AIMachine LearningNLP
Spam / Ham Email Detection
An NLP classification system using TF-IDF vectorization and Logistic Regression to classify messages as spam or ham.
2026Live
01Project Overview
Overview
A supervised NLP classification project that converts email text into numerical features and predicts spam or ham.
02Problem Space
The Problem
Email systems need automated ways to distinguish unwanted messages from legitimate communication.
03Project Objectives
Goals
Preprocess email messages
Convert text into numerical features
Train a binary classifier
Evaluate classification performance
04System Design
Architecture
01Email dataset
02Text preprocessing
03TF-IDF vectorization
04Logistic Regression
05Prediction and evaluation
05Engineering Challenges
Challenges
Text normalization
Feature extraction
Binary classification
06Implementation
Solutions
TF-IDF feature representation
Logistic Regression classifier
Train/test evaluation
07Tech Stack
Technology
Python
Pandas
NumPy
Scikit-learn
TF-IDF
Logistic Regression
NLP
Jupyter Notebook
08What I Learned
Lessons Learned
TF-IDF is an effective baseline for traditional text classification
Simple models can work well on structured NLP problems
09What's Next
Future Improvements
Compare Naive Bayes and SVM
Deploy as an API
Add a web interface
Improve preprocessing