Which algorithms are used in this toxic content detection project?

The system implements and compares two algorithms: Random Forest and XGBoost. XGBoost achieved superior performance with 93.8% accuracy and 86.56% F1-score.

What is the accuracy of the toxic content detection model?

The XGBoost model achieves 93.8% accuracy with 87.19% precision, 85.93% recall, and an F1-score of 86.56%, demonstrating strong predictive capability for content moderation.

Who can benefit from this toxic content detection system?

Social media platforms, online communities, content moderators, forum administrators, and digital content managers can use this system for automated content moderation and user safety.

What technologies are used to build this project?

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

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Machine Learning

Toxic and Abusive Online Content Detection Using Machine Learning

The Toxic and Abusive Online Content Detection Using Machine Learning system is a comprehensive machine learning solution that detects toxic and abusive content in online comments. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Random Forest and XGBoost with leak-free SMOTE, achieving 93.8% accuracy and 86.56% F1-score.

The system utilizes the Toxic Comment Classification Dataset from Wikipedia talk pages, containing approximately 159,571 comments labeled across six toxicity categories. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time toxic content prediction, enabling social media platforms and online communities to maintain safe digital environments.

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

Key Features:

Multi-Model Toxic Content Detection
XGBoost (93.8% Accuracy)
Random Forest (92.5% Accuracy)
TF-IDF with N-gram Features
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time Content Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=12, class_weight='balanced'

🎯 Accuracy: 92.5%

⚡ XGBoost

Gradient boosting with n_estimators=100, max_depth=6, learning_rate=0.1

🎯 Accuracy: 93.8%

📝 TF-IDF Vectorization

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

📊 Top Feature: "stupid"

🔄 SMOTE Oversampling

Synthetic minority oversampling applied leak-free inside CV folds

📊 CV Mean: 93.58%

Methodology & Workflow

1 Data Collection
159,571 comments from Wikipedia talk pages

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

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Random Forest and XGBoost with leak-free SMOTE

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

6 Web Deployment
Flask web app with real-time content moderation

Model Performance Comparison

Metric	Random Forest	XGBoost	Best
Test Accuracy	92.5%	93.8%	XGBoost
Test Precision	85.67%	87.19%	XGBoost
Test Recall	84.32%	85.93%	XGBoost
Test F1-Score	84.99%	86.56%	XGBoost
Test ROC-AUC	92.81%	94.21%	XGBoost
CV Mean ROC-AUC	92.14%	93.58%	XGBoost

Project Package Includes:

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

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