the result in the logit function. As the donor is female, gender_F is one, and also the other variables are given. The result of filling out the variables in the regression function is -1.45. Taking the logit of this number gives 0.19, which means that there is a 19% chance that this donor will donate for the next campaign. Fortunately, you don't need to calculate the predicted probabilities manually in Python. Consider again the 72 year old lady that donated 120 days ago. If we collect her data in a list, making sure we add the values in the same order as they appear in the logistic regression model, you can calculate the prediction by feeding this list as a parameter to the predict_proba method on the logreg object. The output is an array that has two numbers. The first number is the probability that the donor will not donate (target 0) and the second number is the probability that the donor will donate (target 1). This last number is the one that we are interested in. The probability that this donor will donate, 18%, seems pretty low. However, it is quite high if you compare it to 5%, the overall chance that someone