SMS SPAM CLASSIFIER

Description

Email/SMS is the worldwide use of communication application. It is because of the ease of use and faster than other communication application. However, its inability to detect whether the message content is either spam or ham degrade its performance. Nowadays, lot of cases have been reported regarding stealing of personal information or phishing activities via message from the user

Nowadays, a big part of people rely on available email or messages sent by the stranger. The possibility that anybody can leave an email or message provides a golden opportunity for spammers to write spam message about our different interests. Spam fills inbox with number of ridiculous emails/messages. Degrades our internet speed to a great extent. Steals useful information like our details on our contact list. Identifying these spammers and also the spam content can be a hot topic of research and laborious tasks. Email/SMS spam is an operation to send messages in bulk by mail/SMS.

The dangers of spam messages for the users are many: undesired advertisement, exposure of private information, becoming a victim of a fraud or financial scheme, being lured into malware and phishing websites, involuntary exposition to inappropriate content, etc. For the network operator, spam messages result in an increased cost in operations.

A few common spam emails/SMS include fake advertisements, chain emails, and impersonation attempts. While these built-in spam detectors are usually pretty effective, sometimes, a particularly well-disguised spam email/SMS may fall through the cracks, landing in your inbox instead of your spam folder. Clicking on a spam/SMS email can be dangerous, exposing your computer and personal information to different types of malware. Therefore, it’s important to implement additional safety measures to protect your device, especially when it handles sensitive information like user data.

Our main goal of this case study is to design an email/SMS spam filtering system using machine learning. It is a binary classification problem. Given a new email/SMS we will predict whether it is spam or non-spam. The reason to do this is simple: by detecting unsolicited and unwanted emails/SMS, we can prevent spam messages from creeping into the user’s inbox, thereby improving user experience.

Motivation

The exponential growth of mobile communication has made SMS messaging an indispensable part of our daily lives. However, the increasing number of spam messages poses a significant threat to user privacy, security, and convenience. By leveraging the power of machine learning, we can develop an accurate and reliable SMS spam classification system that can effectively detect and filter spam messages in real-time. Such a system can improve the overall quality of mobile communication, reduce the risk of fraudulent activities, and ensure user satisfaction and safety. Therefore, investing in the development of an SMS spam classification system using machine learning is not only crucial but also a responsible step towards a better and safer digital future.

Problem Statement

Build a email/SMS spam detector system using machine learning algorithm

Predict whether the given message is ham or spam.

Classify the email/SMS into a ham or spam given the email/SMS text.

Dataset URL

Refer : https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset

Perfomance Metric

Accuracy Precision and Recall F1-Score Binary Confusion Matrix

Objective

Predict whether the email is spam or not and also predict its class probability.

Web app URL

https://princebari-sms-spam-classifier-app-doacjo.streamlit.app/