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The code uploaded is an implementation of a binary classification problem using the Logistic Regression, Decision Tree Classifier, Random Forest, and Support Vector Classifier.

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sbt5731/Rice-Cammeo-Osmancik

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Rice-Cammeo-Osmancik

A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice. Data Set Characteristics:

Multivariate

Number of Instances:

3810

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

8

Date Donated

2019-10-06

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

1967415

Source:

Ilkay CINAR Graduate School of Natural and Applied Sciences, Selcuk University, TURKEY, ORCID ID : 0000-0003-0611-3316 lkay_cinar '@' hotmail.com

Murat KOKLU Faculty of Technology, Selcuk University, TURKEY. ORCID ID : 0000-0002-2737-2360 mkoklu '@' selcuk.edu.tr

Data Set Information:

Among the certified rice grown in TURKEY, the Osmancik species, which has a large planting area since 1997 and the Cammeo species grown since 2014 have been selected for the study. When looking at the general characteristics of Osmancik species, they have a wide, long, glassy and dull appearance. When looking at the general characteristics of the Cammeo species, they have wide and long, glassy and dull in appearance. A total of 3810 rice grain's images were taken for the two species, processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.

Attribute Information:

1.) Area: Returns the number of pixels within the boundaries of the rice grain. 2.) Perimeter: Calculates the circumference by calculating the distance between pixels around the boundaries of the rice grain. 3.) Major Axis Length: The longest line that can be drawn on the rice grain, i.e. the main axis distance, gives. 4.) Minor Axis Length: The shortest line that can be drawn on the rice grain, i.e. the small axis distance, gives. 5.) Eccentricity: It measures how round the ellipse, which has the same moments as the rice grain, is. 6.) Convex Area: Returns the pixel count of the smallest convex shell of the region formed by the rice grain. 7.) Extent: Returns the ratio of the regionformed by the rice grain to the bounding box pixels. 8.) Class: Cammeo and Osmancik rices

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The code uploaded is an implementation of a binary classification problem using the Logistic Regression, Decision Tree Classifier, Random Forest, and Support Vector Classifier.

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