Impact of Classification Algorithms on Census Dataset
Sangavi N1, Jeevitha R2, Kathirvel P3, Premalatha K4
1N. Sangavi, Pursuing P.G., Department of Computer Science and Engineering, Bannari amman Insititute of Technology(Autonomous), Sathyamangalam, Erode, Tamil Nadu, India.
2R. Jeevitha, Pursuing P.G., Department of Computer Science and Engineering, Bannari amman Insititute of Technology(Autonomous), Sathyamangalam, Erode, Tamil Nadu, India.
3P. Kathirvel, Pursuing P.G., Department of Computer Science and Engineering, Bannari amman Insititute of Technology(Autonomous), Sathyamangalam, Erode, Tamil Nadu, India.
4Dr. K.Premalatha, Professor and Head, Department of Computer Science and Engineering, Bannari Amman Institute of Technology (Autonomous), Sathyamangalam, Erode, Tamil Nadu. India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2666-2670 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6027018520/2020©BEIESP | DOI: 10.35940/ijrte.E6027.018520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Data mining is a method by which valuable information can be obtained from large databases. A supervised method of classification assigns data samples to target groups. In this system, it uses various classification algorithms namely decision trees, SVM, random forest and neural network. This system will classify and analyses the best suited algorithm which gives maximum accuracy among the other algorithms. The accuracy in these algorithms has been calculated by sensitivity and specificity. Evaluation of these models has been calculated by the error rate with respect to the classes. It uses census dataset and finds whether the income above 50k or below 50k. Matrix of error consists of true positive, neutral, true negative and false negative values. Based on true positive and false negative values, specificity is determined. Based on true negative and false positive values, sensitivity is determined. The algorithm analysis which finds the better algorithm with respect to the accuracy, error rate and efficiency.
Keywords: Decision tree, Neural network model Random forest model, SVM.
Scope of the Article: Soil-Structure Interaction.