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german_credit_1

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

german_credit_risk_2

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

german_risk

The analysis explored the relationship between the credit rating and the characteristics of the German borrower. Analyzing the report, bankers may figure out what kind of people may not repay the loan to them. Thus, lenders may consider lending to a borrower based on his/her behaviors. The analysis used a dataset from Kaggle's open-data platform. The data is originally from the UCI machine learning lab, and the dataset is real and valid. The dataset is about German credit risk, and risk prediction is one of the classic application scenarios in the data mining field. From a data cleaning perspective, the UCI ML’s researchers have preprocessed the dataset that reduced our works of data clean progress. The dataset recorded 1000 borrowers' 11 features which represent borrowers' age, sex, job type, housing stats, Saving accounts' stats, checking account's stats, loan's duration, loan's purpose, credit amount, and the risk level.

getting-and-cleaning-data-uci-har-dataset

Getting-And-Cleaning-Data-Johns-Hopkins-Bloomberg-School-of-Public-Health-Coursera / UCI HAR Dataset

gfer

:exclamation: This is a read-only mirror of the CRAN R package repository. gfer — Green Finance and Environmental Risk. Homepage: https://yuanchao-xu.github.io/gfer/ Report bugs for this package: https://github.com/Yuanchao-Xu/gfer/issues

glmmtmb

:exclamation: This is a read-only mirror of the CRAN R package repository. glmmTMB — Generalized Linear Mixed Models using Template Model Builder. Homepage: https://github.com/glmmTMB Report bugs for this package: https://github.com/glmmTMB/glmmTMB/issues

globalcoffeetrade

Shiny App: Global Coffee Trade

globalterrorismanalysis

Using stepwise regression, ML, and plots, determine what constitutes an effective terrorist attack, and what conditions would attract an attack

golem

A Framework for Building Robust Shiny Apps

google-data-analytics-certificate-case-study

This report is a comprehensive documentation of my work on the case study, which is the culmination of the Google Data Analytics certification program on Coursera.

google-trends-stock-data

Correlation between google search trends and stock trading volume

googlesheets-python_1

Executes a query on MySQL database, get the data, creates a tab in a Google Sheet and dumps the data there

googletensorflowexample

Using linear regression to predict median housing prices. Credit goes to google

gorcure

:exclamation: This is a read-only mirror of the CRAN R package repository. GORCure — Fit Generalized Odds Rate Mixture Cure Model with Interval Censored Data

government-expenditure-project

The government's role in economic growth has been an issue since way back with the perception that, for sustainable development and efficient output, the government's role in economic policies should be reduced. Given this fiscal scenario, there is a need to identify the relationship between GDP, Government Expenditure on the two sectors, and how they relate to growth of GDP in Kenya's economy.

grade-manager

use pyautogui & xlwings to help my coworkers enter data from an excel spreadsheet into a odd custom spreadsheet app that doesn't allow multi-cell paste

gradient_boosting_telecom_churn_prediction

Customer churn, also known as customer attrition, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay-TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business me

gradschoolreport

Graduate School Reports using Shiny and R markdown

graduation-work

Development of a statistical model for predicting price volatility of financial assets for estimation market risks

grain-price-dashboard

Dashboard having forecasted price and of different markets according to variety & market.

gramener-case-study---eda---iiitb

Problem Statement Introduction Solving this assignment will give you an idea about how real business problems are solved using EDA. In this case study, apart from applying the techniques you have learnt in EDA, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers. Business Understanding 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.

gramener_case_study

The consumer finance company specializes in different types of loans to the urban customer. The major problem they face is when the customer fails to replay the loan . 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 Business Objective : • The aim of this analysis is to try to find the customers who are likely to pay the loan and who are likely to default. • We aim to find the parameters that significantly influences a customer’s probability of being a defaulter thereby taking necessary actions.

graphical-lasso-to-identify-trading-pairs-in-international-stock-etfs

Graphical Lasso to Identify Trading Pairs in International Stock ETFs

greekstocks_portfolio_with_streamlit

greenpoint-frtb

group_fixed-income

growth_vs_debtratio

Growth of GDP vs Debt to GDP ratio with financial stress indicators.

gs-quantify-bond-prediction

Solution of the given task of predicting the buying and selling volume of the corporate bonds.

gse-520

Problem sets from GSE 520. Shows my knowledge of the R markdown language, coding in R, and my relative knowledge of econometrics, specifically with OLS

gst-graph-cvn201911

R project to create growing season temperature graph used in 2019 November issue of the Climate Viticulture Newsletter