Binary-Class Tabular Model for Pumpkin Seeds 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 Pumpkin Seeds Identification dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: Pumpkin seeds are frequently consumed as confection worldwide because of their adequate amount of protein, fat, carbohydrate, and mineral contents. The research team carried out a study on the two most important types of pumpkin seed, “Ürgüp Sivrisi” and “Çerçevelik,” generally grown in Ürgüp and Karacaören regions in Turkey. Furthermore, the morphological measurements of 2500 pumpkin seeds of both varieties were captured using the gray and binary forms of threshold techniques.

ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 88.40%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 88.68%.

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

Dataset Used: Pumpkin Seeds 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.