• Shiv Kumar
• Pankaj Verma

The spread of fake news on social media and other platforms is a major worry since it has the potential to have devastating effects on society and the country. The detection of it is already the subject of much research. In order to develop a model of a product with supervised machine learning algorithm that can classify fake news as true or false, this paper analyzes the research on fake news detection and investigates traditional machine learning models to determine which is the best. It does this by using tools such as Python scikit-learn and natural language processing (NLP) for textual analysis. This procedure will lead to feature extraction and vectorization; since the Python scikit-learn package includes helpful functions like Count Vectorizer and Tiff Vectorizer, we suggest utilizing it to do tokenization and feature extraction of text data. Based on the results of the confusion matrix, we will then experiment with feature selection techniques to determine which features best fit the data and achieve the highest level of precision.

Fake News, Machine Learning, NLP, Feature Extraction.

"Machine Learning Techniques for the Identification of Fake News", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.e60-e69, September-2025, Available :http://www.jetir.org/papers/JETIR2509407.pdf

"Machine Learning Techniques for the Identification of Fake News", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppe60-e69, September-2025, Available at : http://www.jetir.org/papers/JETIR2509407.pdf