Email Spam Classification

Email Spam Classification Model

Developed a comprehensive email spam classification system to accurately identify and filter spam emails. This project aims to improve email management and enhance user experience by distinguishing between legitimate and unwanted messages.

Features:

• Interactive Interface: Designed a user-friendly Streamlit application for real-time email classification.
• Detailed Feedback:Provides clear and actionable results, indicating whether an email is classified as spam or non-spam.
• Adaptability:The system can handle various email formats and sources, ensuring effective spam detection across different scenarios.

Technologies Used:

• Python
• Streamlit:For building the interactive web application.
• Pandas: For data manipulation and preprocessing.
• Scikit-learn: For machine learning model training and evaluation.
• Matplotlib and Seaborn:For data visualization.

Machine Learning Models
This project utilizes several machine learning models and techniques for predicting if the email entered is spam or not:

Naive Bayes Classifier:Applied for its efficiency in text classification, making it ideal for spam detection tasks.

Random Forest Classifier: Used to enhance classification accuracy and handle complex data features, improving overall model performance.