Multi-Class Model for Kaggle Tabular Playground Series 2021 May Using Python and XGBoost

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 May 2021 dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

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 dataset used for this competition is synthetic but based on a real dataset and generated using a CTGAN. The original dataset deals with predicting the category on an eCommerce product given various attributes about the listing. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The performance of the preliminary XGBoost model achieved a logarithmic loss benchmark of 1.0995. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final logarithmic loss score of 1.0936. When we applied the last model to Kaggle’s test dataset, the model achieved a logarithmic loss of 1.0933.

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

Dataset Used: Kaggle Tabular Playground 2021 May Data Set

Dataset ML Model: Multi-Class classification with numerical and categorical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.