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Hi Maveryx,
A solution to last week’s challenge can be found here.
Welcome to the third and final lap of our Weekly Challenge based on the Inspire 2024 Grand Prix! As we reach the climax of this thrilling journey, we are excited to present a challenge on predictive analysis. This topic, not often featured in Weekly Challenges, promises to push the boundaries of creativity. Get ready to dive deep and showcase your predictive analytics expertise!
Your task is to build a model to predict the top three podium finishers and compare the predicted versus the actual Silverstone podium finishers, and then identify the predicted racer who was not an actual podium finisher.
Use only driver race averages for full races, not qualifiers in Japan, Qatar, and Qatar Sprint to train the model. Use the model you built to score any drivers with full race data from Silverstone and determine the three most likely podium finishers. Then, identify any of those three drivers who did not actually make the podium at Silverstone.
Minimum lap counts per race to determine full race data:
- Japan: 53
- Qatar: 57
- Qatar Sprint: 19
- Silverstone: 52
The tasks to accomplish your objective include:
- Create a training dataset using the Race Data Japan_Qat_Quali_Sprint dataset for your model. Be sure to exclude qualifying races and drivers who did not complete a full race using the minimum lap counts above.
- Using this training dataset, build a Logistic Regression to estimate the likelihood a driver will finish in the top three on the podium.
- Use any Avg variables as possible predictors and nothing else.
- Use a Stepwise tool with the default configuration to determine the best predictor variables. The Stepwise tool will automatically determine the final model variables for the model.
- Score any Silverstone drivers with full race data using the Stepwise output and identify the top three most likely podium finishers.
- Compare the predicted podium finishers versus the actual Silverstone podium finishers. Find the predicted driver who did not finish on the actual podium.
Feel free to use the hints provided within the workflow.
Need a refresher? Review the following lessons in Academy to gear up.
Good luck!
If you skip PitStopAvg predictor variable, then predictions are 100% accurate. How do I know that....well that's what I missed initially and spent good 10 mins finding why prediction were 100% accurate :-).
My solution.
