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Example 1 Data

TensorFlow Data Collection

The data used in Example 1, is a list of car objects like this:

  "Name": "chevrolet chevelle malibu",
  "Miles_per_Gallon": 18,
  "Cylinders": 8,
  "Displacement": 307,
  "Horsepower": 130,
  "Weight_in_lbs": 3504,
  "Acceleration": 12,
  "Year": "1970-01-01",
  "Origin": "USA"
  "Name": "buick skylark 320",
  "Miles_per_Gallon": 15,
  "Cylinders": 8,
  "Displacement": 350,
  "Horsepower": 165,
  "Weight_in_lbs": 3693,
  "Acceleration": 11.5,
  "Year": "1970-01-01",
  "Origin": "USA"

The dataset is a JSON file stored at:


Cleaning 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);

Try it Yourself »

Plotting the Data

Here is some code you can use to plot the data:

function tfPlot(values, surface) {
    {values:values, series:['Original','Predicted']},
    {xLabel:'Horsepower', yLabel:'MPG'});

Try it Yourself »