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🎓 Diploma PDF Generator
Example Order Book Imbalance Algorithm
📄 A selection of minimal examples used to highlight R Markdown templates, as referred to in the "R Markdown Definitive Guide"
Created by Platform Services GitHub tool on Wed Apr 12 2017
Example Rmarkdown report
Example files illustrating Python and Excel
Tools to create an Excel Addin from VBA code
List external references for Excel files (using xlwings)
Accessing Xero through Excel using xlwings and pyxero
Automation for checking measurement calibration calculations in Excel spreadsheets
Import Excel Spreadsheets into Neo4j, using Python and some choice libraries.
Psychrometric Functions implemented in Python for integration with Excel via xlwings addon
A small application that takes data from excel spreadsheets and puts it into a new report, formats it, then emails to customers, management, and sales associates.
win32com extension for Excel types
Lifetime Expected Credit Loss calculation under IFRS 9
Aim of this study is try to explain bonds risk premium in Turkey
EDA of datasets are done in PowerBi, Python and Tableau
You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision: If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company The data given below contains the information about past loan applicants and whether they ‘defaulted’ or not. The aim is to identify patterns which indicate if a person is likely to default, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. In this case study, you will use EDA to understand how consumer attributes and loan attributes influence the tendency of default. Figure 1. Loan Data Set When a person applies for a loan, there are two types of decisions that could be taken by the company: Loan accepted: If the company approves the loan, there are 3 possible scenarios described below: Fully paid: Applicant has fully paid the loan (the principal and the interest rate) Current: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'. Charged-off: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan Loan rejected: The company had rejected the loan (because the candidate does not meet their requirements etc.). Since the loan was rejected, there is no transactional history of those applicants with the company and so this data is not available with the company (and thus in this dataset)
The project is in a RMD format with complete explanation of all EDA performed over Bank Customer Credit default dataset.
A collection of R Markdown files that explore machine learning in data science with R.
for practicing xlwings
ezIBpy, a Pythonic Client for Interactive Brokers API
R package for dependence modelling with factor copulas
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.