The week's pick
Reseach Article
Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 45 |
| Year of Publication: 2018 |
| Authors: Swayanshu Shanti Pragnya |
10.5120/ijca2018917155
|
Swayanshu Shanti Pragnya . Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python. International Journal of Computer Applications. 180, 45 ( May 2018), 30-35. DOI=10.5120/ijca2018917155
Abstract
Reinforcement rate of technics and appositeness towards the convenience of the human being is a perennial mechanism. Mathematics has always been in the root towards the implementation of an algorithm or analysis regarding statistics or language. Extracting more about the data and analyzing them to solve a particular problem is the reason behind any analysis. Scrutiny itself has the different number of outcome which can be predictive or descriptive. Now prediction is how far accurate is tested by using various techniques. The enhancement in problem-solving capability leads to come up with a new aptitude concerning machine learning algorithms. But before prediction of data set collection, exploration, feature extraction, model building, accuracy testing are primarily required to invent. So for explaining all these processes, concept learning is essential . In this paper different algorithms like SVM, Linear and Logistic Regression, Decision tree, and Random forest algorithms will be used to demonstrate the accuracy in titanic data from Kaggle Website with all the required steps by using Python language.
References
- THE TRAGEDY OF TITANIC: A LOGISTIC REGRESSION ANALYSIS. Dina Ahmed Mohamed Ghandour1 and May Alawi Mohamed Abdalla2.
- An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain Park, Hyeon-Ae College of Nursing and System Biomedical Informatics National Core Research Center, Seoul National University, Seoul, Korea
- Bagley, S. C., White, H., & Golomb, B. A. (2001). Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain. Journal of Clinical Epidemiology, 54(10), 979-985. Bewick, V., Cheek, L., & Ball, J. (2004).
- Statistics review 13: Receiver operating characteristic curves. Critical Care (London, England), 8(6), 508512. http://dx.doi.org/10.1186/cc3000
- Austin, J. T., Yaffee, R. A., & Hinkle, D. E. (1992). Logistic regression for research in higher education. Higher Education: Handbook of Theory and Research, 8, 379–410. 2. Bagley, S. C., White, H., & Golomb, B. A. (2001).
- Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain. Journal of Clinical Epidemiology, 54(10), 979-9853
- Prediction of Survivors in Titanic Dataset: A Comparative Study using Machine Learning Algorithms Tryambak Chatterjee* Department of Management Studies, NIT Trichy, Tiruchirappalli, Tamilnadu, India
- GE, “Flight Quest Challenge,” Kaggle.com. [Online]. Available: https://www.kaggle.com/c/flight2-final. [Accessed: 2-Jun-2017].
- “Titanic: Machine Learning from Disaster,” Kaggle.com. [Online]. Available: https://www.kaggle.com/c/titanic-gettingStarted. [Accessed: 2-Jun-2017]. Wiki, “Titanic.” [Online]. Available: http://en.wikipedia.org/wiki/Titanic. [Accessed: 2-Jun-2017].
- Kaggle, Data Science Community, [Online]. Available: http://www.kaggle.com/ [Accessed: 2-Jun-2017
- Svmhttps://aitrends.com/ai-insider/support-vector-machines-svm-ai-self-driving-cars/
- https://seaborn.pydata.org/tutorial/regression.html.
- https://afit-r.github.io/logistic_regression
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