A Comparative Study on Classification Algorithms

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Santhoshini Banda, Nadia Anjum


The word big data records series of facts that is substantial in length and still developing with time. Breast cancer takes the second position in dangerous diseases. Breast cancer occurs in women which is said to be the most dreadful disease. Around hundreds of thousands of cases are being recorded inside the world each year. It remains an awful lot more usual place in high-profits countries. However, it is now growing at fast in center and low-benefits nations which includes within Africa, America and Asia etc... In this paper, we present a comparison between different classification algorithms. This paper implements popular records mining algorithms (Support vector machine, simple logistic regression, decision tree and random forest) on wiscosin breast cancer dataset. The algorithms are compared based on the accuracy achieved, precision, and recall. The output proves that the most classification accuracy of 97% is achieved by Random forest, Support Vector Machine

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