For the Indicators API, XML and JSON representations are available for the Projects API, Atom representation is also available for the World Bank Finances API, XML, JSON and RDF representations are available. All three APIs implementRESTful interfaces to allow users to perform queries of available data using selection parameters.
#Data incubator challenge series#
The World Bank currently has three different APIs to provide access to different datasets: one for Indicators (or time series data), one for Projects (or data on the World Bank’s operations), and one for the World Bank financial data (World Bank Finances API).
#Data incubator challenge download#
This is the link I used to download the data set that I used to do my data analysis for the challenge question:Īnd this is the link to the API data that I would like to make use of for the project: This page was generated by GitHub Pages using the Cayman theme by Jason Long.Data-Incubator-Challenge Data incubator challenge questions 3: Links to public description of data source WORLD BANK More relevant datasets are also worth digging into.ĭata Incubator Challenge is maintained by ziyan-f.
Since Component names are shared by Manufacturers, the plot looks messy when showing all Manufacturers' data altogether. Once chosen, the plot shows data for only one Manufacturer in a descending order. The interactive feature of Plotly allows the viewer to select interested Manufacturer by clicking the corresponding legend. This plot shows the number of defected Components for top 10 Manufacturers. Number of Defected Components by Manufacturer (Click the graph for interactive view) sedan, SUV, etc, to filter out the confusion. In future steps, the Models should be categorized into broader classes, e.g. Since some of the Model names are shared by multiple Makes, the plot looks messy when showing all Makes' data altogether. Once chosen, the plot shows data for only one Make in a descending order. The interactive feature of Plotly allows the viewer to select interested Make by clicking the corresponding legend. This plot shows the number of defected Models for top 10 Makes. Number of Defected Models by Make (Click the graph for interactive view) These plots were generated with R Package "plotly". Preliminary AnalysisĢ plots were made to get a first idea on the data. With this knowledge on historical recall records, the project will be finalized as an interactive application to provide recommendations to vehicle buyers regarding to safety considerations. With these data, the goal of this project is to study the recall events by each variable to see which one has greater effect on ending up a defect investigation recall. The dataset contains variables like Vehicle Make, Model, Model Year, Defect Component, Manufacturer, Case Open Date and Close Date.
The Office Defect Inspection(ODI) of the Department of Transportation (DOT) has a dataset of vehicle defect investigation recall records since 1972.
This repo is created for the Data Incubator Challenge problems Introduction Data Incubator Challenge by ziyan-f Data Incubator Challenge This repo is created for the Data Incubator Challenge problems View on GitHub Download.
0 kommentar(er)
