Multi-Class Tabular Model for Date Fruits Classification Using Python and Scikit-Learn

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 average performance of the machine learning algorithms achieved an accuracy benchmark of 88.39% after training. Furthermore, we selected Linear Discriminant Analysis as the final model as it processed the training dataset with an accuracy score of 91.42% using the 10-fold cross-validation method.

CONCLUSION: In this iteration, the Random Forest 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:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.