RBF-SVM Brand Verification Model

Mykyta Terentiev

ML Engineer

Matplotlib

scikit-learn

seaborn

Artificial Intelligence

RBF-SVM Brand Verification Model

A machine learning pipeline for brand verification using Radial Basis Function (RBF) kernel Support Vector Machines. This model helps identify whether a merchant's claimed brand name matches their actual business identity by analyzing text features and business attributes.

🎯 Overview

Brand verification is crucial for maintaining marketplace integrity and preventing fraud. This model combines text analysis (TF-IDF vectorization) with categorical features to classify brand-merchant relationships as "verified" or "not_verified".

Key Features

Advanced Text Processing: TF-IDF vectorization of brand names, addresses, and combined text features

Categorical Encoding: One-hot encoding for geographical and business type features

Class Imbalance Handling: Sample weighting based on brand frequency to handle long-tail distributions

Hyperparameter Optimization: GridSearchCV with cross-validation for optimal model performance

Comprehensive Evaluation: Detailed metrics including precision, recall, F1-score, ROC-AUC, and confusion matrices

🏗️ Architecture

├── src/
│   ├── config.py              # Configuration parameters
│   ├── data_loader.py         # Data loading and validation
│   ├── feature_engineering.py # Text processing and feature creation
│   ├── preprocessing.py       # Data scaling and splitting
│   ├── model.py              # RBF-SVM model implementation
│   ├── evaluation.py         # Model evaluation and metrics
│   ├── main.py               # Pipeline orchestration
│   └── utils.py              # Utility functions
├── data/                     # Dataset storage
├── results/                  # Model outputs and reports
├── models/                   # Trained model artifacts
└── requirements.txt          # Dependencies

📊 Model Performance

Based on the latest run with 1000 samples:

Training Performance

Accuracy: 99.7%

Precision: 100%

Recall: 99.6%

F1-Score: 99.8%

ROC AUC: 100%

Test Performance

Accuracy: 70.5%

Precision: 70.8%

Recall: 99.5%

F1-Score: 82.7%

ROC AUC: 55.9%

Model Details

Best Hyperparameters: C=1.0, gamma=1.0

Feature Dimensions: 471 features (TF-IDF + categorical)

Support Vectors: 849/850 training samples

Class Distribution: 719 verified, 281 not verified

🚀 Quick Start

Prerequisites

Python 3.8+
pip or conda package manager

Installation

Clone the repository

git clone <repository-url>
cd RBF-SVM-Brand-Verification-Model

Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Usage

Basic Usage

# Run the complete pipeline with default settings
python -m src.main

# Run with custom data file
python -m src.main --data-path data/your_dataset.csv

# Run without hyperparameter tuning (faster)
python -m src.main --no-tune

# Custom results directory
python -m src.main --results-dir custom_results/

Advanced Options

# Full command with all options
python -m src.main \
  --data-path data/custom_data.csv \
  --results-dir results/experiment_1/ \
  --no-tune \
  --no-save-model \
  --log-level DEBUG \
  --random-state 123

Programmatic Usage

from src.main import BrandVerificationPipeline
from pathlib import Path

# Initialize pipeline
pipeline = BrandVerificationPipeline(
    data_path=Path("data/dummy_data_1k.csv"),
    results_dir=Path("results/"),
    random_state=42
)

# Run complete pipeline
results = pipeline.run_complete_pipeline(
    tune_hyperparameters=True,
    save_model=True,
    save_results=True
)

# Access results
print(f"Best F1 Score: {results['test_evaluation']['basic_metrics']['f1_binary']:.3f}")
print(f"Model saved to: {results['model_path']}")

📁 Data Format

The model expects a CSV file with the following columns:

Column Type Description brand_name string Official brand name brand_freq numeric Brand frequency/popularity score snowdrop_name string Merchant's claimed name address_norm string Normalized business address country string Business country city string Business city website string Business website website_match binary Whether website matches brand target string "verified" or "not_verified" calculated_weights numeric Sample weights for training split string "TRAIN", "VALIDATE", or "TEST"

Sample Data Generation

Generate dummy data for testing:

python generate_dummy_data.py --num-samples 1000 --output data/test_data.csv

⚙️ Configuration

Key configuration parameters in src/config.py:

Model Parameters

# Hyperparameter tuning grids
C_GRID = [0.1, 1.0, 10.0, 100.0]
GAMMA_GRID = ["scale", "auto", 0.001, 0.01, 0.1, 1.0]

# Cross-validation settings
CV_FOLDS = 5
RANDOM_STATE = 42

Feature Engineering

# TF-IDF parameters
MAX_FEATURES_TFIDF = 1000
MIN_DF_TFIDF = 2
MAX_DF_TFIDF = 0.8
NGRAM_RANGE = (1, 2)

Text Processing

# Features for TF-IDF vectorization
TEXT_FEATURES = [
    "brand_name_processed",
    "address_norm_processed", 
    "combined_text"
]

# Categorical features for encoding
CATEGORICAL_FEATURES = ["country", "city"]

📈 Results and Evaluation

The pipeline generates comprehensive evaluation reports:

Generated Files

pipeline_summary.json - Complete pipeline results and metrics

evaluation_results.json - Detailed evaluation metrics

rbf_svm_model.joblib - Trained model artifact

confusion_matrices.png - Visualization of model performance

classification_reports.txt - Detailed classification metrics

Key Metrics Tracked

Basic Metrics: Precision, Recall, F1-Score, Accuracy

Probability Metrics: ROC-AUC, Average Precision

Class Analysis: Per-class performance breakdown

Confusion Matrix: True/False positive/negative analysis

Cross-Validation: Grid search results with CV scores

🔧 Development

Project Structure Details

src/
├── config.py           # All configuration parameters and constants
├── data_loader.py      # CSV loading, validation, schema checking
├── feature_engineering.py  # TF-IDF, text processing, categorical encoding
├── preprocessing.py    # Scaling, splitting, sample weight extraction
├── model.py           # RBF-SVM implementation with hyperparameter tuning
├── evaluation.py      # Comprehensive evaluation metrics and reporting
├── main.py           # Pipeline orchestration and CLI interface
└── utils.py          # Logging, file operations, text cleaning utilities

Testing

# Run unit tests
python -m pytest tests/ -v

# Run with coverage
python -m pytest tests/ --cov=src --cov-report=html

Code Quality

# Format code
black src/

# Lint code  
flake8 src/

# Type checking
mypy src/

📋 Dependencies

Core Requirements

numpy (≥1.21.0): Numerical computing

pandas (≥1.3.0): Data manipulation and analysis

scikit-learn (≥1.0.0): Machine learning algorithms

scipy (≥1.7.0): Scientific computing utilities

Visualization

matplotlib (≥3.4.0): Plotting and visualization

seaborn (≥0.11.0): Statistical data visualization

Development Tools

pytest (≥6.0.0): Testing framework

black (≥21.0.0): Code formatting

flake8 (≥3.9.0): Code linting

mypy (≥0.910): Static type checking

See requirements.txt for complete dependency list with version constraints.

🤝 Contributing

Fork the repository

Create a feature branch: git checkout -b feature-name

Make your changes with proper documentation

Add tests for new functionality

Run quality checks: black, flake8, mypy, pytest

Submit a pull request with detailed description

Contribution Guidelines

Follow PEP 8 style guidelines

Add type hints for all functions

Write comprehensive docstrings

Include unit tests for new features

Update documentation as needed

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔮 Future Enhancements

Deep Learning Integration: Experiment with transformer-based embeddings

Feature Engineering: Add fuzzy string matching for brand name similarity

Model Ensemble: Combine SVM with other algorithms (Random Forest, XGBoost)

Real-time Inference: Add API endpoint for live brand verification

Explainability: Integrate SHAP values for model interpretability

AutoML: Automated feature selection and hyperparameter optimization

Data Drift Detection: Monitor model performance over time

📞 Support

For questions, issues, or contributions:

Issues: Open a GitHub issue for bugs or feature requests

Documentation: Check the inline docstrings and comments

Performance: Review the pipeline_summary.json for detailed metrics

Note: This model achieves high recall (99.5%) on the test set, making it excellent for catching verified brands while maintaining reasonable precision (70.8%). The high training performance vs. moderate test performance suggests some overfitting, which is common with SVMs on small datasets but doesn't significantly impact the model's practical utility for brand verification tasks.

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Posted Jul 21, 2025

Developed an RBF-SVM model for brand verification with high recall and precision.

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