This project focuses on classifying news articles into two categories: fake news and true news, using various machine learning models and text vectorization techniques. The goal is to build a robust text classification model that can accurately distinguish between fake and true news.

Three different text vectorization techniques have been used:

1. Count Vectorizer: This technique converts text data into a numerical format based on the frequency of words.

2. TF-IDF Vectorizer: TF-IDF (Term Frequency-Inverse Document Frequency) is used to represent the importance of words in a document relative to the entire corpus.

3. Word2Vec Vectorizer: This method creates word embeddings by learning word associations within the text data.

The following machine learning models have been implemented and evaluated for news classification:

1. Logistic Regression: Implemented with variations for Count Vectorizer, TF-IDF Vectorizer, and Word2Vec Vectorizer.

2. Naive Bayes: Implemented with variations for Count Vectorizer, TF-IDF Vectorizer, and Word2Vec Vectorizer.

3. Support Vector Machine (SVM): Implemented with variations for Count Vectorizer, TF-IDF Vectorizer, and Word2Vec Vectorizer.

4. Random Forest: Initially implemented with variations for Count Vectorizer, TF-IDF Vectorizer, and Word2Vec Vectorizer. An improved version of the Random Forest model with TF-IDF Vectorization is also presented.

The models have been evaluated using the test dataset, and the following metrics have been calculated:

• Accuracy
• F1 Score

The Random Forest model with TF-IDF Vectorization has been improved by adjusting its parameters for better performance.

Ensure you have the following Python libraries installed:

• numpy
• pandas
• scikit-learn
• gensim
• matplotlib
• seaborn

1. Clone this repository to your local machine:

git clone https://github.com/NikosMav/AI-FakeNews-Classification.git

2. Install the required dependencies:

pip install numpy pandas scikit-learn gensim matplotlib seaborn

3. Run the Jupyter Notebook or Python script to execute the machine learning models and perform news classification.

This project is licensed under the MIT License - see the LICENSE.md file for details.

Feel free to explore the Jupyter Notebook for a detailed step-by-step explanation of the project and its implementation.