Spam_Mail_Prediction_using_Machine_Learning
Spam_Mail_Prediction_using_Machine_Learning
Spam Mail Prediction Web App
This repository contains a web application that predicts whether an email is spam or not using a Logistic Regression model. The application is built with a user-friendly interface and offers real-time predictions based on the email content.
Overview
pam emails are a major issue in digital communication, causing various problems ranging from minor annoyances to severe security threats. This project aims to develop a solution to detect spam emails effectively using machine learning techniques. The application preprocesses email text using TF-IDF vectorization and classifies it with a Logistic Regression model.
Dataset
The dataset used for training the model consists of a collection of emails labeled as spam or not spam. The data has been preprocessed to remove noise and irrelevant information.
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The model achieves high accuracy in predicting spam emails, with key metrics as follows:
Accuracy: 96.59% These results demonstrate the model's effectiveness in distinguishing between spam and non-spam emails.
Conclusion
This project successfully demonstrates the use of machine learning for spam email detection. The Logistic Regression model, combined with TF-IDF vectorization, provides a reliable solution for predicting spam emails. Future improvements could include testing with larger datasets, experimenting with different models, and enhancing the web application's features.
Contributing
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
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