Skip to content

Analyze your marketing data by AI & ML algorithms without a single-line of code.

Notifications You must be signed in to change notification settings

catyung/AI-Marketer

 
 

Repository files navigation

What is AI Marketer ?

AI Marketer is an AI-enabled marketing analytics tool that uses only a few clicks to generate insightful data. Its for online marketer to analyze marketing data by AI & ML algorithms without a single-line of code.

By using our preset marketing task, you can get answer from unstructured data in numbers, text, tables, and charts.

Website :

www.ai-marketer.tech

Demo :

http://www.app.ai-marketer.tech:8501/

How to start the streamlit server ?

streamlit run app.py
OR
python -m streamlit run app.py

Why we create AI Marketer ?

To help marketer with no statistic / data analytics background to analyze data and handling routine marketing task in just a few clicks

Tutorial :

https://studio.youtube.com/channel/UCvmEPC9fUfY8L2-v9IV1i4w

What packages do we use ?

AI Marketer is a non-profit open-source project, we build AI Marketer with a lot of help from other open source packages :

Front end :

Streamlit (https://streamlit.io/)

Machine Learning & AI packages :

PyCaret (https://pycaret.org/)

Transformers (https://huggingface.co/docs/transformers/index)

Bertopic (https://maartengr.github.io/BERTopic/index.html)

Prophet (https://facebook.github.io/prophet/)

SpaCy (https://spacy.io/)

Top2Vec (https://github.com/ddangelov/Top2Vec)

Others :

Google Trans (https://github.com/ssut/py-googletrans)

Plotly (https://plotly.com/)

Creator & Contributor

Super Chain (www.super-chain.tech)

LAU, Ching Ming, Samuel (Github : samuellau0802)

Motaz Saidani (Github : Motaz-Saidani)

We welcome your contribution anytime

Collabration & Contact

Please feel free to contact us at : [email protected]

Version

Beta

About

Analyze your marketing data by AI & ML algorithms without a single-line of code.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 98.7%
  • CSS 1.3%