Text Analysis with Machine Learning: Spam Message Filtering

Overview

This project involves the application of machine learning techniques, specifically Naive Bayes, for spam message filtering. The goal is to automatically detect and classify unsolicited and unwanted emails or messages as spam. The project also includes hyperparameter tuning to optimize the performance of the machine learning model.

Table of Contents

1. Introduction
2. Applications
3. Model Building
   • Naive Bayes
   • CountVectorizer
   • TF-IDF
4. Hyperparameter Tuning
   • Process Outline
5. Steps in Model Building
   • Data Cleaning
   • Exploratory Data Analysis (EDA)
   • Data/Text Preprocessing
   • Model Building
   • Evaluation
   • Hyperparameter Tuning
   • Simple Website for Model Deployment

Introduction

Text analysis involves the application of natural language processing (NLP) techniques to extract meaningful insights and information from textual data. This project specifically focuses on spam message filtering using machine learning algorithms.

Applications

1. Sentiment Analysis

Sentiment analysis is applied to determine the emotional sentiment expressed in a piece of text, whether it's positive, negative, or neutral. Businesses use sentiment analysis to monitor brand reputation, customer feedback, and public perception.

2. Spam Messages Filtering

Spam filtering is a classic application of machine learning in text analysis. The goal is to automatically detect and classify unsolicited and unwanted emails or messages as spam.

Model Building

Naive Bayes

Naive Bayes is a probabilistic machine learning algorithm based on Bayes' theorem, particularly well-suited for text classification tasks. It assumes independence among features, making it computationally efficient and straightforward to implement.

CountVectorizer

CountVectorizer is a text preprocessing technique used for converting text documents into numerical feature vectors. It creates a matrix where each row represents a document, and each column represents the count of a word or token in that document.

TF-IDF

TF-IDF (Term Frequency - Inverse Document Frequency) is a statistical measure that evaluates how relevant a word is to a document. It considers both the frequency of a word in a document and its inverse document frequency across a set of documents.

Hyperparameter Tuning

Hyperparameter tuning is the process of finding the best combination of hyperparameters for a machine learning model to achieve optimal performance. The goal is to find hyperparameters that result in the best generalization performance on unseen data.

Process Outline

1. Split Data
2. Choose the Model
3. Choose the Search Method (Grid, Randomized)
4. Perform Hyperparameter Search
5. Evaluate Performance
6. Select Best Hyperparameters
7. Retrain with Best Hyperparameters
8. Evaluate Final Model

Steps in Model Building

1. Data Cleaning

Ensure the dataset is free from inconsistencies, missing values, and irrelevant information.

2. Exploratory Data Analysis (EDA)

Analyze and visualize the dataset to gain insights into the distribution of spam and non-spam messages.

3. Data/Text Preprocessing

Prepare the text data by cleaning, tokenizing, and transforming it into numerical representations suitable for machine learning models.

4. Model Building

Train machine learning models (Naive Bayes, SVM, RF, LGR, KNN) on the preprocessed data.

5. Evaluation

Assess the performance of the models using appropriate metrics, such as accuracy, precision, recall, and F1 score.

6. Hyperparameter Tuning

Optimize the model's hyperparameters to improve its generalization performance.

7. Simple Website for Model Deployment

Create a simple website to deploy the final model for real-world usage.