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 December 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 dataset is used for this competition is synthetic, but based on a real dataset and generated using a CTGAN. This dataset is based off of the original Forest Cover Type Prediction competition.
ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 0.9590. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 0.9613. When we processed the test dataset with the final model, the model achieved an accuracy score of 0.9546.
CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground 2021 December Data Set
Dataset ML Model: Multi-Class classification with numerical and categorical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-dec-2021
One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-dec-2021/leaderboard
The HTML formatted report can be found here on GitHub.