Regression Model for Kaggle Tabular Playground Series 2021 Feb Using Python and Scikit-learn

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

SUMMARY: This 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 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: 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 average performance of the machine learning algorithms achieved an RMSE benchmark of 0.8790 using the training dataset. We selected Random Forest and Gradient Boosting to perform the tuning exercises. After a series of tuning trials, the refined Gradient Boosting model processed the training dataset with a final RMSE score of 0.8447. When we processed Kaggle’s test dataset with the final model, the model achieved an RMSE score of 0.8455.

CONCLUSION: In this iteration, the Gradient Boosting model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set

Dataset ML Model: Regression with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.