Job Recommendation System Implementation in Python vs. C++
Chanda1, Rajesh Kumar Aggarwal2
1Chanda, Student, Department of University School of Information, Communication and Technology (U.S.I.C.T), Guru Gobind Singh Indraprastha University (G.G.S.I.P.U), New Delhi, India.
2Rajesh Kumar Aggarwal, Student, Department of University School of Information, Communication and Technology (U.S.I.C.T), Guru Gobind Singh Indraprastha University (G.G.S.I.P.U), New Delhi, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2299-2302 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8132118419/2019©BEIESP | DOI: 10.35940/ijrte.D8132.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Implementing a machine learning algorithm gives you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for the transformations on those structures. There are numerous micro-decisions required when implementing a machine learning algorithm, like Select programming language, Select Algorithm, Select Problem, Research Algorithm, Unit Test and these decisions are often missing from the formal algorithm descriptions. The notion of implementing a job recommendation (a classic machine learning problem) system using to two algorithms namely, KNN  and logistic regression  in more than one programming language (C++ and python) is introduced and we bring here the analysis and comparison of performance of each. We specifically focus on building a model for predictions of jobs in the field of computer sciences but they can be applied to a wide range of other areas as well. This paper can be used by implementers to deduce which language will best suite their needs to achieve accuracy along with efficiency We are using more than one algorithm to establish the fact that our finding is not just singularly applicable.
Keywords: Algorithm Comparison, C++ Vs. Python, Data Analysis, Job Recommendation, K-Nearest Neighbor, Logistic Regression, Machine Learning.
Scope of the Article: Machine Learning.