fernandus16/kaggle

Additional files for the Otto Group Challenge hosted by Kaggle

Otto Group Product Classification Challenge

About the competition

For the Otto Group Product Classification Challenge, hosted by Kaggle, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories.

Using the script

This repository contains a script for the benchmark submission of the competition. Use this script in the following way:

python benchmark.py <path-to-train> <path-to-test> <name-of-submission>

Each argument is optional as the script will guess the right names if you don't change them after downloading and put them in subfolder called data. It will then create a submission called my_submission.csv which should produce the benchmark posted on the leaderboard.

Requirements

To run the script, you will need to install the following packages:

• Numpy
• Pandas
• Scikit-learn

This script was tested using Python 2.7.9.

Getting started with nolearn/lasagne

We also provide a getting started notebook for those who want to try out neural nets:

Getting started with nolearn/lasagne

Questions

If you have a question regarding this script or the competition in general, head to the forum and post them.