The purpose of this Project is to explore the results of applying machine learning techniques to detect Message spam detection. SMS spam (sometimes called cell phone spam) is any junk message delivered to a mobile phone as text messaging through the Short Message Service (SMS). The dataset for this project originates from the UCI Machine Learning Repository.
Mobile phone spam also known as (unsolicited messages, especially advertising), directed at the text messaging or other communications services of mobile phones or smartphones. Fighting SMS spam is complicated by several factors (compared to Internet email), including the lower rate of SMS spam, which has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software.
Use different approaches to establish relation between the text and the category SPAM or HAM like, based on size of message, word count, special keywords. Then build classification models using different techniques to distinguish spam sms. Compare accuracy of each technique and plot the accuracy graphs in a single bar plot. Also generate a word-cloud for spam SMS.