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

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 average performance of the machine learning algorithms achieved an RMSE benchmark of 0.5276 using the training dataset. We selected ElasticNet and Extra Trees to perform the tuning exercises. After a series of tuning trials, the refined Extra Trees model processed the training dataset with a final RMSE score of 0.4949. When we apply the last model to Kaggle’s test dataset, the model achieved an RMSE score of 0.7038.

CONCLUSION: In this iteration, the Extra Trees 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.