What is Fake Online Review Detection Using Machine Learning?

It is a machine learning-based system that automatically detects fake or counterfeit online reviews from genuine ones. It helps e-commerce platforms, review moderators, and consumer protection agencies identify fraudulent reviews that mislead consumers and manipulate product ratings.

Which algorithms are used in this fake review detection project?

The system implements and compares two classification algorithms: K-Nearest Neighbors (KNN) and Decision Tree. The Decision Tree classifier achieved the best performance with 92.5% accuracy and an F1-Score of 0.925.

What features are used for fake review detection?

The system uses 11 engineered features including text length, word count, rating, exclamation count, question count, period count, uppercase ratio, has quotes, ellipsis count, repeated characters, and has digits. Review length and rating are the most predictive features.

What is the accuracy of this fake review detection model?

The Decision Tree model achieves 92.5% test accuracy with a precision of 0.925, recall of 0.925, F1-Score of 0.925, and ROC-AUC of 0.957, making it highly reliable for fake review detection.

Who can benefit from this fake review detection system?

E-commerce platforms can use it to moderate reviews, review aggregators can filter fake content, consumer protection agencies can identify fraudulent practices, and researchers can study opinion spam patterns.

What technologies are used to build this project?

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

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Machine Learning

Fake Online Review Detection Using Machine Learning

The Fake Online Review Detection Using Machine Learning system is a comprehensive machine learning solution that automatically distinguishes between genuine and fake online reviews. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like K-Nearest Neighbors (KNN) and Decision Tree with comprehensive feature engineering to achieve 92.5% accuracy.

The system leverages 11 engineered features including text length, word count, rating, punctuation patterns (exclamation, question, period counts), uppercase ratio, and other textual indicators. It provides interactive visualizations, feature importance analysis, model comparison, and real-time review classification to help e-commerce platforms and consumer protection agencies combat online review fraud.

Python 3.8+ Machine Learning KNN Decision Tree Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Binary Review Classification
Decision Tree (92.5% Accuracy)
KNN (90.8% Accuracy)
11 Feature Engineering
Feature Importance Analysis
Real-time Review Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Cross-Validation
Flask Web Application

Algorithms Used

🌳 Decision Tree Classifier

Tree-based algorithm with max_depth=10, captures hierarchical feature patterns

🎯 Accuracy: 92.5%

📊 K-Nearest Neighbors

Instance-based learning with k=5, distance weighting, intuitive classification

🎯 Accuracy: 90.8%

📝 Feature Engineering

11 features from text and metadata including length, punctuation, rating

📊 Features: 11

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 DT: 94.8% ± 0.1%

Methodology & Workflow

1 Data Loading & Inspection
Amazon review dataset with 4,849 records

2 Data Preprocessing
Text cleaning, handling missing values, label encoding

3 Feature Engineering
11 features from text and metadata

4 Model Training
KNN & Decision Tree with optimized parameters

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

6 Web Deployment
Flask web app for real-time review classification

Model Performance Comparison

Metric	Decision Tree	KNN	Best
Accuracy	0.925	0.908	Decision Tree
Precision	0.925	0.908	Decision Tree
Recall	0.925	0.908	Decision Tree
F1-Score	0.925	0.908	Decision Tree
ROC-AUC	0.957	0.941	Decision Tree
CV Mean (5-Fold)	0.948	0.935	Decision Tree

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (4,849 reviews) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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