🚀 Fake News Detection

In today’s digital era, fake news has become a growing concern, leading to widespread misinformation and societal consequences. This project leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques to accurately detect fake news articles, ensuring better dissemination of authentic information.

---

🌟 Features

• Dataset Loading and Cleaning: Efficiently loads datasets (Fake.csv and True.csv), removes null values, and cleans text for analysis.
• Text Preprocessing: Prepares raw text using techniques such as tokenization, stop word removal, stemming/lemmatization, and vectorization.
• Machine Learning Models: Implements advanced classification models:
  • Logistic Regression
  • Random Forest
  • Support Vector Machines (SVM)
• Model Evaluation: Uses key performance metrics for evaluation:
  • Accuracy
  • Precision
  • Recall
  • F1-Score
• Interactive Interface: User-friendly application built with Streamlit, providing real-time predictions.
• Deployment: Fully hosted and accessible online using Streamlit.

---

📊 Dataset

• Source: Datasets sourced from reliable repositories (e.g., Kaggle or public datasets).
• Format: CSV files with the following structure:
  • Fake.csv: Labeled fake news articles.
  • True.csv: Labeled authentic news articles.

📁 Key Dataset Details:

Attribute	Description
Title	Headline of the news
Text	Full news content
Label	Classification (Fake/True)

---

🧠 AI Techniques

🔍 Natural Language Processing (NLP)

• Preprocessing:
  • Tokenization
  • Stop word removal
  • Stemming/Lemmatization
• Vectorization:
  • TF-IDF
  • Count Vectorizer

🤖 Machine Learning Models

• Logistic Regression
• Random Forest
• Support Vector Machines (SVM)

📈 Performance Metrics

• Accuracy: Measures the percentage of correctly predicted labels.
• Precision: Evaluates the correctness of positive predictions.
• Recall: Measures how many actual positive cases were identified.
• F1-Score: Provides a balance between precision and recall.

---

⚙️ Tools and Technologies

Category	Technologies Used
Programming	Python
Libraries	NLTK, spaCy, Scikit-learn, TensorFlow/Keras
Data Analysis	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Deployment	Streamlit
Environment	Jupyter Notebook

---

🛠️ System Architecture

Below is an architectural representation of the project: