What is logistic regression?

Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables.

In logistic regression, a logistic function (also known as the sigmoid function) is used to model the probability that a certain event will occur. The logistic function outputs a probability value between 0 and 1, which can then be used to make a prediction. The logistic function maps any input value to the output value between 0 and 1.

The model is trained on a labeled dataset, where the independent variables are used to predict the binary outcome. The coefficients of the independent variables and the intercept term are estimated through maximum likelihood estimation. Once the model is trained, it can be used to make predictions on new, unseen data.

Logistic Regression is widely used in various fields, including but not limited to medicine, social sciences, and marketing, for tasks such as classification, prediction, and estimation.

In this example, the data.csv file contains the independent variables and the binary outcome in separate columns. The first line of code imports the necessary libraries. Then, the data is loaded into a pandas DataFrame and split into training and testing sets using the train_test_split function.

Next, a logistic regression model is trained using the fit method on the training set. The model is then used to make predictions on the test set using the predict method. Finally, the accuracy of the model is evaluated using the accuracy_score function from the scikit-learn library.