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Fake News Detection with Machine Learning in Python with Deployment

Learn how to build a Fake News Detection System using machine learning and deploy it as an interactive web app with Streamlit! In this step-by-step tutorial, we’ll guide you through the process of creating a robust machine learning model to classify news as real or fake using Python. 📌 What You’ll Learn in This Video: • Data preprocessing and cleaning for text data. • Feature extraction using TF-IDF Vectorizer. • Building and training a machine learning model with Scikit-learn. • Evaluating the model’s accuracy and performance. • Deploying your Fake News Detection model using Streamlit. 📊 Topics Covered: • Text preprocessing (removing stopwords, stemming, etc.) • Feature engineering with text data. • Machine learning algorithms like Logistic Regression, Naive Bayes, etc. • Building an intuitive UI with Streamlit for real-time fake news detection. 🚀 Tools and Libraries Used: • Python • Pandas, NumPy • Scikit-learn • Re , String • Streamlit 💡 Who Is This Video For? This video is ideal for machine learning enthusiasts and developers who want to build AI/ML projects and deploy them as functional web apps. 🔗 Links Mentioned in the Video: • Code Repository: https://github.com/TensorTitans01/Fak... • Dataset: https://drive.google.com/drive/folder... 👉 Subscribe for More Practical Projects! If you love content on machine learning, Artificial Intelligence, Python coding, and real-world NLP projects, make sure to subscribe to Tensor Titans. New tutorials every week! 🔔 Turn On Notifications to stay updated! 💬 Got Questions? Ask your questions in the comments, and I’ll be happy to help! #FakeNewsDetection #MachineLearning #Python #NLP #Streamlit #TensorTitans