Regression Model for Kaggle Tabular Playground Series 2021 February Using TensorFlow Decision Forests

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Tabular Playground Series Aug 2021 dataset is a regression situation where we are trying to predict the value of a continuous variable.

Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary Random Forest model achieved an RMSE benchmark of 0.8422 on the validation dataset. The final model processed the validation dataset with a final RMSE score of 0.8549. When we applied the finalized model to Kaggle’s test dataset, the model achieved an RMSE score of 0.8534.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series Feb 2021 Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.