Today, we will explore Logistic Regression — a powerful and efficient machine learning algorithm for binary classification tasks — especially in text classification. Our goal is to help you grasp the principles of Logistic Regression, create a Logistic model to classify texting messages, and validate the performance of this model. Let's dive right in!

Logistic Regression is a statistical method that we use for binary classification problems. Unlike linear regression, which predicts a continuous output, logistic regression is designed to predict the probability of a particular class or event. It produces a logistic curve, which is limited to values between 0 and 1.

The logistic function, also known as the sigmoid function, maps any real-valued number into a range between 0 and 1. This function forms the foundation of logistic regression and is also a key element in neural networks, which lie at the heart of deep learning.

Logistic regression is often used in fields such as machine learning, and most applications of logistic regression involve binary classification. A classic use case is predicting whether an email is spam or not. Logistic regression has both advantages and drawbacks: it's efficient, does not require too many computational resources, it’s easy to implement, and it's highly interpretable. On the other hand, it can't solve non-linear problems as it has a linear decision surface, and it also tends to underperform when there are multiple or non-linear decision boundaries.

Our first step is to load the SMS Spam Collection dataset. After that, we will preprocess the data to make it suitable for our model.

Our preprocessing will include splitting the data into a training set and a testing set using stratified cross-validation. Then, we will convert the input features (message) from text format to a numerical format that our machine can understand. Lastly, we will define our output labels ().