Binary Classification Model for Kaggle Tabular Playground Series 2021 November Using TensorFlow

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 performance of the cross-validated TensorFlow models achieved an average accuracy benchmark of 0.6361 after running for 20 epochs. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.7387.

CONCLUSION: In this iteration, the TensorFlow 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.