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jsonpickle

Python library for serializing any arbitrary object graph into JSON. It can take almost any Python object and turn the object into JSON. Additionally, it can reconstitute the object back into Python.

jspaint

🎨 Classic MS Paint, ＲＥＶＩＶＥＤ + ✨Extras

jupyter-scala

Lightweight Scala kernel for Jupyter / IPython 3

jupyter-themes

Custom Jupyter Notebook Themes

jupyter_contrib_nbextensions

A collection of various notebook extensions for Jupyter

jupyter_tensorboard

Start Tensorboard in Jupyter Notebook

jupyterlab

JupyterLab computational environment.

jupyterlab_xkcd

Random xkcd comic in a JupyterLab panel

justenoughscalaforspark

A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.

jvcr-3dlandmark

Code for "Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization"

k-poselets

k-poselets CVPR2014 code

kafka-python

Python client for Apache Kafka

kafka-streams-machine-learning-examples

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, DeepLearning4 and other technologies.

kafka-utils

kaggle

Contains all Kaggle meetup documents: tutorials, examples etc.

kaggle-1

Additional files for the Otto Group Challenge hosted by Kaggle

kaggle-2

Kaggle 项目实战（教程） = 文档 + 代码 + 视频

kaggle-2014-criteo

kaggle-airbnb-recruiting-new-user-bookings

2nd Place Solution in Kaggle Airbnb New User Bookings competition

kaggle-api

Official Kaggle API

kaggle-avazu

Code for the 3rd place finish for Avazu Click-Through Rate Prediction

kaggle-avazu-1

2nd place solution for Avazu click-through rate prediction competition

kaggle-avazu-clickthrough-rate-prediction

Python kernels for exploratory data analysis, feature engineering, modeling and evaluation, using two different approaches: gradient boosting machines with LightGBM, and logistic regression.

kaggle-competition-sberbank

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

kaggle-criteo

Kaggle Criteo https://www.kaggle.com/c/criteo-display-ad-challenge

kaggle-denoising-dirty-documents

6th place solution in "Denoising Dirty Documents"

kaggle-distracted-driver-detection

10th place solution in "State Farm Distracted Driver Detection"

kaggle-dsb2-keras

Keras tutorial for Kaggle 2nd Annual Data Science Bowl

kaggle-housing-prices

Predict sales prices and practice feature engineering, RFs, and gradient boosting

kaggle-music-recommendation-machine-learning

Chose Kaggle competition of Music Recommendation System which involves application of various machine learning methodologies. By using the dataset from KKBox which includes 10 million rows, we will be predicting the chances that a user will listen to the song again within a time window. It involves preprocessing of datasets along with the cross validation so as to get better results. The best classifier was LGBM giving an accuracy of 66.65%. The various classifiers used are: -ANN -Deep Learning -Gradient Boosting -Naive Bayes -Random Forest -Support Vector Machines -Extreme Gradient Boosting -Decision Trees -LBGM -Logistic Regression -Perceptron Languages used: Python(Jupyter Notebook) IDE- Anaconda Navigator The code file and the output screenshots are attached within every folder. The accuracy, precision, recall and ROC courve and area under ROC is used as an evaluation metrics.