Fake advertisements are a growing problem in today's digital age, with businesses and consumers alike falling victim to scams and fraud. This project aims to tackle this issue by using logistic regression to detect fake advertisements in a given dataset.
The model is trained on historical data to identify patterns and trends in advertisement behavior. Using this information, the model can make predictions about the authenticity of advertisements, flagging those that are likely to be fake.
By using this model, businesses and consumers can avoid falling victim to fake advertisements, improving their overall trust and confidence in the advertising industry. This can also help to reduce the financial and reputational damage caused by fake advertisements.
This project demonstrates the power of machine learning in detecting fake advertisements. By using logistic regression, businesses and consumers can protect themselves from scams and fraud, and help to improve the overall trustworthiness of the advertising industry.