Fake News Detection using Machine Learning

This project builds a fake-news detection system in Python that classifies news articles as real or fake based on their textual content, using a labeled dataset of real and fabricated news articles for training.

Text preprocessing (tokenization, stop-word removal, TF-IDF vectorization) feeds into and compares several classifiers — Logistic Regression, Naive Bayes, and a fine-tuned DistilBERT transformer — evaluated on accuracy, precision, recall, and F1-score to identify the best-performing approach.

A simple Streamlit interface lets a user paste an article's text or headline and get an instant real/fake prediction along with a confidence score, and the project discusses the limitations of purely text-based fake-news detection given that credibility often depends on external fact-checking, not just writing style.

Detailed Project Description

Fake News Detection using Machine Learning applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- NLP
- Scikit-learn
- DistilBERT
- Streamlit

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Scikit-learn, Hugging Face Transformers) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface