Problem Statement:
An SMS unsolicited mail (every now and then known as cell smartphone junk mail) is any junk message brought to a cellular phone as textual content messaging via the Short Message Service (SMS). Use a probabilistic approach (Naive Bayes Classifier / Bayesian Network) to implement an SMS Spam Filtering system. SMS messages are categorized as SPAM or HAM using features like the length of the message, the word depend, unique keywords, etc.
Download Data -Set from: https://archive.ics.uci.edu/ml/datasets/sms%2Bspam%2Bcollection
This dataset is composed of just one text file, where each line has the correct class followed by the raw message.
A. Apply Data pre-processing (Label Encoding, Data Transformation... ) techniques if necessary
B. Perform data preparation (Train-Test Split)
C. Apply at least two Machine Learning Algorithms and Evaluate Models
D. Apply Cross-Validation and Evaluate Models and compare performance.
E. Apply Hyperparameter tuning and evaluate models and compare performance.
