Binary Classification Model for Kaggle Tabular Playground Series 2021 October Using Scikit-learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. [https://machinelearningmastery.com/]

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 October 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 a real dataset and generated using a CTGAN. The original dataset deals with predicting the biological response of molecules given various chemical properties. Although the features are anonymized, they have properties relating to real-world features.

ANALYSIS: The average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 0.7767 using the training dataset. We selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 0.8397. When we processed Kaggle’s test dataset with the final model, the model achieved a ROC/AUC score of 0.8378.

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

Dataset Used: Kaggle Tabular Playground 2021 October Data Set

Dataset ML Model: Binary classification with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-oct-2021

One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-oct-2021/leaderboard

The HTML formatted report can be found here on GitHub.