Fake_News_Classifier_using_Natural_Language_Processing (NLP)

I. Introduction

Embark on a journey to develop a Fake News Classifier using NLP techniques. This README outlines the key steps involved in building and implementing the classifier.

II. Setup and Data Preparation

1. Import Libraries: Begin by importing the necessary Python libraries to facilitate the development of the Fake News Classifier.

2. Loading the Data: Load the dataset containing news articles, a crucial step in preparing the data for analysis.

3. Data Preprocessing: Clean and preprocess the data to ensure a consistent and reliable foundation for the classifier.

4. Remove Null Values: Eliminate any null or missing values from the dataset for robust model training.

5. Add a New Field: Enhance the dataset by introducing a new field or feature that aids in classification.

6. Drop Unnecessary Features: Streamline the dataset by removing features that do not contribute significantly to the classification task.

III. Text Processing

7. Text Processing: Implement text processing techniques to prepare the textual data for NLP-based analysis.

IV. Data Splitting and Vectorization

8. Splitting the Data: Divide the dataset into training and testing sets to evaluate the classifier's performance effectively.

9. Vectorization: Convert text data into numerical vectors, a crucial step for NLP model training.

V. Model Fitting

10. Logistic Regression: Train the Fake News Classifier using the Logistic Regression algorithm.

11. Support Vector Machine (SVM): Implement the SVM algorithm for classification purposes.

12. RandomForestClassifier: Explore the RandomForestClassifier as an alternative model for detecting fake news.

VI. Conclusion

This NLP-based Fake News Classifier project encompasses key stages, from data loading and preprocessing to model fitting. Utilize the implemented models to distinguish between genuine and fake news, contributing to the fight against misinformation.