Avito Demand Prediction Challenge: open solution

This is an open solution to the Avito Demand Prediction Challenge.

More competitions 🎇

Check collection of public projects 🎁, where you can find multiple Kaggle competitions with code, experiments and outputs.

The goal

Create (entirely) open solution to this competition. We are opening not only the code, but also the process of creating it. Rules are simple:

• Clean code and extendable solution are - in the long run - much better than current public LB position
• This solution should - by itself - establish solid benchmark, as well as provide good base for your custom ideas and experiments.

Disclaimer

In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉.

Installation

1. clone this repository: git clone https://github.com/minerva-ml/open-solution-avito-demand-prediction.git
2. install requirements
3. register to Neptune (if you wish to use it)
4. update neptune.yaml configuration file with your data filepaths
5. run experiment

• with neptune:

$ neptune login
$ neptune experiment run --config neptune.yaml main.py -- train_evaluate_predict --pipeline_name main

collect submit from /output/solution-1 directory.

• with pure python:

$ python main.py -- train_evaluate_predict --pipeline_name main

collect submit from experiment_dir directory that was specified in neptune.yaml

Get involved

You are welcome to contribute your code and ideas to this open solution. To get started:

1. Check competition project here, on GitHub to see what we are working on right now.
2. Express your interest in particular task by writing comment in this task, or by creating new one with your fresh idea.
3. We will get back to you quickly in order to start working together.
4. Check CONTRIBUTING for some more information.

User support

There are several ways to seek help:

1. Kaggle discussion is our primary way of communication.
2. Read project's Wiki, where we publish descriptions about the code, pipelines and supporting tools such as neptune.ml.
3. Submit an issue directly in this repo.