Capstone Project

Multi Objective Recommender System

OTTO's Challenge on Kaggel

The goal of this project is to predict e-commerce clicks, cart additions, and orders through building a multi-objective recommender system based on previous events in a user session.

This challenge will help improve the shopping experience for everyone involved. Customers will receive more tailored recommendations while online retailers may increase their sales

ds-modeling-pipeline

Here you find a Skeleton project for building a simple model in a python script or notebook and log the results on MLFlow.

There are two ways to do it:

• In Jupyter Notebooks: We train a simple model in the jupyter notebook, where we select only some features and do minimal cleaning. The hyperparameters of feature engineering and modeling will be logged with MLflow

• With Python scripts: The main script will go through exactly the same process as the jupyter notebook and also log the hyperparameters with MLflow

Data used is the OTTO's Challenge on Kaggel.

Requirements:

• pyenv with Python: 3.9.8

Setup

Use the requirements file in this repo to create a new environment.

make setup

#or

pyenv local 3.9.8
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

The requirements.txt file contains the libraries needed for deployment.. of model or dashboard .. thus no jupyter or other libs used during development.

challenges and features to implement in the future

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