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

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 preliminary XGBoost model achieved an RMSE benchmark of 0.5068. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final RMSE score of 0.4883. When we applied the last model to Kaggle’s test dataset, the model achieved an RMSE score of 0.6996.

CONCLUSION: In this iteration, the XGBoost model 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.