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 Apr 2021 dataset is a binary classification situation where we attempt 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 may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.
ANALYSIS: The performance of the cross-validated TensorFlow models achieved an average accuracy benchmark of 0.7702 after running for 45 trials. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.7865.
CONCLUSION: In this iteration, the AutoKeras-generated TensorFlow model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground Series 2021 Apr Data Set
Dataset ML Model: Regression with numerical and categorical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-apr-2021
One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-apr-2021/leaderboard
The HTML formatted report can be found here on GitHub.