Fake News Detection Using Machine Learning And NLP

Welcome to the Fake News Detection project! This repository contains a machine learning pipeline built to detect whether a news article is real or fake based on its textual content. In an era of overwhelming information, this tool helps combat misinformation by classifying news using NLP techniques and ML algorithms. 🚀 Project Overview This project uses Natural Language Processing (NLP) and Machine Learning to classify news articles as either "Real" or "Fake". The model is trained on a labeled dataset of news headlines and articles. The aim is to help users, journalists, and organizations identify potentially misleading content. 🧠 Key Features •✅ Text Preprocessing: Tokenization, stop word removal, punctuation removal, and lowercasing. •📊 Exploratory Data Analysis (EDA): Understand class distribution, word frequency, and common patterns in fake vs real news. •📚 TF-IDF Vectorization: Converts text to numerical format to feed into the model. •🤖 ML Models Used: Logistic Regression, PassiveAggressiveClassifier, Naive Bayes, etc. •🧪 Evaluation Metrics: Accuracy, Confusion Matrix, Precision, Recall, F1-Score. •📈 High Accuracy: Achieved over 90% accuracy in detecting fake news.•🛠️ Technologies & Libraries 🛠️ Technologies & Libraries •Python 🐍 •Pandas, NumPy •Scikit-learn •NLTK •Matplotlib, Seaborn •Jupyter Notebook 📌 How it Works 1.Data Loading: News dataset is loaded using pandas. 2.Text Cleaning: Articles are cleaned using NLP techniques (lowercasing, stop words, tokenization). 3.Feature Engineering: TF-IDF is used to convert text into vectors. 4.Model Training: ML models are trained to classify articles. 5.Evaluation: Models are evaluated using accuracy, confusion matrix, etc. 6.Prediction: New/unseen news articles can be tested using the trained model. 🧑‍💻 Author Niti Desai AI & ML Enthusiast | Passionate about using data for good 🌐 www.linkedin.com/in/niti-desai-25a90b217 📧 nitidesai21@gmail.com