Binary-Class Tabular Classification Model for Raisin Grains Identification Using Python and XGBoost

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 Raisin Grains Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In this study, the research team developed a computerized vision system to classify two different varieties of raisin grown in Turkey. The dataset contains the measurements for 900 raisin grain images. The image further broke down into seven major morphological features for each grain of raisin.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 85.92%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 86.17%. When we processed the test dataset with the final model, the model achieved an accuracy score of 86.66%.

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

Dataset Used: Raisin Dataset

Dataset ML Model: Binary classification with numerical features

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.