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 performance of the preliminary XGBoost model achieved an accuracy benchmark of 89.41%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 90.75%.
CONCLUSION: In this iteration, the XGBoost 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.