Binary Classification Model for Kaggle Rice Seed Dataset Using Python and XGBoost

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 Rice Seed dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.

INTRODUCTION: The dataset owner collected data on two different kinds of rice (Gonen and Jasmine). The goal is to train the best model that can correctly predict the rice crop.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 0.9903. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final score of 0.9903. When we applied the final model to the test dataset, the model achieved an accuracy score of 0.9879.

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

Dataset Used: Kaggle Rice Seed Dataset

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.