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 November 2021 dataset is a binary classification situation where we are trying to predict one of the 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 data is synthetically generated by a GAN trained on a real-world dataset used to identify spam emails via various extracted features from the email. Although the features are anonymized, they have properties relating to real-world features.
ANALYSIS: After a series of tuning trials, the best AutoKeras model processed the training dataset with a ROC/AUC score of 0.7492. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.7484.
CONCLUSION: In this iteration, the AutoKeras model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground 2021 November Data Set
Dataset ML Model: Binary classification with numerical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-nov-2021
One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-nov-2021/leaderboard
The HTML formatted report can be found here on GitHub.