mltrev23/Rice-classification

Rice Classification Dataset

Overview

The Rice Classification Dataset is intended for the classification of different types of rice grains based on their physical and geometric properties. The dataset includes multiple features that describe the shape and size of rice grains, which can be utilized to classify the grains using various machine learning algorithms.

Dataset Structure

The dataset is provided as a single CSV file named riceClassification.csv. It contains 18,185 entries, each corresponding to a unique rice grain. The dataset is structured with the following columns:

Columns

1. id: Unique identifier for each rice grain (integer).
2. Area: The area covered by the rice grain, given as an integer.
3. MajorAxisLength: The length of the major axis of the rice grain (float).
4. MinorAxisLength: The length of the minor axis of the rice grain (float).
5. Eccentricity: Measure of the eccentricity of the rice grain, which describes the deviation of the grain's shape from a perfect circle (float).
6. ConvexArea: The area of the convex hull surrounding the rice grain (integer).
7. EquivDiameter: The diameter of a circle with the same area as the rice grain (float).
8. Extent: The ratio of the area of the rice grain to the area of the bounding box (float).
9. Perimeter: The perimeter of the rice grain (float).
10. Roundness: Measure of how close the shape of the rice grain is to a circle (float).
11. AspectRation: The ratio of the major axis length to the minor axis length (float).
12. Class: The class label indicating the type of rice grain, where 0 represents one class and 1 represents another (binary integer).

Summary Statistics

Below are the summary statistics for the dataset:

• Area: Ranges from 2,522 to 10,210, with a mean of 7,036.49.
• MajorAxisLength: Ranges from 74.13 to 183.21, with a mean of 151.68.
• MinorAxisLength: Ranges from 34.41 to 82.55, with a mean of 59.81.
• Eccentricity: Ranges from 0.68 to 0.97, with a mean of 0.92.
• ConvexArea: Ranges from 2,579 to 11,008, with a mean of 7,225.82.
• EquivDiameter: Ranges from 56.67 to 114.02, with a mean of 94.13.
• Extent: Ranges from 0.38 to 0.89, with a mean of 0.62.
• Perimeter: Ranges from 197.02 to 508.51, with a mean of 351.61.
• Roundness: Ranges from 0.17 to 0.90, with a mean of 0.71.
• AspectRation: Ranges from 1.36 to 3.91, with a mean of 2.60.
• Class: Binary class, with approximately equal distribution between the two classes.

Usage

This dataset can be used for various machine learning tasks, particularly for binary classification. The dataset's rich feature set makes it suitable for exploring algorithms like logistic regression, support vector machines, decision trees, and neural networks.