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 June 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 average performance of the three decision forests algorithms achieved a logarithmic loss benchmark of 2.2783 using the validation dataset. After a series of tuning trials, the refined Gradient Boosted Trees model processed the training dataset with a final logarithmic loss score of 1.8932. When we processed Kaggle’s test dataset with the final model, the model achieved a logarithmic loss score of 2.8624.

CONCLUSION: In this iteration, the Decision Forests model did not appear to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground 2021 June Data Set

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

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-jun-2021/

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-jun-2021/leaderboard

The HTML formatted report can be found here on GitHub.