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 Series 2021 Feb dataset is a regression situation where we are trying to predict the value of a continuous variable.
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 February dataset may be synthetic but is based on a real dataset and generated using a CTGAN. The original dataset tries to predict the amount of an insurance claim. Although the features are anonymized, they have properties relating to real-world features.
ANALYSIS: The performance of the preliminary XGBoost model achieved an RMSE benchmark of 0.8531. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final RMSE score of 0.8434. When we applied the last model to Kaggle’s test dataset, the model achieved an RMSE score of 0.8443.
CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground Series 2021 Feb Data Set
Dataset ML Model: Regression with numerical and categorical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2021
One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-feb-2021/leaderboard
The HTML formatted report can be found here on GitHub. [https://github.com/daines-analytics/tabular-data-projects/tree/master/py_regression_kaggle_tabular_playground_2021feb]