Deep Learning for Email Classification with LSTM and Word2Vec

In this project, we’ll learn to construct a robust spam email detection model by using data preprocessing techniques, Word2Vec embeddings, and LSTM models.

You will learn to:

Gain proficiency in natural language processing (NLP) techniques for text-based classification tasks.

Understand the Word2Vec technique to generate word embeddings for text data.

Construct and train a LSTM (Long Short-Term Memory) model for spam email detection using Keras.

Master the compilation, training, and evaluation steps in deep learning model development.

Skills

Machine Learning

Deep Learning

Natural Language Processing

Prerequisites

Knowledge of machine learning concepts and techniques

Familiarity with data manipulation using pandas

Basic understanding of deep learning concepts

Exposure to text data preprocessing techniques

Technologies

Numpy

Pandas

Gensim

Tensorflow

Scikit-learn

Project Description

In this project, we’ll learn how to build an effective model for discerning spam emails using natural language processing (NLP) and deep learning. We’ll start off with some data preprocessing steps, which include extracting email texts and labels from the dataset, splitting the dataset, and tokenizing sequences for model input.

We’ll then train a Word2Vec model to generate word embeddings and construct a Long Short-Term Memory (LSTM) model using these embeddings for robust spam detection. The model’s construction and training, along with evaluation and reporting tasks, will provide a comprehensive journey into email classification.

By the end of this project, we will have a well-rounded understanding of spam email detection and a functional model ready for deployment.

Project Tasks

Get Started

Task 0: Introduction

Task 1: Import Libraries

Task 2: Load the Dataset

Data Preprocessing

Task 3: Extract Email Texts and Labels

Task 4: Split the Dataset

Task 5: Tokenize and Pad Sequences

Text Preprocessing and Model Building

Task 6: Train a Word2Vec Model

Task 7: Prepare the Embedding Matrix

Task 8: Build an LSTM Model

Model Construction and Training

Task 9: Compile the Model

Task 10: Train the Model

Task 11: Evaluate the Model

Evaluation and Reporting

Task 12: Generate Predictions

Task 13: Print the Classification Report

Congratulations!

Relevant Courses

Use the following content to review prerequisites or explore specific concepts in detail.