文件列表:

LICENSE (1071, 2023-04-06)
fake-news.ipynb (32680, 2023-04-06)
train.csv.zip (38841253, 2023-04-06)

# Fake News Detection using Machine Learning In this project, we aimed to address the problem of fake news detection using machine learning techniques. Our goal was to build a model that could accurately classify news articles as either real or fake. To do this, we obtained a dataset of news articles and their labels from the Kaggle platform. ## Dataset The dataset used in this project was obtained from Kaggle and contained a collection of news articles labeled as either real or fake. The dataset included features such as the title of the article, the content of the article, and the label (real or fake). ## Preprocessing We preprocessed the text data using natural language processing (NLP) techniques such as stemming and tokenization. These techniques helped to standardize the text data and make it more amenable to analysis. ## Machine Learning Model We applied classification algorithms such as logistic regression to train our model. Our model achieved impressive results, with a precision of ***.08% on the test set and 99.29% on the training set, as well as an accuracy of ***.73% on the training set and 97.74% on the test set. ## Libraries Used We implemented our model in Python, making use of libraries such as pandas, numpy, and scikit-learn to assist with data processing and model building. ## Conclusion The results of this project demonstrate the effectiveness of using classification and NLP techniques in the task of fake news detection. Our model achieved high accuracy and precision in classifying news articles as either real or fake. The code and data used in this project can be found in this repository.

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：