Introduction

This project focuses on developing an email spam detection system using NLP techniques. The objective is to accurately classify emails as either spam or non-spam (ham) based on their content.

Project Overview

The goal of this project is to build a machine learning model that can effectively identify and classify spam emails. The project involves the following steps:

1. Data Preprocessing: The email dataset is preprocessed by cleaning the text, removing stop words, and performing tokenization.

2. Feature Extraction: Textual data is transformed into numerical feature vectors using the TF-IDF (Term Frequency-Inverse Document Frequency) technique. This helps capture the importance of each word in the emails relative to the entire corpus.

3. Model Training: A logistic regression model is trained on the preprocessed and feature-extracted data. Logistic regression is a popular algorithm for binary classification tasks.

4. Model Evaluation: The trained model is evaluated using various metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into the model’s performance and its ability to correctly classify spam and non-spam emails.

Technologies used: Exploratory Data Analysis, Data Visualization, NLP, Machine Learning, Supervised Learning Classification

GitHub Repository Link: Email Spam Detection