FormulaE-EnvisionRacing

Problem Statement

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.

• Leaderboard topper - baseline-lights-out-and-away.ipynb

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Data Description

Weather dataset contains the following data from start time to end time of each session at 1 min interval

• Air Temp
• Humidity
• Pressure
• Track Temp
• Wind Speed
• Wind Direction
• Rain
• Location
• Event (Free Practice / Qualifying Session)

Train dataset contains Location 1-5 Free practice and Qualifying Events

• Driver detailes (Driver Number, Name, Team, Group)
• Sector times (S1,S2,S3 and S1_LARGE, S2_LARGE, S3_LARGE)
• Sector improvements
• Pit time
• Elapsed time**
• Power & Max Speed
• Event
• Location

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Approach

• Cleaned weather data as it contains corrupted data in track side variables
• Cleaned corrupted team name in train data
• Converted elapsed time into to minute index based on their sector times and merged it with the indexed weather data, we get weather available for each sector
• Pit time are overlaped with either S1 of next lap or S3 of previous lap, cleaned the data to get proper sector times for each lap
• Standardized the weather data as the each numeircal values are in different unit, replaced missing values with -999
• Train data is further split into 80/20 for cross validation

Evalualtion Metrics

Submissions are evalualted based on the RMLSE values

Models

Catboost was the base model used

Best model - baseline-lights-out-and-away.ipynb - 0.7232

Exprerimented Approaches

• Ran a H2O AutoML "GLM" model with just Sector and Pit times
• Used Bayesian Optimization on Catboost for hyperparameter tuning
• Considered the residual errors of the regression model and used it as a output variable for Catboost+Bayes
• Ran a AutoML H2O model

Future Enhancement (TO-DO)

• Run A Deeplearning RNN/LSTM model
• Consider the lag values of previous lap
• Create a timeline based visualization for each event and location to check each driver position