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 Playground Series Season 3 Episode 7 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 to give the Kaggle community a variety of reasonably lightweight challenges that can be used to learn and sharpen skills in different aspects of machine learning and data science. The dataset for this competition was generated from a deep learning model trained on the Reservation Cancellation Prediction dataset. Feature distributions are close to but different from the original.
ANALYSIS: The average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 0.8470 after training. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a ROC/AUC score of 0.8891. When we processed the test dataset with the final model, the model achieved a ROC/AUC score of 0.8716.
CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Playground Series Season 3, Episode 7
Dataset ML Model: Binary-Class classification with numerical features
Dataset Reference: https://www.kaggle.com/competitions/playground-series-s3e7
One source of potential performance benchmarks: https://www.kaggle.com/competitions/playground-series-s3e7/leaderboard
The HTML formatted report can be found here on GitHub.