Binary Classification Model for Kaggle Tabular Playground Series 2021 November Using 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 November 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 data is synthetically generated by a GAN trained on a real-world dataset used to identify spam emails via various extracted features from the email. 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 0.6588 using the training dataset. We selected Logistic Regression as the final model as it processed the training dataset with a final ROC/AUC score of 0.7489. When we processed Kaggle’s test dataset with the final model, the model achieved a ROC/AUC score of 0.7454.

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

Dataset Used: Kaggle Tabular Playground 2021 November Data Set

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.