Spam Mail Detection

[SayantikaLaskar]

Issue #204

Spam emails pose a significant challenge by cluttering inboxes and potentially posing security risks. This project addresses this issue by using machine learning to distinguish spam emails from legitimate ones. The project involves preprocessing email data, extracting meaningful features, training a classification model, and using this model to detect spam.

Features

• Email Preprocessing: Methods to clean and prepare email data, including removing unwanted characters, normalizing text, and handling missing values.
• Feature Extraction: Techniques to derive useful features from emails, such as word frequencies, the presence of certain keywords, and email metadata.
• Model Training: Processes for training a machine learning model, including selecting an algorithm, training on the dataset, and optimizing model parameters.
• Spam Detection: Using the trained model to classify new emails as spam or not, providing a practical tool for users.
• Evaluation Metrics: Tools to measure the model's performance through metrics like accuracy, precision, recall, and F1-score, ensuring the model's effectiveness.

[invigorzz313]

Please take care of the extra files.