1. Objective:
  We want to predict the vehicle type given other onboard metrics.

2. License: Free to use, collected by Splunk.

3. Data Source:
  Splunk's annual Track Day!

4. Data Set Information:
  The dataset contains 50,000 instantaneous measurements collected via onboard instrumentation from several vehicles while being driven around a racetrack.

5. Field Meanings:
  A. batteryVoltage - battery voltage in volts (surprise!)
  B. engineCoolantTemperature - degrees celcius
  C. engineSpeed - rotations per minute
  D. lateralGForce - left/right
  E. longitudeGForce - front/back
  F. speed - miles per hour
  G. vehicleType - year, make, and model
  H. verticalGForce - up/down

6. Parameter Selection:
  A. Dashboard: Predict Categorical Fields
      Settings:
    1) Field to predict: vehicleType
    2) Fields to use for predicting: batteryVoltage, engineCoolantTemperature, engineSpeed, lateralGForce, longitudeGForce, speed, and verticalGForce

  B. Dashboard: Cluster Numeric Fields
        Settings:
    0) Search: | inputlookup track_day.csv
    1) Model name: track_day
    2) Fields to preprocess: batteryVoltage, engineCoolantTemperature, engineSpeed, lateralGForce, longitudeGForce, speed, verticalGForce
    3) Apply StandardScaler
    4) Apply PCA to reduce to 3 fields
    5) Algorithm: Birch
    6) Fields to use: PC_1, PC_2, PC_3
    7) K: 6