Accuracy Prediction Using Analysis Methods and F-Measures. (November 2019)

• Record Type: Journal Article
• Authors: El Fiorenza Caroline, J; Parmar, Prateek; Tiwari, Shivam; Dixit, Ayush; Gupta, Arjun
• Is Part Of: Journal of physics. Volume 1362(2019)
• Publication Date: 2019-11
• Subjects: Accuracy analysis -- Algorithms -- computation -- feature selection -- machine learning -- classification -- predictions; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1362/1/012040
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Accuracy prediction is basically used in machine learining for evaluating the accuracy of data to get better results during analysis of data for various purposes like financial analysis, credit card fraud detection and sales prediction. Predicting the accuracy of data is necessary for making better decisions in field of business, engineering, medical science and analytics. We introduce a methodology for analysis that improves the accuracy of data while ensuring that the performance of the algorithm also improves so that it improves decision making so that it can be used in real world applications. The analysis involves three phases, first is product analysis phase which involves product analysis and SWOT analysis. Then comes analysis phase where we use various techniques like Straight line method of depreciation, moving average technique, simple linear regression and multiple linear regression. These methods are used for analyzing the trend in data and for comparison. Then comes the next phase where we calculate accuracy and find optimal value. For that we first add more data, then we select essential features for getting accurate results. For that we use multiple algorithms. Multiple algorithms basically consists of algorithms that are used for clustering, classification and comparison. These algorithms are used for creating a better machine learning model by using ensemble method. Ensemble method is basically a method of combining various weak algorithms to create a more accurate algorithm that gives better performance. For checking and performance and getting an accurate value we use Algorithm tuning. Algorithm tuning is used for getting an improved algorithm that gives less error percentage is assists in making predictions. This gives an accurate and optimized model for training the data.