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 Rice Varieties Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: The dataset owner collected 75,000 pieces of rice grain and created a dataset that classifies the grains into one of the varieties (Arborio, Basmati, Ipsala, Jasmine, Karacadag). The research team applied various preprocessing operations to the rice images and obtained the features. Each record contains 106 attributes, including 12 morphological features, four shape features, and 90 color features obtained from five different color spaces (RGB, HSV, Lab*, YCbCr, XYZ).

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 99.88%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 99.90%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.87%.

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

Dataset Used: Rice MSC Dataset

Dataset ML Model: Multi-Class classification with numerical features

Dataset Reference: https://www.kaggle.com/mkoklu42/rice-msc-dataset

One source of potential performance benchmarks: https://www.kaggle.com/mkoklu42/rice-msc-dataset/code

The HTML formatted report can be found here on GitHub.