Spam Message Classifier

This project is a machine learning-based classifier designed to distinguish between spam and ham (non-spam) messages. The classifier is built using Python, leveraging techniques for text preprocessing, feature extraction, and binary classification.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Exploratory Data Analysis
• Preprocessing
• Modeling
• Evaluation
• License

Introduction

The Spam Message Classifier aims to accurately classify messages as either spam or ham using a variety of machine learning techniques. This project provides insights into text data analysis, feature engineering, and model evaluation.

Dataset

The dataset used for this project is the Kaggle SMS Spam Collection Dataset. It contains a collection of SMS messages labeled as either "spam" or "ham" (not spam).

Installation

To set up the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/GiovanniCornejo/spam-message-classifier.git
   cd spam-message-classifier

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Exploratory Data Analysis Open and run 01_eda.ipynb notebook for an exploratory data analysis on the dataset. This notebook includes data overview, class distribution, and text analysis.

2. Data Preprocessing Before modeling, preprocess the data to prepare it for ML algorithms. Open and execute the preprocessing steps outlined in the 02_preprocessing.ipynb notebook. This includes tasks such as text cleaning, handling class imbalance, and feature extraction.

3. Modeling After preprocessing the data, proceed to 03_modeling.ipynb notebook to build and train machine learning models for message classification. Explore various algorithms and techniques to find the most effective classifier.

4. Evaluation Once models are trained, evaluate their performance using 04_evaluation.ipynb notebook. Assess the effectiveness of each model using metrics such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC).

Exploratory Data Analysis

Key findings from the exploratory data analysis:

• The dataset is imbalanced with more ham messages than spam.
• Spam messages tend to be longer on average compared to ham messages.
• Common words and bigrams and trigrams in spam messages indicate urgency and actions (e.g., "call", "claim", "won").

Preprocessing

Key preprocessing steps:

• Text Cleaning: Remove punctuation, special characters, unnecessary whitespace, and convert text to lowercase to ensure consistency.
• Stopword Removal: Remove common words (e.g., "the", "is", "and", etc.) that do not carry significant meaning.
• Vectorization: Convert text data into numerical vectors using TF-IDF (Term Frequency-Inverse Document Frequency) technique.

Modeling

Common algorithms used for text classification are modeled:

• Logistic Regression: A linear model used for binary classification tasks.
• Random Forest: An ensemble learning method that builds multiple decision trees and combines their predictions.
• Support Vector Machine (SVM): A supervised learning algorithm that separates data points using hyperplanes in high-dimensional space.
• Naive Bayes: A probabilistic classifier based on Bayes' theorem with strong independence assumptions between features.
• And a few others.

Evaluation

The performance of trained models is assessed using various metrics to measure their effectiveness in distinguishing between spam and ham messages.

• Accuracy: The proportion of correctly classified instances out of the total instances.
• Precision: The proportion of true positive predictions out of all positive predictions.
• Recall (Sensitivity): The proportion of true positive predictions out of all actual positive instances.
• F1 Score: The harmonic mean of precision and recall, providing a balanced measure between the two.

Further analysis is done using:

• Confusion Matrix: A table that summarizes the performance of a classification model by comparing actual and predicted classes.
• Area Under the ROC Curve (AUC): A measure of the classifier's ability to distinguish between classes, where higher values indicate better performance.

License

This project is licensed under the MIT License. See the LICENSE file for details.