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 Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than 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. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.
ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 98.16%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.24%. When we processed the test dataset with the final model, the model achieved an accuracy score of 93.45%.
CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Kaggle Tabular Playground Series February 2022
Dataset ML Model: Multi-Class classification with numerical attributes
Dataset Reference: https://www.kaggle.com/c/tabular-playground-series-feb-2022/data
One potential source of performance benchmark: https://www.kaggle.com/c/tabular-playground-series-feb-2022/leaderboard
The HTML formatted report can be found here on GitHub.