Spam Mail Prediction

Spam mail prediction using machine learning is an essential application in email filtering and cybersecurity.
This project analyzes email text content to classify whether a message is spam or legitimate.

Using Python and machine learning the model learns word patterns frequency distribution and writing style differences between spam and non spam mails which helps users reduce unwanted emails and enhances online security.

Project Overview

• Machine learning based text classification system
• Uses Logistic Regression as the main classification model
• Applies TF IDF vectorization to convert email text into numerical features
• Built using Python and widely used NLP and machine learning libraries

Libraries Used

• Pandas for data loading and preprocessing
• NumPy for numerical operations
• Scikit Learn for vectorization model training and evaluation
• TF IDF Vectorizer for converting email text into feature vectors
• Train Test Split for evaluating model performance

Dataset Details

The dataset contains email messages labeled as spam or not spam.
Key columns include

• Email text
• Label where one represents spam and zero represents non spam

The dataset focuses on identifying patterns such as promotional language suspicious phrases and repetitive keywords.

Preprocessing Steps

• Cleaned and prepared email text
• Converted text to lower case and removed unnecessary characters if needed
• Applied TF IDF Vectorizer to transform text into numerical form
• Split the dataset into training and testing sets for evaluation

Model Building

• Logistic Regression selected as the classification model
• Model trained on TF IDF transformed email text
• Learned patterns that differentiate spam from legitimate emails
• Evaluated using accuracy precision recall and F1 score

Performance and Accuracy

• Achieved strong accuracy on both training and test datasets
• Precision and recall used to ensure reliable spam detection
• Confusion matrix helps identify false positives and false negatives

Prediction Flow

1 User inputs an email message
2 Text is transformed using the fitted TF IDF vectorizer
3 Logistic Regression model predicts spam or not spam

• One means the email is spam
• Zero means the email is not spam

Deployment Possibilities

• Can be deployed using Flask or Streamlit for real time spam detection
• Useful for email services businesses and cybersecurity platforms
• Can be integrated into automated email filtering systems

Key Takeaways

• Complete end to end NLP classification pipeline successfully implemented
• Demonstrates practical use of machine learning for email filtering
• Shows how Logistic Regression and TF IDF can create reliable spam classifiers

Future Enhancements

• Use more advanced models such as SVM Random Forest or Naive Bayes
• Experiment with deep learning models like LSTM or transformer based architectures
• Add real time email scanning features
• Build a full dashboard showing spam statistics and filtering insights