Regression Model for Kaggle Tabular Playground Series 2021 Apr 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 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 used for this competition is synthetic but based on the real Titanic dataset and generated using a CTGAN. The statistical properties of this dataset are very similar to the original Titanic dataset, but there is no shortcut to cheat by using public labels for predictions.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 0.7253 using the training dataset. We selected k-Nearest Neighbors and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined k-Nearest Neighbors model processed the training dataset with a final accuracy score of 0.7699. When we processed Kaggle’s test dataset with the final model, the model achieved an accuracy score of 0.7780.

CONCLUSION: In this iteration, the k-Nearest Neighbors 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:

One potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.