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 machine learning algorithms achieved a logarithmic loss benchmark of 5.6058 using the training dataset. We selected Logistic Regression and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined Random Forest model processed the training dataset with a final logarithmic loss score of 1.7700. When we processed Kaggle’s test dataset with the final model, the model achieved a logarithmic loss score of 1.7682.
CONCLUSION: In this iteration, the Random Forest model appeared 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.