Technical-SEO-Detector: Technical SEO Issue Detection

Type: Commercial | Domain: SEO, Technical SEO
Hugging Face: syeedalireza/technical-seo-detector

Classify or detect technical SEO issues: redirect chains, thin content, duplicate signals, etc., from URL and page data.

Author

Alireza Aminzadeh

Problem

Automated detection of technical issues (redirect chains, thin content, canonical/duplicate problems) speeds up audits.

Approach

Input: URL, redirect_chain_length, content_length, status_code, canonical_match, etc.
Output: Issue type (redirect_chain, thin_content, duplicate, ok) or multi-label.
Models: XGBoost/LightGBM on tabular features; optional text classifier for “thin” from content snippet.

Tech Stack

Category	Tools
ML	scikit-learn, XGBoost
Data	pandas, NumPy
Evaluation	sklearn metrics

Setup

pip install -r requirements.txt

Usage

python train.py
python inference.py --input data/crawl_export.csv --output issues.csv

Project structure

07_technical-seo-detector/
├── config.py
├── train.py           # XGBoost classifier for issue_type
├── inference.py       # Predict issue type for crawl export
├── requirements.txt
├── .env.example
├── data/
│   └── crawl_export.csv   # Sample: features + issue_type
└── models/

Data

Sample data (included): data/crawl_export.csv — columns: redirect_chain_length, content_length, status_code, word_count, internal_links, issue_type (e.g. ok, redirect_chain, thin_content).
Set DATA_PATH in .env if using another file.

License

MIT.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

syeedalireza
/

technical-seo-detector