SMS Spam Detection Project

Part of my data science portfolio - Building a machine learning system for binary classification of SMS messages.

Project Overview

Developing a spam detection system using ML techniques, currently focusing on establishing strong baseline models and evaluation metrics.

Motivation

This project builds on prior work in text analysis (e.g., Word Cloud Visualization, Travel Blog Analysis) and classification (e.g., SME Closure Prediction). It establishes a solid foundation before diving into more sophisticated techniques, starting with strong baseline models and robust evaluation metrics to develop a deep understanding of the core challenges in classification.

🛠 Tech Stack

Current

• Python 3.9.13
• Data Processing: Pandas, NumPy
• Machine Learning: Scikit-learn
• NLP: NLTK, WordCloud
• Data Visualization: Matplotlib, Seaborn

📊 Project Structure

/sms-spam-classifier
│
├── README.md                        # Project overview and documentation
├── LICENSE                          # Project license file
├── requirements.txt                 # Python dependencies
├── vs_code_setup.md                # VS Code configuration guide
├── notebooks/                       # Jupyter notebooks for analysis
├── src/                            # Source code directory
│   ├── data/                       # Data storage and processing
│   ├── models/                     # Machine learning models
│   └── utils/                      # Utility functions and helpers
├── tests/                          # Unit tests
└── docs/                           # Project documentation

🚧 Current Progress

• Implemented initial baseline models using different approaches:
  • Count Vectorizer + Logistic Regression
  • TF-IDF + Random Forest
• Enhanced Exploratory Data Analysis (EDA) focusing on:
  • Message length distribution analysis
  • Text feature analysis (word count, special characters, capitals ratio, etc.)
  • Word frequency visualization and word clouds
• Basic text preprocessing and model evaluation completed

📝 Next Steps

1. Model Performance Improvement
2. Code Structure Enhancement
3. Further EDA and Feature Engineering

📁 Dataset

• Using the UCI SMS Spam Collection Dataset from Kaggle
• Binary classification: spam vs ham (non-spam) messages

🔧 Setup

1. Create virtual environment

python -m venv spam_detector_env

2. Activate virtual environment

# Windows
spam_detector_env\Scripts\activate
# Mac/Linux
source spam_detector_env/bin/activate

3. Install dependencies

pip install numpy pandas scikit-learn jupyter

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This project is part of my journey to become a data scientist who solves real-world problems through data-driven solutions.