InstaGuard uses an XGBoost machine learning model to analyze behavioral patterns and profile metadata — classifying Instagram accounts as authentic or inauthentic with 99.2% accuracy.
InstaGuard analyzes account metadata patterns that strongly correlate with inauthentic behavior, then uses ML predictions to deliver a final verdict.
Enter 11 account features manually — including profile picture, follower counts, bio length, and username patterns — or upload a CSV for batch analysis.
An XGBoost model trained on 5,000 samples analyzes 11 behavioral and structural features to compute a fake probability score for each account.
Receive a real/fake verdict with confidence score, feature influence visualization, and a plain-English explanation of the prediction.
Input 11 account features including profile picture, privacy status, and external URL — and receive an instant ML prediction with confidence gauge and feature influence visualization.
Upload CSV or JSON files to analyze hundreds of accounts simultaneously with exportable results.
XGBoost classifier trained with GridSearchCV hyperparameter tuning across 5-fold cross-validation, achieving 99.2% accuracy on the held-out test set.
See exactly which of the 11 features drove the prediction with animated influence bars and a plain-English explanation of why the model decided as it did.
One-click demo accounts — bot, real, and edge case — to explore the model's behavior without manual entry.
The XGBoost model was trained on 5,000 labeled Instagram profiles with stratified splitting, achieving near-perfect performance across all key classification metrics.
Read full methodology →Try the live dashboard — no setup required.