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nahiar/
instagram-bot-detection-v2

Name: instagram-bot-detection-v2
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INSTAGRAM Bot Detection Model

Overview

This directory contains a trained Random Forest classifier for detecting bot accounts on Instagram.

Model Version: v2 Training Date: 2025-12-30 11:38:28 Framework: scikit-learn 1.5.2 Algorithm: Random Forest Classifier with GridSearchCV Hyperparameter Tuning

📊 Model Performance

Final Metrics (Test Set)

Metric	Score
Accuracy	0.9788 (97.88%)
Precision	0.9923 (99.23%)
Recall	0.9652 (96.52%)
F1-Score	0.9786 (97.86%)
ROC-AUC	0.9979 (99.79%)
Average Precision	0.9923 (99.23%)

Model Improvement

Baseline ROC-AUC: 0.9982
Tuned ROC-AUC: 0.9979

🗂️ Files

File	Description
`instagram_bot_detection_v2.pkl`	Trained Random Forest model
`instagram_scaler_v2.pkl`	MinMaxScaler for feature normalization
`instagram_features_v2.json`	List of features used by the model
`instagram_metrics_v2.txt`	Detailed performance metrics report
`images/`	All visualization plots (13 images)
`README.md`	This file

🎯 Dataset Information

Training Configuration

Training Samples: 4,000
Test Samples: 1,000
Total Samples: 5,000
Features: 10
Cross-Validation Folds: 5
Random State: 42

Class Distribution

The model was trained with balanced class weights to handle any class imbalance in the dataset.

🔧 Model Architecture

Algorithm

Random Forest Classifier - An ensemble learning method that operates by constructing multiple decision trees during training and outputting the class that is the mode of the classes (classification) of the individual trees.

Hyperparameters (Tuned via GridSearchCV)

Parameter	Value
`n_estimators`	100
`max_depth`	15
`min_samples_split`	2
`min_samples_leaf`	1
`max_features`	sqrt
`class_weight`	balanced

Feature Preprocessing

Scaler: MinMaxScaler (normalizes features to [0, 1] range)
Missing Values: Handled during data preprocessing
Feature Engineering: Custom features derived from account metadata

📈 Feature Importance

The model uses 10 features to detect bot accounts. Top 5 most important features:

Rank	Feature	Importance	Description
1	`profile_pic`	0.3500	Indicates if account has a profile picture
2	`followers`	0.2587	Number of followers
3	`username_num_ratio`	0.2029	Ratio of numbers in username
4	`follows`	0.0997	Ratio of followers to following count
5	`fullname_words`	0.0345	Number of accounts followed

All Features

profile_pic - Profile picture presence
username_num_ratio - Numeric character ratio in username
username_is_numeric - Username is entirely numeric
fullname_words - Number of words in full name
fullname_num_ratio - Numeric character ratio in full name
is_name_number_only - Full name contains only numbers
name_equals_username - Full name matches username
followers - Follower count
follows - Following count
followers_to_follows_ratio - Follower/following ratio

🚀 Usage

Prerequisites

pip install scikit-learn joblib numpy

Loading the Model

python

import joblib
import numpy as np

# Load model and scaler
model = joblib.load('instagram_bot_detection_v2.pkl')
scaler = joblib.load('instagram_scaler_v2.pkl')

# Example prediction
features = np.array([[
    1,      # profile_pic
    0.15,   # username_num_ratio
    0,      # username_is_numeric
    2,      # fullname_words
    0.0,    # fullname_num_ratio
    0,      # is_name_number_only
    0,      # name_equals_username
    1200,   # followers
    300,    # follows
    4.0     # followers_to_follows_ratio
]])

# Scale features
features_scaled = scaler.transform(features)

# Make prediction
prediction = model.predict(features_scaled)
probability = model.predict_proba(features_scaled)

print(f"Bot: {prediction[0] == 1}")
print(f"Probability: {probability[0][1]:.4f}")

API Integration

python

def predict_instagram_bot(account_data: dict) -> dict:
    """
    Predict if an Instagram account is a bot.

    Args:
        account_data: Dictionary with account features

    Returns:
        Dictionary with prediction and probability
    """
    features = np.array([[
        account_data['profile_pic'],
        account_data['username_num_ratio'],
        account_data['username_is_numeric'],
        account_data['fullname_words'],
        account_data['fullname_num_ratio'],
        account_data['is_name_number_only'],
        account_data['name_equals_username'],
        account_data['followers'],
        account_data['follows'],
        account_data['followers_to_follows_ratio']
    ]])

    features_scaled = scaler.transform(features)
    prediction = model.predict(features_scaled)[0]
    probability = model.predict_proba(features_scaled)[0]

    return {
        'is_bot': bool(prediction),
        'bot_probability': float(probability[1]),
        'confidence': float(max(probability))
    }

📊 Visualization

The images/ directory contains 13 visualization plots:

confusion_matrix.png - Classification confusion matrix
roc_curve.png - ROC curve with AUC score
precision_recall_curve.png - Precision-recall trade-off
feature_importance.png - Feature importance ranking
learning_curve.png - Model learning curve
class_distribution.png - Training data class distribution
prediction_distribution.png - Prediction score distribution
calibration_curve.png - Probability calibration
cv_scores.png - Cross-validation scores
top_features.png - Top 10 features
correlation_matrix.png - Feature correlation heatmap
threshold_analysis.png - Classification threshold analysis
model_comparison.png - Baseline vs tuned model comparison

🎓 Model Training

Training Process

Data Preprocessing: Feature engineering and normalization
Train-Test Split: 80/20 split with stratification
Hyperparameter Tuning: GridSearchCV with 5-fold cross-validation
Model Selection: Best parameters based on ROC-AUC score
Evaluation: Comprehensive metrics on held-out test set

Cross-Validation

Mean ROC-AUC: 0.9979
Folds: 5
Strategy: Stratified K-Fold

⚠️ Limitations

Data Dependency: Model performance depends on feature quality and data accuracy
Feature Availability: All 10 features must be available for prediction
Temporal Drift: Instagram's platform and bot behavior may change over time
Privacy: Ensure compliance with Instagram's terms of service when collecting data
Threshold Sensitivity: Default threshold is 0.5; may need adjustment based on use case

📝 License

This model is released under the Apache License 2.0.

🔄 Version History

v2 (2025-11-27): Current version with hyperparameter tuning
- ROC-AUC: 0.9979
- Accuracy: 97.88%
- 10 features

📧 Contact & Citation

If you use this model in your research or application, please cite:

bibtex

@misc{instagram-bot-detection-v2,
  title={Instagram Bot Detection Model v2},
  author={Nahiar},
  year={2025},
  month={November},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/nahiar/instagram-bot-detection}}
}

🤝 Contributing

For issues, improvements, or questions, please contact the model maintainer.

nahiar/instagram-bot-detection-v2