piedomains 0.0.3

Predict categories based domain names and it's content

This package used Shallalist dataset to train the model. Scrapped homepages of the domains mentioned in above dataset. This package predicts the category based on the domain name and its content.

Install

We strongly recommend installing piedomains inside a Python virtual environment (see venv documentation)

pip install piedomains

General API

1. domain.pred_shalla_cat will take array of domains and predicts category.

Examples

from piedomains import domain
domains = [
    "yahoo.com",
    "forbes.com",
    "xvideos.com",
    "last.fm",
    "facebook.com",
    "bellesa.co",
    "marketwatch.com"
]
result = domain.pred_shalla_cat(domains)
print(result)

Output -

              name  pred_label  label_prob  used_domain_content                                   all_domain_probs
0        yahoo.com       forum    0.020146                 True  {'adv': 0.01915626, 'aggressive': 0.01823743, ...
1       forbes.com        news    0.024546                 True  {'adv': 0.019166455, 'aggressive': 0.018233137...
2      xvideos.com        porn    0.023876                 True  {'adv': 0.021233741, 'aggressive': 0.018176463...
3          last.fm       music    0.021811                 True  {'adv': 0.018456697, 'aggressive': 0.018200265...
4     facebook.com        news    0.020649                 True  {'adv': 0.018711654, 'aggressive': 0.01822204,...
5       bellesa.co        porn    0.022348                 True  {'adv': 0.019042958, 'aggressive': 0.018210754...
6  marketwatch.com     finance    0.024208                 True  {'adv': 0.018741421, 'aggressive': 0.01818002,...

Functions

We expose 1 function, which will take array of domains and predicts category.

• domain.pred_shalla_cat(input)

  • What it does:

    • predicts category based on domain and its content

  • Output

    • Returns panda dataframe with label and probabilities

Authors

Rajashekar Chintalapati and Gaurav Sood

Contributor Code of Conduct

The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.

License

The package is released under the MIT License.