From README.md in the repository.
A lightweight Machine Learning project to detect fake news articles using Scikit-Learn. This system uses TF-IDF (Term Frequency-Inverse Document Frequency) for feature extraction and Logistic Regression for classification, offering high accuracy with minimal computational cost.
🚀 Project Overview
Misinformation is a growing problem in the digital age. This project builds a binary classifier to distinguish between:
- Real News: Verified articles from legitimate sources.
- Fake News: Fabricated stories intended to deceive.
🛠️ Tech Stack
- Python 3.x
- Pandas & NumPy: For efficient data manipulation.
- Scikit-Learn:
TfidfVectorizer: Converts text into numerical vectors based on word importance.LogisticRegression: A robust linear model for binary classification.Pipeline: Streamlines the training process.
⚙️ Installation
Clone the repository
[git clone [https://github.com/Soham-o/fake-news-detection.git](https://github.com/Soham-o/fake-news-detection.git)
cd fake-news-detection](https://github.com/Soham-o/Fake-News-Detection-.git)
Install Dependencies
You can install the required libraries using pip:
pip install pandas numpy scikit-learn
🧠 How It Works
The code follows a clean 4-step pipeline:
- Data Loading & Cleaning:
- Loads the raw CSV files.
- Assigns labels (1 for True, 0 for Fake).
- Combines, shuffles, and removes duplicate entries to prevent data leakage.
- Preprocessing (TF-IDF):
- Converts raw text into a matrix of TF-IDF features.
- This statistically measures how important a word is to a document in the corpus.
- Model Training:
- The processed vectors are fed into a Logistic Regression classifier.
- Evaluation:
- Outputs the accuracy score and a detailed classification report (Precision, Recall, F1-Score).
🏃♂️ Usage
To run the project, simply execute the python script:
python main.ipynb