Commonly used Algorithms in Data Science Along with Internal Logics and Implementations through R Programming
T. Srikanth1, Sateesh Nagavarapu2, K. Umapavankumar3, Narahari D4

1Mr. T. Srikanth*, Asst. Professor,CSE, Malla Reddy Inst. of Tech.
2Dr. Sateesh Nagavarapu, Associate Professor,CSE, Malla Reddy Inst. of Tech
3Dr. K. Umapavankumar, Associate Professor,CSE, Malla Reddy Inst. of Tech.
4Mr. NarahariD, Asst. Professor,CSE, Malla Reddy Inst. of Tech
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2649-2652 | Volume-9 Issue-3, February 2020. | Retrieval Number:  C5811029320/2020©BEIESP | DOI: 10.35940/ijeat.C5811.029320
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 (

Abstract: The terms machine learning, deep learning and data science are buzz words now a days. The usage of these techniques with some technologies like R and Python is most common in the industry and academics. The current work is dealing with the inherent logics existing in the algorithms like Classification, Dimensionality reduction and Recommender systems along with the suitable examples. Some of the applications mentioned here like Facebook, Twitter and LinkedIn to exploit the usage of these algorithms in their daily usage. The discussion about online platforms like Amazon, Flipkart are other areas where the recommender systems were most commonly used algorithms. The outcome of the work is the logical things hidden in the usage of the algorithms and the implementation wise which are packages and functions helpful for the implementation of the algorithms. The belief is the work will be helpful for the researchers and academicians in the context of algorithmic perspective and they can extend the work by contributing their thoughts and views on the same work. Unlike in the normal programming, R/Python simplifies the logic of algorithms so that the lines of code and understanding of the problem is bit simple when compared with general programming languages. The work explains the mail respondents related to the allocation of the house by the company as a response to their mail by considering Urban, semi-urban and rural areas of the customers, the income range of the customers also observed in the allocation of the house. The implementations are with R by using classification and the corresponding results were published with the explanation of the values found in the implementation.
Keywords: R, Mail respondents, , Classification, rpart, Factor Analysis.