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 Mar 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 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 average performance of the machine learning algorithms achieved a ROC benchmark of 0.8430 using the training dataset. We selected Logistic Regression and Random Forest to perform the tuning exercises. After a series of tuning trials, the refined Random Forest model processed the training dataset with a final ROC score of 0.8901. When we processed Kaggle’s test dataset with the final model, the model achieved a ROC score of 0.8902.

CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series 2021 Mar Data Set

Dataset ML Model: Regression with numerical and categorical attributes

Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-mar-2021

One potential source of performance benchmarks: https://www.kaggle.com/c/tabular-playground-series-mar-2021/leaderboard

The HTML formatted report can be found here on GitHub.