https://github.com/massimilianovisintainer/fake-news-prediction-model

Fake News Prediction Model
https://github.com/massimilianovisintainer/fake-news-prediction-model

machine-learning nltk numpy pandas python3 sklearn

Last synced: about 23 hours ago
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Fake News Prediction Model

• Host: GitHub
• URL: https://github.com/massimilianovisintainer/fake-news-prediction-model
• Owner: MassimilianoVisintainer
• Created: 2024-07-25T18:04:16.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-07-25T18:05:54.000Z (2 months ago)
• Last Synced: 2024-09-23T06:32:44.240Z (5 days ago)
• Topics: machine-learning, nltk, numpy, pandas, python3, sklearn
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Fake News Prediction with Logistic Regression

### Introduction

This repository contains the code for a Fake News prediction system using Logistic Regression. The code is based on a Jupyter notebook originally generated by Colab.

### Functionality

This code performs the following tasks:

* **Import Libraries:** Imports necessary libraries like pandas, numpy, nltk etc. for data manipulation, text processing and machine learning.

* **Data Preprocessing:**

  * Loads the training data (`train.csv`) into a pandas dataframe.

  * Handles missing values by replacing them with empty strings.

  * Combines author name and title into a single "content" column.

  * Separates the data (content) and the target label (fake/real).

  * Applies stemming to reduce words to their root form and removes stopwords (common words like "the", "and").

  * Converts textual data into numerical features using TF-IDF vectorizer.

* **Train-Test Split:** Splits the data into training and testing sets for model evaluation.

* **Model Training:** Trains a Logistic Regression model on the training data.

* **Evaluation:**

  * Evaluates the model's accuracy on both training and testing data.

* **Prediction:**

  * Makes a prediction on a new unseen piece of text data (example from the testing set).

  * Classifies the news as Real or Fake based on the prediction.

### Running the Code

This code is intended to be run in a Jupyter Notebook environment. You can follow these steps:

1. Download the code and data files.

2. Open the `Fake_News_Prediction.ipynb` file in a Jupyter Notebook environment.

3. Run the code cells sequentially.

### Dependencies

* Python 3.x

* pandas

* numpy

* nltk

* scikit-learn

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