Given the historical purchases of a user by category, we would like to know the likelyhood of them purchasing items from other categories.
This is called Market Basket Analysis
Market Basket Analysis can be used in fraud detection or to suggest other items that a customer may be interested in purchasing.
This demo will allow you to score various baskets based on some pre-populated users and profiles.
docker-compose up
Access the demo through your browser
- Select a user to act as
- View the user profile
- Click on the cart tab and add new items and score
- Select a different user and experiment with baskets
The scores vary between 0 and 1.
A score closer to 1 indicates that the basket of items by category is more likely to mirror broader purchasing patterns.
For example given the user profile "splurgenarrow" :
If they add Wireless this is a higly likely basket with a score of 0.99989
But they add Major_Appliances then it will be an unlikely baske with a score of 0.00417
Start Docker container
docker run -p 6379:6379 redislabs/redismod:edge
Setup Environment
python -m venv venv
source venv/bin/active
pip install -r requirements.txt
python3 app.py
User profiles are stored in Redis as hash data structures
After the user adds items to the cart for scoring, the cart is transformed into a tensor, scored by RedisAI and a confidence score is returned.
The data was pulled from Amazon reviews datasets and the model was built using reviews with verified purchases. Individual user profiles were compiled and analyzed to describe baskets that were trained to build the model that is included in this repo.