Which algorithms are used in this news headline classification project?

The system implements and compares two algorithms: Multinomial Naive Bayes and Gradient Boosting Classifier. Gradient Boosting achieved superior performance with 87.4% accuracy and 0.916 ROC-AUC.

What is the accuracy of the news headline classification model?

The Gradient Boosting model achieves 87.4% accuracy with an F1-Score of 87.0% and ROC-AUC of 91.6%, demonstrating excellent predictive capability for text classification.

Who can benefit from this news headline classification system?

News organizations, content aggregators, media monitoring services, researchers, and educators can use this system for automatic content tagging, personalized news delivery, and information retrieval.

What technologies are used to build this project?

The project is built using Python with Scikit-learn, pandas, and NumPy for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

News Headline Classification Using Machine Learning

The News Headline Classification Using Machine Learning system is a comprehensive machine learning solution that classifies news headlines into four categories: Politics, Sports, Business, and Technology. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Naive Bayes and Gradient Boosting with TF-IDF features, achieving 87.4% accuracy and 0.916 ROC-AUC.

The system utilizes a dataset of over 10,000 news headlines from Reuters and Associated Press, containing Title, Description, and Class Index. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time news headline classification, enabling automatic content organization and personalized news delivery.

Python 3.8+ Machine Learning Naive Bayes Gradient Boosting NLP TF-IDF Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class News Classification
Gradient Boosting (87.4% Accuracy)
Naive Bayes (82.1% Accuracy)
TF-IDF with N-gram Features
4 News Categories (Politics, Sports, Business, Tech)
Interactive Visualizations
Real-time Headline Classification
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Multinomial Naive Bayes

Probabilistic classifier with alpha=0.1, fit_prior=True for text classification

🎯 Accuracy: 82.1%

🚀 Gradient Boosting

Ensemble of decision trees with n_estimators=200, max_depth=6, learning_rate=0.1

🎯 Accuracy: 87.4%

📝 TF-IDF Vectorization

Text feature extraction with max_features=5000, n-gram range (1,2), sublinear_tf=True

📊 Top Feature: "election"

📊 Cross-Validation

5-fold stratified CV with leak-free evaluation

📊 CV Mean: 86.2%

Methodology & Workflow

1 Data Collection
10,000+ news headlines with 4 categories

2 Data Preprocessing
Cleaning, encoding, feature extraction (TF-IDF)

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Naive Bayes and Gradient Boosting with 5-fold CV

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

6 Web Deployment
Flask web app with real-time classification

Model Performance Comparison

Metric	Naive Bayes	Gradient Boosting	Best
Test Accuracy	82.1%	87.4%	Gradient Boosting
Test Precision (Weighted)	81.9%	87.2%	Gradient Boosting
Test Recall (Weighted)	82.1%	87.4%	Gradient Boosting
Test F1-Score (Weighted)	81.9%	87.0%	Gradient Boosting
Test ROC-AUC	87.2%	91.6%	Gradient Boosting
CV Mean Accuracy	81.9%	86.2%	Gradient Boosting

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial News Headline Dataset 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