Fake News Detection using Machine Learning

Overview

This repository contains a Jupyter notebook for a Fake News Detection project using machine learning techniques. The notebook is designed to run on Google Colab, leveraging a GPU for accelerated processing.

Prerequisites

Make sure you have the required libraries installed by running the following commands:

pip install pandas numpy matplotlib seaborn wordcloud nltk gensim plotly scikit-learn

Contents

The notebook is divided into the following sections:

1. Loading the Dataset

   • Reads and loads the True and Fake news datasets from Kaggle.

2. Setting up a target and merging datasets

   • Adds a target column indicating whether the news is true (1) or fake (0).

3. Checking the number of null values

   • Identifies and handles null values in the dataset.

4. Data Cleaning

   • Removes stopwords and performs data cleaning.

5. Exploratory Data Analysis (EDA)

   • Analyzes and visualizes the distribution of true and fake news, most covered issues, word cloud and maximum word count in a title.

6. Data Preprocessing

   • Further cleans and prepares the data for model training.

7. Model Building

   • Utilizes four algorithms for training and evaluation:
     • Logistic Regression
     • Decision Tree Classifier
     • Gradient Boosting Classifier
     • Random Forest Classifier

8. Manual Testing

   • Allows users to input news for manual testing and provides predictions from the trained models.

9. Hindi to English News Translation