The article explores the process of building a news categorization classifier using Logistic Regression, NewsAPI, and Natural Language Processing (NLP) techniques. It highlights the importance of news categorization in managing the vast amount of daily published news articles, facilitating subsequent aggregation, monitoring, and retrieval tasks. The project involves collecting news categories using NewsAPI, preprocessing text data, and training a logistic regression model to classify news headlines into categories such as business, entertainment, sports, or tech. The text classifier model demonstrates how logistic regression, a machine learning algorithm suitable for both binary and multiclass classification problems, can be effectively applied to text data. The article provides insights into text preprocessing, vectorization using TF-IDF, and model evaluation, emphasizing logistic regression's simplicity and effectiveness in handling text classification tasks. It encourages readers, particularly those working with text data, to apply the discussed techniques to other datasets and further explore the potential of logistic regression in text classification.