In the heat of a Formula E race, teams need fast access to insights that can help drivers make split-second decisions and cross the finish line first. Can your data-science skills help Envision Racing, one of the founding teams in the championship, take home even more trophies?

To do so, you will have to build a machine learning model that predicts the Envision Racing drivers’ lap times for the all-important qualifying sessions that determine what position they start the race in. Winning races involves a combination of both a driver’s skills and data analytics. To help the team you’ll need to consider several factors that affect performance during a session, including weather, track conditions, and a driver’s familiarity with the track.

Genpact, a leading professional services firm that focuses on digital transformation, is collaborating with Envision Racing, a Formula E racing team and digital hackathon platform MachineHack, a brainchild of Analytics India Magazine, is launching ‘Dare in Reality’.’ This two-week hackathon allows data science professionals, machine learning engineers, artificial intelligence practitioners, and other tech enthusiasts to showcase their skills, impress the judges, and stand a chance to win exciting cash prizes.

Genpact (NYSE: G) is a global professional services firm that makes business transformation real, driving digital-led innovation and digitally enabled intelligent operations for our clients.

Feature Description Provided
NUMBER Number in sequence
DRIVER_NUMBER Driver number
LAP_NUMBER Lap number
LAP_TIME Lap time in seconds
LAP_IMPROVEMENT Number of Lap Improvement
S1 Sector 1 in [min sec.microseconds]
S1_IMPROVEMENT Improvement in sector 1
S2 Sector 2 in [min sec.microseconds]
S2_IMPROVEMENT Improvement in sector 2
S3 Sector 3 in [min sec.microseconds]
S3_IMPROVEMENT Improvement in sector 3
KPH Speed in kilometer/hour
ELAPSED Time elapsed in [min sec.microseconds]
HOUR In [min sec.microseconds]
S1_LARGE In [min sec.microseconds]
S2_LARGE In [min sec.microseconds]
S3_LARGE In [min sec.microseconds]
DRIVER_NAME Name of the driver
PIT_TIME Time taken to car stops in the pits for fuel and other consumables to be renewed or replenished
GROUP Group of driver
TEAM Team name
POWER Brake Horsepower(bhp)
LOCATION Location of the event
EVENT Free practice or qualifying

The submission will be evaluated using the RMSLE metric.\ One can use numpy.sqrt(mean_squared_log_error(actual, predicted)) to calculate the same

Importing packages and data, basic cleaning & overview of data

Importing packages

Downloading ML Helper :

Importing packages :

Importing data

Files under Data folder :

Importing each file :

Spending that 70% of time cleaning this data :

High level overview of data

Train data : At glance

Test data : At glance

Train weather data : At glance