Example 2 Data
Example 2 uses the same source code as Example 1.
But, because another dataset is used, the code must collect other data.
Data Collection
The data used in Example 2, is a list of house objects:
{
"Avg. Area Income": 79545.45857,
"Avg. Area House Age": 5.682861322,
"Avg. AreaNumberofRooms": 7.009188143,
"Avg. Area Number of Bedrooms": 4.09,
"Area Population": 23086.8005,
"Price": 1059033.558,
},
{
"Avg. Area Income": 79248.64245,
"Avg. Area House Age": 6.002899808,
"Avg. AreaNumberofRooms": 6.730821019,
"Avg. Area Number of Bedrooms": 3.09,
"Area Population": 40173.07217,
"Price": 1505890.915,
},
The dataset is a JSON file stored at:
https://github.com/meetnandu05/ml1/blob/master/house.jsonCleaning Data
When preparing for machine learning, it is always important to:
- Remove the data you don't need
- Clean the data from errors
Remove Data
A smart way to remove unnecessary data, it to extract only the data you need.
This can be done by iterating (looping over) your data with a map function.
The function below takes an object and returns only x and y from the object's Horsepower and Miles_per_Gallon properties:
function extractData(obj) {
return {x:obj.Horsepower, y:obj.Miles_per_Gallon};
}
Remove Errors
Most datasets contain some type of errors.
A smart way to remove errors is to use a filter function to filter out the errors.
The code below returns false if on of the properties (x or y) contains a null value:
function removeErrors(obj) {
return obj.x != null && obj.y != null;
}
Fetching Data
When you have your map and filter functions ready, you can write a function to fetch the data.
async function runTF() {
const jsonData = await fetch("cardata.json");
let values = await jsonData.json();
values = values.map(extractData).filter(removeErrors);
}
Plotting the Data
Here is some code you can use to plot the data:
function tfPlot(values, surface) {
tfvis.render.scatterplot(surface,
{values:values, series:['Original','Predicted']},
{xLabel:'Rooms', yLabel:'Price',});
}
