Logistic Regression Algorithm Implementation and Code Explanation

In this article, I will provide a detailed explanation and demonstration of logistic regression algorithm code. First, we will discuss the fundamental concepts and principles of logistic regression, along with its practical applications, advantages, and limitations. The implementation section will cover key algorithmic components including data preprocessing techniques (such as handling missing values and normalization), feature extraction methods, and model training processes using gradient descent optimization. For the code demonstration, we will implement the logistic regression algorithm in Python, utilizing libraries like NumPy for numerical computations and scikit-learn for dataset handling. The implementation will include the sigmoid activation function, cost function calculation, and weight update procedures. We will validate the algorithm's performance and accuracy using real-world datasets, with metrics such as precision, recall, and F1-score. This article aims to provide valuable insights and practical guidance for students and practitioners learning logistic regression algorithms.