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Predict the project state (success, failure, in progress, etc.) for Kickstarter projects using key project goal indicators and information (goal amount, updates, backers, etc.)
Information taken from Dan Misener's KickbackMachine. Raw data available here
Model that will predict the quality of risk of a loan application. This dataset was initially created by dr. Hans Hofmann at the Institut fur Statistik und Okonometrie Universitat Hamburg.
Currency rate USD/GBP prediction based on other world currency rates USD/XXX
- USD/EUR rate 1 USA Dollar in Euros from Europe
USD/JPY rate 1 USA Dollar in Yens from Japan - USD/CZK rate 1 USA Dollar in Korunas from Czech Republic
USD/GBP rate 1 USA Dollar in Pound Sterlings from Great Britain - USD/AUD rate 1 USA Dollar in Dollars from Australia
USD/BRL rate 1 USA Dollar in Reals from Brazil - USD/CNY rate 1 USA Dollar in Yuan Renminbis from China
USD/ZAR rate 1 USA Dollar in Rands from South Africa - USD/CAD rate 1 USA Dollar in Dollars from Canada
USD/MXN rate 1 USA Dollar in New Pesos from Mexico - USD/ARS rate 1 USA Dollar in Pesos from Argentina
USD/CHF rate 1 USA Dollar in Francs from Switzerland - USD/INR rate 1 USA Dollar in Rupees from India
USD/VND rate 1 USA Dollar in New Dngs from Vietnam - USD/ZMW rate 1 USA Dollar in Kwachas from Zambia
USD/IDR rate 1 USA Dollar in Rupiahs from Indonesia - USD/IQD rate 1 USA Dollar in Dinars from Iraq
USD/IRR rate 1 USA Dollar in Rials from Iran
Quandl dataset about currency rates
Data on loan delinquency for loans given by LendingClub.com based on about 50000 loans. Data is available at [http://lendingclub.com/info]
Dataset aimed to improve in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal is to build model that borrowers can use to help make the best financial decisions.
• 150,000 borrowers
ID: ID of borrower Default: Person experienced 90 days past due delinquency or worse (Type: Y/N).
Balance on credit cards: Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits (Type: percentage)
Age: Age of borrower in years (Type: integer) Number of times due in 30-59 Days. Number of times borrower has been 30-59 days past due but no worse in the last 2 years. (Type: integer).
DebtRatio: Monthly debt payments, alimony, living costs divided by monthly gross income (Type: integer)
MonthlyIncome: Monthly income (Type: real)
Number of loans: Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards) (Type: integer) NumberOfTimes90DaysLate: Number of times borrower has been 90 days or more past due. (Type: integer)
NumberRealEstateLoansOrLines: Number of mortgage and real estate loans including home equity lines of credit (Type: integer)
NumberOfTime60-89DaysPastDueNotWorse: Number of times borrower has been 60-89 days past due but no worse in the last 2 years. (Type: integer)
NumberOfDependents: Number of dependents in family excluding themselves (spouse, children etc.). (Type: integer)
Source: Give Me Some Credit at Kaggle
Predicting $ per square foot for rentals in the SF Bay Area.
Sample data courtesy of RentMetrics; dataset available for download here.
Trained with Listings data from the last 1yr, updated on 2013/02/05, this model attempts to predict the "BorrowerRate".
Prosper, a peer-to-peer lending marketplace, makes their loan data available to the public.
One of the tables in this data is the Listing data, which contains details about every loan request submitted to the Prosper marketplace.
This model was created by:
- Starting with the "Prosper Listings (1yr 20130205)" dataset.
Setting the "BorrowerRate" as the objective and deselecting fields (BidCount,Status).
Submitting the model request to BigML.
For a description of the fields in the model, see Prosper Data Export Definition
The goal is to predict if the client will subscribe a term deposit using the data of direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.