This internship project done under AFAME Technologies aims to build a machine learning model for classifying SMS messages as spam or legitimate (ham).
The model utilizes techniques like TF-IDF for feature extraction and several classification algorithms including Naive Bayes, Logistic Regression, and Support Vector Machines.
The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research.
It contains one set of SMS messages in English of 5,574 messages, tagged acording being ham (legitimate) or spam.
The performance of each classification algorithm is evaluated using accuracy as the main metric. Here are the accuracy scores achieved by each algorithm:
- Naive Bayes: 0.98
- Logistic Regression: 0.99
- Support Vector Machine: 0.97
Based on the evaluation results, Logistic Regression achieved the highest accuracy among the three algorithms.
Further improvements can be made by fine-tuning hyperparameters or exploring more advanced techniques.