InstaGuard uses a trained Logistic Regression model to analyze behavioral patterns and profile metadata — classifying Instagram accounts as authentic or inauthentic with 95.2% accuracy.
InstaGuard analyzes account metadata patterns that strongly correlate with inauthentic behavior.
Enter account metadata manually or upload a CSV/JSON file containing multiple accounts for batch processing.
Our Logistic Regression model, trained on labeled Instagram data, analyzes behavioral and structural patterns.
Receive a real/fake classification with confidence score, probability breakdown, and plain-English reasoning.
Manually input any account's metadata and receive an instant prediction with confidence gauge, probability breakdown, and feature influence visualization.
Upload CSV or JSON files to analyze hundreds of accounts simultaneously with exportable results.
Every prediction includes a confidence percentage and High/Medium/Low classification so you know how certain the model is.
See exactly which features drove the prediction with animated influence bars and plain-English explanation.
One-click demo accounts — bot, real, and edge case — to explore the model's behavior without manual entry.
The model was trained and validated on a labeled Instagram account dataset, achieving strong performance across all key classification metrics.
Read Full Methodology →Try the live dashboard — no setup required.