README.md

metadata

title: Recommender system and customer segmentation
emoji: 🐨
colorFrom: purple
colorTo: blue
sdk: streamlit
sdk_version: 1.10.0
app_file: recommender_system.py
pinned: false
license: mit

Recommender system and customer segmentation

Demo with recsys and clustering for the online retail dataset.

Objective

Recommender system:

1. interactively select a user
2. show all the recommendations for the user
3. explain why we get these suggestions (which purchased object influences the most)
4. plot the purchases and suggested articles 

Clustering:

1. compute the user clustering
2. plot users and their clusters
3. explain the meaning of the clusters (compute the mean metrics or literally explain them)

Setup

In your terminal run:

# Enable the env
source .venv/bin/activate

# Install the dependencies

pip install -r requirements.txt

# Or install the freezed dependencies from the requirements_freezed.txt

# You are ready to rock!

Run

In your terminal run:

streamlit run recommender_system.py

# Now the defualt browser will be opened with 
# the stramlit page. It you want to customize the
# execution of streaming, refer to its documentation.

Resources

• streamlit
• implicit, recsys library
• t-sne guide
• RFM segmentation