Glass Classification based on Machine Learn-ing Algorithms
Harshit Mathur1, Aditya Surana2

1Harshit Mathur, Department of Computer Science, Jaipur Engineering College and Research Centre, Jaipur, India.
2Aditya Surana, Department of Computer Science, Jaipur Engineering College and Research Centre, Jaipur, India.
Manuscript received on August 26, 2020. | Revised Manuscript received on September 05, 2020. | Manuscript published on September 10, 2020. | PP: 139-142 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.H6819069820 | DOI: 10.35940/ijitee.H6819.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Logis-tic Regression algorithm. By comparing all the algorithm Ran-dom Forest did the best in glass classification. 
Keywords: Glass Classification, Machine Learning, Support Vector Machine (SVM) algorithm, K-nearest neighbors (KNN) algorithm, Random Forest algorithm, Artificial Neural Network (ANN), Logistic Regression algorithm.