cnocr

A python package for Chinese OCR with the available pre-trained model. So it can be used directly after installed.

competition_cail

2018**‘法研杯’法律智能挑战赛（CAIL2018）个人作品

complexeventextraction

A concept and obvious expression pattern collection of Chinese compound event extraction which then be evolved into ComplexEventGraph，本项目提出了中文复合事件的概念与显式模式，包括条件事件、因果事件、顺承事件、反转事件等事件抽取，并形成事理图谱。

cond_rnn

Conditional RNNs made easy with Tensorflow.

constellation

20190920

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

convert-ais-geopositions-to-cfd-cartesian

Convert geopositions of AIS ship tracks to cartesian coordinates to make non-stationary CFD simulations possible, in which multiple ships move within the computational domain. Requires remeshing technology on CFD-side.

cookbook

The Data Engineering Cookbook

cookbook-2nd-code

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

costsensitive

(Python, R) Cost-sensitive multiclass classification (Weighted-All-Pairs, Filter-Tree & others)

crawlab

Celery-based web crawler admin platform for managing distributed web spiders regardless of languages and frameworks.

credit-risk-eda-and-modeling.

Context The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below. Content It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. Several columns are simply ignored, because in my opinion either they are not important or their descriptions are obscure. The selected attributes are: Age (numeric) Sex (text: male, female) Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled) Housing (text: own, rent, or free) Saving accounts (text - little, moderate, quite rich, rich) Checking account (numeric, in DM - Deutsch Mark) Credit amount (numeric, in DM) Duration (numeric, in month) Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others) Acknowledgements Source: UCI

creditriskanalyzer

To build a custom Credit Risk Analyzer, using a decision tree model, which will predict the suspected credit card defaulters among the new applicants.

csmlc

Cost-Sensitive Multi-Label Classification

cubemapslam

A Piecewise-Pinhole Monocular Fisheye SLAM System

customer-analytics

Customer Analytics & Financial Valuation for Zakka Canada. This is the associated iPython Notebook code.

customer-churn-analysis

customer-returns-analysis-customer-fraud-detection-

customer-segmentation

Learn real-world techniques on customer segmentation and behavioral analytics, using a real dataset containing customer transactions from an online retailer.

customer360

Customer 360 analytics powered by MapR

customer_growth

A set of tools and techniques used to measure and optimize ecommerce businesses

customercentricretail

The system is being built with a view to promote and strengthen the relation between the customer and retailers. There are different models being added to the sytem in order to improve and strengthen it's claim.

customerchurnwebapp

Created for toolchain: https://console.ng.bluemix.net/devops/toolchains/892328e7-a6ab-484c-9e30-3e2163dd9a51?env_id=ibm%3Ayp%3Aus-south

customertemporalregularities

cv

My CV built using RMarkdown and the pagedown package.

d3-sankey

Visualize flow between nodes in a directed acyclic network.

dash-bio

2

dash-oil-and-gas-demo

20190920

dash-salesforce-crm

2019

data-science--cheat-sheet

Cheat Sheets