Fake News Detection using Machine Learning & NLP!

Dev.to / 5/5/2026

💬 OpinionTools & Practical UsageModels & Research

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Key Points

The project presents an end-to-end fake news detection system that classifies news articles as FAKE or REAL using Python, NLP, and deep learning.
It uses an NLP preprocessing pipeline (text cleaning and lemmatization) and TF-IDF vectorization with bigrams (5,000 features) as the main text representation.
The author compares six classical machine learning models (e.g., Logistic Regression, Naive Bayes, Random Forest, SVM) alongside a dense neural network implemented with TensorFlow/Keras.
Reported insights suggest fake news tends to use more emotional/sensational and uppercase language, while real news is more formal and structured, and Logistic Regression performed excellently on TF-IDF features.
The system includes training regularization (dropout and early stopping) to reduce overfitting and provides a real-time prediction interface plus EDA visualizations (e.g., word clouds).

Excited to share my latest project: Fake News Detection using Machine Learning & NLP!
In today's world of misinformation, I built an end-to-end system that automatically detects whether a news article is FAKE or REAL using Python, NLP, and Deep Learning.
What I built: ✅ Complete NLP pipeline with text cleaning & lemmatization ✅ TF-IDF vectorization with bigrams (5000 features) ✅ 6 Machine Learning models compared side by side ✅ Deep Neural Network with Dropout regularization ✅ Real-time user input prediction system ✅ EDA with Word Clouds & visualizations
Models Used: ▸ Logistic Regression ▸ Naive Bayes ▸ Random Forest ▸ Decision Tree ▸ Gradient Boosting ▸ Linear SVM ▸ Dense Neural Network (TensorFlow/Keras)
Key Findings: ▸ Fake news uses emotional, sensational & uppercase language ▸ Real news is formal, structured & factual ▸ Logistic Regression achieved excellent accuracy on TF-IDF features ▸ Neural Network with Early Stopping & Dropout prevented overfitting
️ Tech Stack: Python | Pandas | Scikit-learn | TensorFlow | Keras | NLTK | Matplotlib | Seaborn
This project taught me how powerful simple NLP techniques like TF-IDF can be when combined with the right models. Sometimes you don't need GPT — a well-tuned Logistic Regression can be just as effective!
Full code available on GitHub https://github.com/Urooj25/News_Detection_Model.git