Master Text Classification with Python 3

Table of Contents

1. Introduction
2. Installation and Setup
3. Loading the Dataset
4. Exploratory Data Analysis
5. Data Pre-processing
6. Text Classification
   1. Lowercasing
   2. Tokenization
   3. Stop Words Removal
   4. Punctuation Removal
   5. Stemming
7. Word Cloud Analysis
   1. Spam Word Cloud
   2. Ham Word Cloud
8. Most Common Spam Messages
9. Text Vectorization
   1. Bag of Words (BoW)
   2. TF-IDF (Term Frequency-Inverse Document Frequency)
10. Model Development
    1. Naive Bayes Algorithm
    2. Multinomial Naive Bayes
    3. Bernoulli Naive Bayes
    4. Logistic Regression
    5. Support Vector Machine
    6. Decision Tree
    7. K-Nearest Neighbors
    8. Random Forest
    9. AdaBoost
    10. XGBoost
11. Model Evaluation
12. Conclusion

Introduction

In this article, we will explore the implementation of text classification for spam emails and non-spam emails. We will cover installation and setup, loading the dataset, exploratory data analysis, data pre-processing, text classification techniques, word cloud analysis, text vectorization, model development, and model evaluation.

Installation and Setup

Before getting started, make sure to have Anaconda installed on your system. You can follow the instructions provided in the video tutorial for detailed guidance on installation. Once Anaconda is installed, open Anaconda Prompt and navigate to the desired folder using the cd command. Launch Jupyter Notebook by entering the command jupyter notebook. This will open the Jupyter Notebook interface where You can access the necessary files and libraries.

Loading the Dataset

The dataset for this project is stored in an Excel sheet with two columns - "Result" and "Text". The "Result" column denotes whether a message is spam or non-spam, and the "Text" column contains the actual text of the message. We will load this dataset and perform exploratory data analysis to gain insights and understand the data better.

Exploratory Data Analysis

Before diving into text classification, it is important to explore the dataset and analyze the distribution of spam and non-spam messages. We will examine the number of samples for each class, plot histograms to Visualize the distribution, and analyze the most common words in spam and non-spam messages.

Data Pre-processing

To prepare the data for classification, we need to perform several pre-processing steps. These include converting the text to lowercase, tokenizing the text into individual words, removing stop words and punctuation, and performing stemming to reduce words to their base form. These steps are crucial for improving the accuracy of our classification model.

Text Classification

Once the data has been pre-processed, we can proceed with text classification. In this step, we will convert the pre-processed text into numerical features using techniques like Bag of Words (BoW) and TF-IDF vectorization. These techniques help in representing text data in a format that machine learning models can understand and process.

Word Cloud Analysis

To gain further insights into the data, we will Create word clouds for both spam and non-spam messages. Word clouds visualize the most common words in a dataset, providing a visual representation of the frequency of occurrence for different words. By analyzing the word clouds, we can identify the most common words and gain a better understanding of the language used in spam and non-spam messages.

Most Common Spam Messages

In this section, we will examine the most common spam messages in the dataset. By analyzing the frequency of words in spam messages, we can identify Patterns and keywords that are frequently associated with spam. This analysis will provide valuable insights into the characteristics of spam messages.

Text Vectorization

Text vectorization is the process of converting textual data into numerical features that can be used by machine learning algorithms. We will explore two popular techniques for text vectorization: Bag of Words (BoW) and TF-IDF (Term Frequency-Inverse Document Frequency). These techniques help us represent the text in a format compatible with machine learning algorithms.

Model Development

With text vectorization in place, we can now develop our text classification models. We will train and test various classifiers including Naive Bayes, Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, Random Forest, AdaBoost, and XGBoost. By comparing the performance of these models, we can identify the most suitable algorithm for our task.

Model Evaluation

To evaluate the performance of our classification models, we will use a variety of evaluation metrics including accuracy, precision, recall, F1 score, and support. These metrics will help us determine the effectiveness of our models in accurately classifying spam and non-spam messages. We will also compare the performance of different classifiers Based on these metrics.

Conclusion

In conclusion, this article presented a comprehensive approach to text classification for spam detection. We covered various steps including data loading, exploratory data analysis, data pre-processing, text classification techniques, word cloud analysis, text vectorization, model development, and model evaluation. By following these steps, we were able to build and evaluate different classification models to accurately classify spam and non-spam messages.