• Samiran Deore
• Kaushal Kotkar
• Sharad Manani
• Riddhi Tak

This study proposes a Fake News Detection System (FNDS) utilizing logistic regression, natural language processing (NLP), and feature engineering. The system addresses the growing challenge of identifying fake news amidst vast amounts of online content. By leveraging logistic regression for classification, NLP for text processing, and feature engineering for informative feature extraction, FNDS achieves accurate detection. Experimental results demonstrate the effectiveness of this approach, showcasing its potential for combating misinformation in real-world settings.

Identifying fake news amidst vast amounts of online content.

"FAKE NEWS DETECTION SYSTEM USING LOGISTIC REGRESSION, NLP & FEATURE ENGINEERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.c197-c200, June-2024, Available :http://www.jetir.org/papers/JETIR2406227.pdf

"FAKE NEWS DETECTION SYSTEM USING LOGISTIC REGRESSION, NLP & FEATURE ENGINEERING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppc197-c200, June-2024, Available at : http://www.jetir.org/papers/JETIR2406227.pdf