BibTeX:

@article{IJIRSTV4I2032,
     title={Utilizing Various Machine Learning Techniques to Classify Data in the Business Domain},
     author={Garima Malik, Aakansha Rathore and Sonakshi Vij},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={4},
     number={2},
     pages={118--122},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV4I2032.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Machine learning techniques are commonly deployed in various real-time applications in order to generate interesting inferences, which helps in bridging the gap between relevant knowledge and the user. This paper explores the transformation of the data-sets into consolidated information. A data set that concerns the details of Indian companies, both private and government, is analyzed using K-means clustering, support vector machines and decision trees. Such an analysis will help in making the user fully informed about the budding companies and entrepreneurs in various fields that concerns them. Based upon the analysis, the company is categorized as public, private and one person company. The root node error is found to be minimal in the analysis using decision trees. The users can use this analysis to classify the company class and company status depending on factors such as company’s authorized capital and paid-up capital, which further helps them to understand the revolutionized industrial environment.

Machine Learning, K-Means clustering, Support Vector Machine, Classification, Decision Trees