Displaying 1 to 20 from 20 results

open-solution-home-credit - Open solution to the Home Credit Default Risk challenge :house_with_garden:

  •    Python

This is an open solution to the Home Credit Default Risk challenge 🏡. 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 🐍.




painters - :art: Winning solution for the Painter by Numbers competition on Kaggle

  •    Python

This repository contains a 1st place solution for the Painter by Numbers competition on Kaggle. Below is a brief description of the dataset and approaches I've used to build and validate a predictive model. The challenge of the competition was to examine pairs of paintings and determine whether they were painted by the same artist. The training set consists of artwork images and their corresponding class labels (painters). Examples in the test set were split into 13 groups and all possible pairs within each group needed to be examined for the submission. The evaluation metric for the leaderboard was AUC (area under the curve).

pytorch-speech-commands - Speech commands recognition with PyTorch

  •    Python

Convolutional neural networks for Google speech commands data set with PyTorch. We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.

kaggle-for-fun - All my submissions for Kaggle contests that I have been, and going to be participating

  •    Python

All my submissions for Kaggle contests that I have been, and going to be participating. I will probably have everything written in Python (utilizing scikit-learn or similar libraries), but occasionally I might also use R or Haskell if I can.

minimal-datascience - This repository contains all the code and dataset used in my blog series: Minimal Data Science

  •    Python

My goal for this minimal data science blog series is not only sharing, tutorializing, but also, making personal notes while learning and working as a Data Scientist. I’m looking forward to receiving any feedback from you. Chapter-1: Classify StarCraft 2 players with Python Pandas and Scikit-learn.


kaggle-malware-classification - Kaggle "Microsoft Malware Classification Challenge"

  •    Python

Kaggle "Microsoft Malware Classification Challenge". 6th place solution

kaggle-coupon-purchase-prediction - Code for RECRUIT Challenge. 5th place.

  •    Python

Code for Coupon Purchase Prediction (RECRUIT Challenge). Note: This code is able to achieve a 5th place score (Private LB: 0.008776). But this is not a full version of my submitted solution (Private LB: 0.008905). My submitted solution is average of this solution and another XGBoost solution. This repositoy provides a simple version of 5th place solution.

Kaggle-MNIST - Simple ConvNet to classify digits from the famous MNIST dataset

  •    Python

Simple ConvNet to classify digits from the famous MNIST dataset. This program gets 98.63% on Kaggle's test set. In order to run this program, you need to have Theano, Keras, and Numpy installed as well as the train and test datasets (from Kaggle) in the same folder as the python file.

kaggle-carvana - Solution for the Carvana Image Masking Challenge on Kaggle

  •    Python

The solution for the Carvana Image Masking Challenge on Kaggle. It uses a custom version of RefineNet with Squeeze-and-Excitation modules implemented in PyTorch. It was a part of the final ensemble that was ranked 23 out of 735 teams (top 4%). The goal of the Carvana Image Masking Challenge was to develop an algorithm that removes a background from a wide variety of car photos. Here you can see predictions from a trained neural network for 16 images of a single car.

open-solution-cdiscount-starter - Open solution to the Cdiscount’s Image Classification Challenge

  •    Python

This is ready to use, end-to-end sample solution for the currently running Kaggle Cdiscount challenge. It involves data loading and augmentation, model training (many different architectures), ensembling and submit generator.

open-solution-data-science-bowl-2018 - Open solution to the Data Science Bowl 2018

  •    Python

This is an open solution to the Data Science Bowl 2018 based on the topcoders winning solution from ods.ai. 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 😉.

open-solution-ship-detection - Open solution to the Airbus Ship Detection Challenge

  •    Python

This is an open solution to the Airbus Ship Detection Challenge. 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 🐍.

open-solution-toxic-comments - Open solution to the Toxic Comment Classification Challenge

  •    Python

Here, at Neptune we enjoy participating in the Kaggle competitions. Toxic Comment Classification Challenge is especially interesting because it touches important issue of online harassment. You need to be registered to neptune.ml to be able to use our predictions for your ensemble models.

home-credit-default-risk - Default risk prediction for Home Credit competition - Fast, scalable and maintainable SQL-based feature engineering pipeline

  •    Python

This is code I built for the Home Credit default risk competition on Kaggle. This should be seen more as an ML engineering achievement than a data science top of the line prediction model. First of all, due to time constraints this is not a top scorer. First rank was 0.80570 AUC (499 submissions), this is 0.78212 AUC (12 submissions).