SMS Spam Classification

Table of Contents

1. Project Introduction
2. Technologies Used
3. Methods Used
4. Project Description
   • 1. Data Sources
   • 2. File Descriptions
5. Licences
6. Contact

Project Introduction

The SMS Spam Collection is a public set of SMS labeled messages that have been collected for mobile phone spam research. The goal of this project is to develop a machine learning model that can accurately classify SMS messages as either "spam" or "ham". This is important because spam messages can be a nuisance and even pose a security risk if they contain phishing scams or malicious links. By accurately identifying spam messages, users can avoid them and better protect their personal information.

Technologies Used

• Python
• Numpy
• Pandas
• Matplotlib
• Seaborn
• NLTK
• scikit-learn

Methods Used

• Data Processing / Data Cleaning
• Data Analysis
• Data Visualization
• Text Preprocessing
• Predictive Modeling and Hyperparameter Tuning
• Evaluating Model Results
• Reporting

Project Description

This project is a machine learning model that classifies SMS messages as either "spam" or "ham" (non-spam). The model achieved an accuracy of 0.97 using Multilayer Perceptron.

Data Sources

The data was obtained here

File Descriptions

• Images - folder containing assets such as images
• SMS Spam Classification - Notebook of the project (end-to-end)

Licenses

Database Contents License (DbCL) v1.0

Contact

Find me on LinkedIn, Twitter or adzictanja.com.