2f:T477,

Data Maketh (Hu)man

Is our data really an accurate representation of ourselves? Enough to determine if we are trustworthy enough to own a credit card?

· Clustered based on the industry the individual works in.
· Sized based on the individual's amount of debt.
· Colored based on a combination of whether the individual had a prior default and if they were approved or not.

30:T405,{ for (let i = 0; i < data.length; i++) { if (data[i]["PriorDefault"] == "0") { data[i]["PriorDefault"] = "NoPriorDefault" } else { data[i]["PriorDefault"] = "YesPriorDefault" } if (data[i]["Approved"] == "0") { data[i]["Approved"] = "Rejected" } else { data[i]["Approved"] = "Approved" } if (data[i]["PriorDefault"] == "NoPriorDefault" && data[i]["Approved"] == "Approved") { data[i]["PriorDefault_Approved"] = "NoPriorDefault_Approved"; } else if (data[i]["PriorDefault"] == "NoPriorDefault" && data[i]["Approved"] == "Rejected") { data[i]["PriorDefault_Approved"] = "NoPriorDefault_Rejected"; } else if (data[i]["PriorDefault"] == "YesPriorDefault" && data[i]["Approved"] == "Approved") { data[i]["PriorDefault_Approved"] = "YesPriorDefault_Approved"; } else if (data[i]["PriorDefault"] == "YesPriorDefault" && data[i]["Approved"] == "Rejected") { data[i]["PriorDefault_Approved"] = "YesPriorDefault_Rejected"; } } }31:[{"id":"3f86597c798cc33ccda7f50d5fbb6b4339ad7fb16cfe4cfc0d5fe8eff47c7a948cfe63196c342ff4a2b7eb5f49bc0923d7773a2b85e1a92fe02bfcc1626405b1","href":"https://static.observableusercontent.com/files/3f86597c798cc33ccda7f50d5fbb6b4339ad7fb16cfe4cfc0d5fe8eff47c7a948cfe63196c342ff4a2b7eb5f49bc0923d7773a2b85e1a92fe02bfcc1626405b1","name":"credit_card_approvals_dataset.csv","type":"text/csv","lastModified":1683582062238.696,"size":45288}] 2d:{"title":"Final project (Static Visual, ver. 3) - Financial","cells":[{"id":0,"value":"# Final project (Static Visual, ver. 3) - Financial\n- Clustered based on the industry the individual works in.\n- Size based on the individual's amount of debt.\n- Colored based on a combination of whether the individual had a prior default and if they were approved or not.","pinned":false,"hidden":false,"mode":"md"},{"id":29,"value":"$2f","pinned":true,"hidden":false,"mode":"html"},{"id":22,"value":"data = FileAttachment(\"credit_card_approvals_dataset.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":380,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":116,"value":"fociIndustries = ({\n \"CommunicationServices\":{x: 100, y: 200},\"ConsumerDiscretionary\":{x: 300, y: 200},\"ConsumerStaples\":{x: 500, y: 200},\"Education\":{x: 700, y: 200},\n \"Energy\":{x: 900, y: 200},\"Financials\":{x: 100, y: 400},\"Healthcare\":{x: 300, y: 400},\"Industrials\":{x: 500, y: 400},\n \"InformationTechnology\":{x: 700, y: 400},\"Materials\":{x: 900, y: 400},\"Real Estate\":{x: 100, y: 600},\"Research\":{x: 300, y: 600},\"Transport\":{x: 500, y: 600},\"Utilities\":{x: 700, y: 600}\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":35,"value":"clElem = d3.select(svgContainer).select(\"#layer1\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":113,"value":"options = ({\n \"startFoci\": fociIndustries,\n \"fociField\": \"Industry\",\n \"classField\": \"PriorDefault_Approved\",\t\t// to set their class/color\n \"dotScaleFactor\": 5,\n \"strength\": -1,\n \"alphaTarget\": 0.09,\n \"collisionFactor\": 1.3\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"clusters = dvCluster(clElem,data,800,800,\"Debt\",options)","pinned":true,"hidden":false,"mode":"ojs"},{"id":375,"value":"labelLayer = clElem.append(\"g\").attr(\"id\",\"labels\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":400,"value":"dvClusterFociLabels(labelLayer,fociIndustries,{offsetY:115})","pinned":true,"hidden":false,"mode":"ojs"},{"id":82,"value":"-----","pinned":false,"hidden":false,"mode":"md"},{"id":267,"value":"import {dvCluster, dvClusterFociLabels} from \"@emfielduva/dvlib_layout\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}