Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The 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 Series December 2021 dataset is a multi-class modeling situation where we are trying 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: After a series of tuning trials, the best AutoKeras model processed the training dataset with an accuracy score of 72.54%. When we processed the test dataset with the final model, the model achieved an accuracy score of 70.71%.
CONCLUSION: In this iteration, the AutoKeras model did not appear to be a suitable algorithm for modeling this dataset without using additional trial iterations.
Dataset Used: Kaggle Tabular Playground Series December 2021 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.