I will build a recommendation systems

Want to show your users the right items at the right time? I will build a recommendation system that suggests the most relevant products, jobs, articles or movies from your own data, so people find what they need faster. How it works: I turn your item descriptions into numerical features (TF-IDF) and rank items by how similar they are to what a user searches, uploads or likes. This is called a content-based recommender, and it works well when you have text data like titles, descriptions or resumes. What you get:

- A working recommender built in Python with Scikit-learn

- Data cleaning and preprocessing

- Clean source code

- Higher packages add a Streamlit app you can try,

-deployment and documentation See my work:

I built a resume-based job recommender with PDF upload and ranked matches. It is live in my portfolio. What I need from you: -

A CSV or Excel file of your items with a text column (title and description) Tools:

Python,

Scikit-learn,

Pandas,

NumPy,

Streamlit.

Message me before ordering if you are unsure what fits.