Exercise - Predict the success of a rocket launch using machine learning

  • 3 minutes

Finally, it's time to test your model with data that was never found in your dataset.

On July 30, 2020, NASA launched the Perseverance rover to Mars from Cape Canaveral at 7:50 AM Eastern Time.

Gather the input data for the model:

  • Crewed or Uncrewed
  • High Temp
  • Low Temp
  • Ave Temp
  • Temp at Launch Time
  • Hist High Temp
  • Hist Low Temp
  • Hist Ave Temp
  • Precipitation at Launch Time
  • Hist Ave Precipitation
  • Wind Direction
  • Max Wind Speed
  • Visibility
  • Wind Speed at Launch Time
  • Hist Ave Max Wind Speed
  • Hist Ave Visibility
  • Condition

You can find this information on most weather sites. Remember the data should be all numerical.

The following example uses hypothetical data:

# ['Crewed or Uncrewed', 'High Temp', 'Low Temp', 'Ave Temp',
#        'Temp at Launch Time', 'Hist High Temp', 'Hist Low Temp',
#        'Hist Ave Temp', 'Precipitation at Launch Time',
#        'Hist Ave Precipitation', 'Wind Direction', 'Max Wind Speed',
#        'Visibility', 'Wind Speed at Launch Time', 'Hist Ave Max Wind Speed',
#        'Hist Ave Visibility', 'Condition']

data_input = [ 1.  , 75.  , 68.  , 71.  ,  0.  , 75.  , 55.  , 65.  ,  0.  , 0.08,  0.  , 16.  , 15.  ,  0.  ,  0. ]

tree_model.predict([data_input])

Continue to improve

As you continue to improve your model in ways described throughout this learning path, keep an eye out on other NASA rocket launches. See if your model can accurately predict the outcomes.

You can also use weather predictions combined with your machine learning model to see if you can predict if there's a delay even before the launches happen!