Regression Model for Kaggle Tabular Playground Series 2021 August Using Python and Scikit-learn

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 Aug 2021 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 hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. The February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the loss from a loan default. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The average performance of the machine learning algorithms achieved an RMSE benchmark of 8.0771 using the training dataset. We selected ElasticNet and Gradient Boosting to perform the tuning exercises. After a series of tuning trials, the refined Gradient Boosting model processed the training dataset with a final RMSE score of 7.8563. When we processed Kaggle’s test dataset with the final model, the model achieved an RMSE score of 7.8416.

CONCLUSION: In this iteration, the Gradient Boosting model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series Aug 2021 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.