What is the Intelligent SMS and Email Spam Detection System?

It is a machine learning-based system that automatically classifies SMS and email messages as spam or legitimate (ham) using advanced algorithms like Random Forest and KNN with SMOTE for handling class imbalance.

Which algorithms are used in this spam detection project?

The system implements and compares two classification algorithms: Random Forest and K-Nearest Neighbors (KNN). Random Forest achieved the best performance with 97.8% accuracy and an F1-score of 0.978.

What features are used for spam detection?

The system uses 12 features including text length, word count, exclamation count, question count, capital count, URL count, digit count, currency count, and other structural characteristics extracted from messages.

What is the accuracy of this spam detection model?

The Random Forest model achieves 97.8% test accuracy with precision of 97.8%, recall of 97.8%, F1-score of 0.978, and ROC-AUC of 0.993, making it highly reliable for spam filtering.

Who can benefit from this spam detection system?

Email service providers, SMS gateways, mobile network operators, messaging platforms, and end-users can benefit from automated spam filtering to reduce exposure to unsolicited and potentially harmful messages.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, imbalanced-learn (SMOTE), Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

Intelligent SMS and Email Spam Detection Using Machine Learning

The Intelligent SMS and Email Spam Detection Using Machine Learning system is a comprehensive machine learning solution that automatically classifies messages as spam or legitimate (ham) using advanced algorithms and feature engineering. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Random Forest and K-Nearest Neighbors (KNN) with SMOTE to achieve 97.8% accuracy.

The system leverages 12 features including text length, word count, exclamation count, capital count, URL count, and other structural characteristics extracted from messages. It provides interactive visualizations, feature importance analysis, model comparison, and real-time spam detection to help protect users from unsolicited and potentially harmful messages.

Python 3.8+ Machine Learning NLP Random Forest KNN SMOTE Scikit-learn imbalanced-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Binary Spam/Ham Classification
Random Forest (97.8% Accuracy)
KNN (94.2% Accuracy)
SMOTE for Class Imbalance
12 Feature Engineering
Feature Importance Analysis
Real-time Spam Prediction
Model Performance Comparison
3-Fold Leak-Free CV
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 100 trees, robust to overfitting and noise

🎯 Accuracy: 97.8%

📊 K-Nearest Neighbors

Instance-based learning with k=5, simple and interpretable

🎯 Accuracy: 94.2%

🔄 SMOTE

Synthetic Minority Over-sampling for handling class imbalance

📊 k-neighbors: 5

🔍 3-Fold Leak-Free CV

Cross-validation with SMOTE inside each fold

📊 RF: 0.985 ± 0.008

Methodology & Workflow

1 Data Loading & Inspection
SMS dataset with 5,574 messages (13% spam)

2 Text Preprocessing
Cleaning, lowercasing, punctuation removal

3 Feature Engineering
12 features including length, word count, capitalization

4 Model Training
Random Forest & KNN with SMOTE pipelines

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 3-fold CV

6 Web Deployment
Flask web app for real-time spam detection

Model Performance Comparison

Metric	Random Forest	KNN	Best
Accuracy	0.978	0.942	Random Forest
Precision	0.978	0.943	Random Forest
Recall	0.978	0.942	Random Forest
F1-Score	0.978	0.943	Random Forest
ROC-AUC	0.993	0.953	Random Forest
CV Mean (3-Fold)	0.985	0.961	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (5,574 messages) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

Check Payment Status

LIMITED TIME OFFER -70%

Complete Project Package Lifetime Access

Original Price

₹9,999

Today's Price ₹2,999 💎 Save ₹7,000

You Save ₹7,000 (70% OFF)

Complete Source Code

Documentation & PPT

Video Tutorial

24/7 Expert Support

Scan & Pay with UPI

SECURE

Payee Thirumalai Kumar

UPI ID 9600095045@icici

Amount ₹2,999

Submit Your Payment

100% SECURE

Full Name *

Email *

Phone Number *

College/University *

Degree

Passout Year

Department

Payment Details

Enter your UPI Transaction ID and upload payment screenshot for verification.

Transaction ID *

Payment Screenshot *

Click to upload screenshot JPG, PNG, GIF, PDF • Max 5MB

📚 Academic & Research Support Services

Need help with Thesis, Dissertation, Assignments, or PhD Research? We've got you covered!

📝

Thesis & Dissertation

Complete thesis writing, research guidance, and formatting support

Expert Help

📄

Assignment Help

Quality assignment writing, editing, and proofreading services

100% Original

🔬

PhD Research

Research proposal, literature review, data analysis & publication

PhD Level

📊

Project Guidance

Academic projects, mini projects, and final year project support

Hands-on

📞 Need custom support? Contact us directly!

Chat on WhatsApp → Email Us

Final Year Projects