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 on the original Forest Cover Type Prediction competition.
ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 0.9599 on the validation dataset. The final model processed the validation dataset with a final accuracy score of 0.9627. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 0.9535.
CONCLUSION: In this iteration, the TensorFlow Decision Forests 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.