📰 Fake News Detection

• A Machine Learning based web application that detects whether a news article is Fake or Real using NLP (Natural Language Processing).

🚀 Project Overview

• This project uses Text Processing + Machine Learning to classify news articles.

• The model is trained on real and fake news datasets and deployed using Streamlit for real-time prediction.

🛠 Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• NLP (Natural Language Processing)
• TF-IDF Vectorizer
• Logistic Regression
• Streamlit
• Joblib

🤖 Machine Learning Model Used

• Logistic Regression

📊 Dataset Details

Dataset:

• Fake & True News Dataset

Problem Type:

• Classification

Target Variable:

• 0 → Fake News
• 1 → Real News

🔍 Methodology

1️⃣ Data Preprocessing

• Combined fake and real datasets
• Created class labels
• Removed unnecessary columns
• Cleaned text (lowercase, remove links, punctuation, numbers)

2️⃣ Feature Engineering

• Converted text into numerical format using TF-IDF Vectorizer

3️⃣ Model Training

• Split data into train and test sets
• Applied Logistic Regression
• Trained model on text data

4️⃣ Model Evaluation

• Accuracy Score
• Classification Report

📸 Project Screenshots

📈 Model Performance

• Model achieved good accuracy
• Logistic Regression performed well for text classification

🌐 Deployment

• Model and vectorizer saved using Joblib
• Integrated into Streamlit Web App

Users can:

• Enter any news article
• Click Check News
• Get result (Fake or Real) instantly

▶️ How to Run the Project

##Install dependencies

• pip install -r requirements.txt

##Run Streamlit App

• streamlit run fake_news.py

🔮 Future Improvements

• Try advanced models (Naive Bayes, LSTM, BERT)
• Improve UI design
• Add confidence score