Regression Model for Kaggle Tabular Playground Series 2021 Jan Using Python and AutoKeras

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 Series 2021 Jan dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have been hosting playground-style competitions on Kaggle with fun but less complex, tabular datasets. These competitions will be great for people looking for something between the Titanic Getting Started competition and a Featured competition.

ANALYSIS: The performance of the best, preliminary AutoKeras model achieved an RMSE benchmark of 0.7084. When we applied the final model to Kaggle’s test dataset, the model achieved an RMSE score of 0.7092.

CONCLUSION: In this iteration, the TensorFlow model from AutoKeras appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Jan Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.