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 September 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 whether a customer will file a claim on an insurance policy. 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 0.6891 after running for 50 epochs. When we applied the final model to Kaggle’s test dataset, the model achieved an accuracy score of 0.6189.
CONCLUSION: In this iteration, the TensorFlow model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground 2021 September Data Set
Dataset ML Model: Binary classification with numerical and categorical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-sep-2021
One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-sep-2021/leaderboard
The HTML formatted report can be found here on GitHub.