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example-app-pdf-report

🎓 Diploma PDF Generator

example-hftish

Example Order Book Imbalance Algorithm

example-rmd-templates

📄 A selection of minimal examples used to highlight R Markdown templates, as referred to in the "R Markdown Definitive Guide"

example.eikonapi.python.ratingtransitionmatrix

Created by Platform Services GitHub tool on Wed Apr 12 2017

example.refinitivapis.r.jupyter

example_rmarkdown

Example Rmarkdown report

examples

Example files illustrating Python and Excel

excel-addin-generator

Tools to create an Excel Addin from VBA code

excel-dependents

List external references for Excel files (using xlwings)

excel-pyxero

Accessing Xero through Excel using xlwings and pyxero

excel_process_validator

Automation for checking measurement calibration calculations in Excel spreadsheets

excel_to_neo4j

Import Excel Spreadsheets into Neo4j, using Python and some choice libraries.

excel_vba_application

excel_xlwings

excel_xlwings-1

excelpsychrofunctions

Psychrometric Functions implemented in Python for integration with Excel via xlwings addon

excelreports

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.

exceltypes

win32com extension for Excel types

expected-credit-loss

Lifetime Expected Credit Loss calculation under IFRS 9

explaining-bonds-risk-premia-in-turkey

Aim of this study is try to explain bonds risk premium in Turkey

exploratory-data-analysis

EDA of datasets are done in PowerBi, Python and Tableau

exploratory-data-analysis-netflix_rmarkdown

exploratory-data-analysis-on-loan-investment

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)

exploratorydataanalysis

The project is in a RMD format with complete explanation of all EDA performed over Bank Customer Credit default dataset.

exploring-machine-learning

A collection of R Markdown files that explore machine learning in data science with R.

extract_bills_experian

extract_excel

for practicing xlwings

ezibpy

ezIBpy, a Pythonic Client for Interactive Brokers API

factor-modelling

factorcopula

R package for dependence modelling with factor copulas