Fake News Detection

Overview

This project aims to build a machine learning model to detect fake news. The analysis includes data preprocessing, exploratory data analysis, feature engineering, model training, and evaluation.

Project Structure

• fake_news.ipynb: The Jupyter notebook containing the complete analysis, model training, and evaluation.
• news.csv/: Containing the dataset used for the analysis.

Dataset

The dataset used in this project consists of news articles labeled as fake or real. The data includes various features such as the article text, title, and other metadata.

Analysis

The analysis is divided into several key sections:

1. Data Preprocessing: Cleaning the data, handling missing values, and preparing the text for analysis.
2. Exploratory Data Analysis (EDA): Initial exploration of the dataset to identify trends and patterns.
3. Feature Engineering: Creating features from the text data using techniques like TF-IDF, word embeddings, etc.
4. Model Training: Training machine learning models such as Logistic Regression, Naive Bayes, or advanced models like LSTM or BERT.
5. Model Evaluation: Evaluating the performance of the models using metrics like accuracy, precision, recall, and F1-score.

Usage

To run the analysis, open the fake_news.ipynb notebook in Jupyter Notebook and execute the cells sequentially. Ensure that the dataset is placed in the correct directory as specified in the notebook.

Results

The results of the analysis include various visualizations, model performance metrics, and insights derived from the data. Key findings are documented within the notebook.

Conclusion

This project demonstrates how to build and evaluate a machine learning model for fake news detection. The insights and models developed can be used for further research or deployed in applications to help identify and mitigate the spread of fake news.

Requirements

• Python 3.x
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• NLTK (Natural Language Toolkit)
• TensorFlow or PyTorch (if using deep learning models)
• Matplotlib
• Seaborn

You can install the required packages using the following command:

pip install pandas numpy scikit-learn nltk tensorflow matplotlib seaborn