Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The 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 2022 August dataset is a binary-class modeling situation where we attempt to predict one of 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. This data represents the results of an extensive product testing study. For each product code, the research team provided several product attributes and several measurement values for each product, representing various lab testing methods.
Each product is used in a simulated real-world environment experiment and absorbs a certain amount of fluid to see whether it fails. The project task is to use the data to predict individual product failures of new codes with their lab test results.
ANALYSIS: The performance of the preliminary Random Forest model achieved a ROC_AUC benchmark of 0.9703 on the training dataset. When we applied the finalized model to the test dataset, the model achieved a ROC_AUC score of 0.5473.
CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground 2022 August
Dataset ML Model: Binary classification with numerical features
Dataset Reference: https://www.kaggle.com/competitions/tabular-playground-series-aug-2022
One source of potential performance benchmarks: https://www.kaggle.com/competitions/tabular-playground-series-aug-2022/leaderboard
The HTML formatted report can be found here on GitHub.