In today's world Natural Language Processing (NLP) has become a productive method which is highly used in the artificial intelligence and machine learning sector. A chatbot like chatGPT to Blockchain, every method of taking the advantages of NLP. Text classification is an important process of Natural Language processing (NLP) which includes categorizing or labeling text data according to predefined categories. However, there is a lot of text information available about text classification that is becoming a tool for a lot of applications, including sentiment analysis, recommendation systems, and information retrieval. In our research, we aim on text classification for English news articles using NLP. To reach the objective of our research which is to use variations of feature extraction and machine learning (ML) algorithms to enhance the correction rate as well as the effectiveness of text classification for English news articles. We have analyzed the results we get from the algorithm and tried to find the best performance as we compared the results we get from  ML such as the Term Frequency-Inverse Document Frequency (TF-IDF) and the Vectorize method. We used different algorithms such as Random Forest (RF), Logistic Regression (LE), and Naive Bayes (NB) algorithms. For the research, we used the dataset from BBC News containing different data and articles. We worked on that dataset which contains news of various genres and as a result, we could judge the efficiency. Text data are pre-processed, features are extracted using various methods and classification models are trained using different ML algorithms. After attaining the result and accuracy, we have analyzed the results of the models. The results of this study will be applied to enhance the accuracy of the word categorization for news articles in other text-based applications. The results can be applied to develop reliable text classification algorithms that will improve data efficiency and accuracy.

NLP,Text classification,Machine learning model