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 Date Fruits Dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: Many factors determine the type of fruit with external appearance features such as color, length, diameter, and shape. However, classifying the variety of fruits simply by looking at their outward appearance requires expertise and a great effort and is also time-consuming. This study aims to classify the types of date fruit using machine-learning methods. The research team obtained 898 images of seven different date fruit types for this project via a computer vision system. The research team was hopeful that machine learning methods could successfully classify date fruit types.
ANALYSIS: The Gradient Boosted Trees model performed the best with the training dataset. The final model achieved an average accuracy benchmark of 90.41% after 30 iterations of cross-validation.
CONCLUSION: In this iteration, the Gradient Boosted Trees model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Date Fruits Dataset
Dataset ML Model: Multi-Class classification with numerical features
Dataset Reference: https://www.muratkoklu.com/datasets/
One source of potential performance benchmarks: https://doi.org/10.1155/2021/4793293
The HTML formatted report can be found here on GitHub.