Overview This project focuses on the development of a machine learning model for classifying SMS messages as either "spam" or "ham" (legitimate). The primary objective is to create an accurate and reliable system for identifying and filtering out unwanted or potentially harmful text messages.
#Problem Statement Unsolicited SMS spam is a common issue that can be intrusive and deceptive. Detecting and filtering out these unwanted messages is crucial for user privacy and security.
#Data The project uses a dataset of SMS messages, where each message is labeled as "spam" or "ham." The dataset is preprocessed to prepare it for machine learning.
#Methodology The project employs natural language processing (NLP) and machine learning techniques to address the SMS spam classification problem. Key steps include:
Data Preprocessing: Text data is cleaned, tokenized, and prepared for analysis.
Feature Extraction: Textual features are created, such as TF-IDF values or word embeddings.
Model Development: Machine learning models like Naive Bayes, RNN, or LSTM are trained on the data.
Evaluation: Models are assessed based on metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation is applied for robust evaluation.
#Results The project aims to develop an efficient SMS spam classification system that can accurately identify and filter out spam messages, reducing their impact on users.
How to Use To utilize the SMS spam classification model, follow the instructions provided in the project's code and documentation. You can input an SMS message, and the model will predict whether it's "spam" or "ham."
#Dataset Source The SMS dataset used in this project can be found on Kaggle. Access it via the provided Kaggle dataset link. https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
#Contributions Contributions to the project are encouraged. Feel free to submit pull requests, report issues, or suggest improvements to enhance the accuracy and usability of the SMS spam classification system.