Pinned Repositories
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
cross_browser
cross_browser_fingerprinting
Mining-the-Social-Web-2nd-Edition
The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)
MLOpsPython
py-bing-search
Python Bing Search API
pyds
A Python library for performing calculations in the Dempster-Shafer theory of evidence.
spaCy
💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
UnityGameGGJ
Wiley-Downloader
Download any book from Wiley
py-bing-search
Python Bing Search API
ashaherb's Repositories
ashaherb/Wiley-Downloader
Download any book from Wiley
ashaherb/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
ashaherb/cross_browser
cross_browser_fingerprinting
ashaherb/Mining-the-Social-Web-2nd-Edition
The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)
ashaherb/MLOpsPython
ashaherb/py-bing-search
Python Bing Search API
ashaherb/pyds
A Python library for performing calculations in the Dempster-Shafer theory of evidence.
ashaherb/spaCy
💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython
ashaherb/UnityGameGGJ