An curious maker & coder from Malaysia, doing fun projects related to Arduino RPi, IoT, PCB, LED, linux, javascript (p5, phaser, Vue), dataviz & art installation
md `# Auto Loan Plot Analysis`
myLoan = FileAttachment("autoloan.csv").csv({typed: true})
myLoan2 = myLoan.map( d => (
{
Vehicle_Make: d.Vehicle_Make,
Vehicle_Model: d.Vehicle_Model,
Loan_Amount: d.Loan_Amount,
Branch_code: d.Branch_code,
Annual_Income: d.Annual_Income,
Interest_Rate: d.Interest_Rate*100,
Income_Level: parseIncome( d.Annual_Income ),
}
)
)
.sort((a, b) => b.Loan_Amount - a.Loan_Amount)
md` ## Added Income_Level as a value`
md` ### Created a function to convert string to value`
parseIncome = function income(d) {
let n
switch ( d[0] ) {
case '2': n = 12000; break;
case '3': n = 18000; break;
case '4': n = 24000; break;
case '5': n = 48000; break;
case '6': n = 72000; break;
case '7': n = 96000; break;
case '8': n = 120000; break;
case '9': n = 150000; break;
defaut: n = 0;
}
return n
}
incomeRange = [ 12000,18000, 24000, 48000, 72000, 96000, 120000, 150000 ]
Inputs.table(myLoan2)
md`### Get all car brands`
carBrandList = _.uniqBy(myLoan, d => d.Vehicle_Make)
.map(d => d.Vehicle_Make)
.sort()
md`## Use pulldown menu to select car brand to filter`
viewof carBrand = Inputs.select([null].concat(carBrandList), {label: "Make"})
md`## Drag slider for number of results to return`
viewof results = Inputs.range([50, 500], {value: 50, step: 10, label: "Results"})
carBrandLoan = myLoan2.map( d => (
{
Vehicle_Model: d.Vehicle_Model,
Vehicle_Make: d.Vehicle_Make,
Loan_Amount: d.Loan_Amount,
Branch_code: d.Branch_code,
Annual_Income: d.Annual_Income,
Income_Level: parseIncome( d.Annual_Income ),
}
)
)
.filter(d => d.Vehicle_Make == carBrand )
.sort((a, b) => b.Loan_Amount - a.Loan_Amount)
.splice(0,results)
Plot.plot({
width:1200,
height: 600,
grid: true,
inset: 10,
marginLeft: 150,
x: {
label: "Amount (000)",
transform: f => f / 1000,
padding: 0.8
},
y: {
label: "Model",
padding: 2,
},
marks: [
//Plot.ruleY([0]),
//Plot.dot(carBrandLoan, Plot.binX( {color: "count"}, {y: "Vehicle_Model", x: "Loan_Amount", fill:"Branch_code"})),
Plot.dot(carBrandLoan, Plot.binX( {fill: "count"}, {y: "Vehicle_Model", x: "Loan_Amount", fill:"Branch_code"})),
]
})
Inputs.table(carBrandLoan)
md` ## All Brands vs Loan Amount`
Plot.dot(myLoan2, {y: "Vehicle_Make", x: "Loan_Amount", stroke:"Loan_Amount"}).plot({
width:1200,
height: 600,
grid: true,
inset: 10,
marginLeft: 120,
y: {
label: "Make",
padding: 0.1
},
x: {
label: "Amount (000)",
padding: 0.1,
transform: f => f / 1000,
},
})
Plot.plot({
width:1200,
height: 800,
grid: true,
inset: 10,
marginLeft: 150,
x: {
label: "Amount (000)",
transform: f => f / 1000,
padding: 0.8
},
y: {
label: "Make",
padding: 0.2
},
marks: [
//Plot.ruleY([0]),
Plot.barX(myLoan2, {y: "Vehicle_Make", x: "Loan_Amount", stroke:"Loan_Amount"}),
]
})
md `### Income level vs Vehicle Make`
Plot.plot({
width:1200,
height: 800,
grid: true,
inset: 10,
marginLeft: 150,
x: {
label: "Amount (000)",
padding: 0.8
},
y: {
label: "Make",
padding: 0.2
},
marks: [
//Plot.ruleY([0]),
Plot.dot(myLoan2, {y: "Vehicle_Make", x: "Income_Level", stroke:"Income_Level"}),
]
})
md`### Corelation between loan amount and branch`
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.