An advanced spam detection system called SpamDetector is made to recognize and weed out unsolicited emails and SMS messages. By sorting valid communications from spam and maintaining a clutter-free inbox, this project intends to improve user communication through the use of machine learning and natural language processing techniques.

Features: Machine learning algorithms: For message categorization, Random Forest, Logistic Regression, and Naive Bayes classifiers are used, in end choosing the best algorithm with better precision and accuracy such as Multinomial Naive Bayes.

NLP Techniques: For text analysis and feature extraction, this technique uses word embeddings, tokenization, and TF-IDF vectorization.

Data visualization: Makes use of Matplotlib and Seaborn to present comprehensive visuals of model performance.

Support for Multiple systems: Designed to function flawlessly on a range of email and SMS systems.

Technologies Used: Python 3, Scikit-learn, NLTK, Pandas, Matplotlib, Seaborn

Import streamlit on pycharm to run the code.