Binary Classification Model for Kaggle Rice Seed Dataset Using Python and Scikit-learn

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 average performance of the machine learning algorithms achieved an accuracy benchmark of 0.9881 using the training dataset. We selected k-Nearest Neighbors 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 accuracy score of 0.9900. When we processed the test dataset using the final model, the model achieved an accuracy score of 0.9876.

CONCLUSION: In this iteration, the Random Forest 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.