Evaluation of Data Science Algorithm Using Prediction System: Government Schemes in Rural Sectors

2021 ◽  
pp. 20-35
Author(s):  
S. Maheswari ◽  
M. Manochitra ◽  
D. Thamarai Selvi
Keyword(s):  
Data Science ◽  
Prediction System ◽  
Evaluation Of Data

scholarly journals Applying Government Schemes in Rural Sectors Prediction System for Evaluation of Data Science Algorithm

2021 ◽  
Vol 9 (5) ◽  
pp. 70-79
Author(s):  
S. Maheswari ◽  
S. Kalaiselvi ◽  
D.Thamarai Selvi ◽  
M. Manochitra
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Environmental Factors ◽  
Data Science ◽  
Naive Bayes ◽  
Support Vector ◽  
Prediction System ◽  
Government Assistance ◽  
The Government ◽  
Evaluation Of Data

The administration dispatches different aggressive projects attempting to make the nation more prosperous, yet what they bomb is in fruitful execution and coming to recipients. The fundamental explanation for this issue is the absence of mindfulness among rustic individuals. This paper is to give an answer for this uninformed circumstance. Through this framework the rustic understudies will be instructed such that they can become acquainted with about what are the different plans that are outfitted by the administration and what are the plans they are qualified for. On the off chance that the country understudies came to know and get mindful of the apparent multitude of legislative plans gave by the Government of India for the government assistance of the provincial understudies, at that point their life would venture into next level. At first this framework will investigate the accessible government plans in the instructive for the government assistance of country understudies. Next, the understudy's information ((i.e.) name, age, station, occupation, annualincome.etc) are accumulated. At that point; both the datasets are brought into the Anaconda Navigator. At that point, investigation and grouping dependent on networks (SC, ST, BC and MBC) of the understudies and the plans are performed. At that point utilizing the forecast calculations (Naïve Bayes, Random Forest and Support Vector Machine (SVM)) what are generally the plans the specific understudy is qualified for are anticipated. An investigation is made on the proficiency of the three calculations. The exactness of the three calculations is broke down and the effective calculation which creates the outcome with most elevated precision is at last used to play out the forecast of the plans that a specific understudy is qualified for. At long last, the anticipated plans anticipated utilizing the most elevated effective calculation among the three calculations will be gotten back to the understudies. Hence, through this undertaking the rustic understudies will come to think about different recipient plans gave by government and they can use those plans for the improvement of the country environmental factors

scholarly journals Documenting and evaluating Data Science contributions in academic promotion in Departments of Statistics and Biostatistics

2017 ◽  
Author(s):  
Lance A Waller
Keyword(s):  
Department Chair ◽  
Data Science ◽  
Junior Faculty ◽  
Faculty Members ◽  
Junior Faculty Member ◽  
Senior Faculty ◽  
Academic Promotion ◽  
Academic Communities ◽  
Lively Debate ◽  
Evaluation Of Data

The dynamic intersection of the field of Data Science with the established academic communities of Statistics and Biostatistics continues to generate lively debate, often with the two fields playing the role of an upstart (but brilliant), tech-savvy prodigy and an established (but brilliant), curmudgeonly expert, respectively. Like any emerging discipline, Data Science brings new perspectives and new tools to address new questions requiring new perspectives on traditionally established concepts. We explore a specific component of this discussion, namely the documentation and evaluation of Data Science-related research, teaching, and service contributions for faculty members seeking promotion and tenure within traditional departments of Statistics and Biostatistics. We focus on three perspectives: the department chair nominating a candidate for promotion, the junior faculty member going up for promotion, and the senior faculty members evaluating the promotion package. We contrast conservative, strategic, and iconoclastic approaches to promotion based on accomplishments in Data Science.

scholarly journals Salary Estimator using Data Science

2020 ◽  
Vol 6 (12) ◽  
pp. 319-322
Author(s):  
Winner Walecha and Dr. Bhoomi Gupta
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Data Science ◽  
The Internet ◽  
Prediction System ◽  
Lasso Regression ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Number Of Factors ◽  
Using Data

This paper presents a salary prediction system using the job listings from an employment website, in this case Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobs from the employment website, clean it on the basis of number of factors including the rival companies, revenue and skill required thereby predicting the salary to be expected when applying for a data science job. Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model. The model can be further extended to build a flask API thus can be deployed on the internet for public usage.

scholarly journals Documenting and evaluating Data Science contributions in academic promotion in Departments of Statistics and Biostatistics

2017 ◽  
Author(s):  
Lance A Waller
Keyword(s):  
Department Chair ◽  
Data Science ◽  
Junior Faculty ◽  
Faculty Members ◽  
Junior Faculty Member ◽  
Senior Faculty ◽  
Academic Promotion ◽  
Academic Communities ◽  
Lively Debate ◽  
Evaluation Of Data

The dynamic intersection of the field of Data Science with the established academic communities of Statistics and Biostatistics continues to generate lively debate, often with the two fields playing the role of an upstart (but brilliant), tech-savvy prodigy and an established (but brilliant), curmudgeonly expert, respectively. Like any emerging discipline, Data Science brings new perspectives and new tools to address new questions requiring new perspectives on traditionally established concepts. We explore a specific component of this discussion, namely the documentation and evaluation of Data Science-related research, teaching, and service contributions for faculty members seeking promotion and tenure within traditional departments of Statistics and Biostatistics. We focus on three perspectives: the department chair nominating a candidate for promotion, the junior faculty member going up for promotion, and the senior faculty members evaluating the promotion package. We contrast conservative, strategic, and iconoclastic approaches to promotion based on accomplishments in Data Science.

scholarly journals DOCUMENTING AND EVALUATING DATA SCIENCE CONTRIBUTIONS IN ACADEMIC PROMOTION IN DEPARTMENTS OF STATISTICS AND BIOSTATISTICS

2017 ◽  
Author(s):  
Lance A. Waller
Keyword(s):  
Data Science ◽  
Faculty Members ◽  
Specific Component ◽  
Academic Promotion ◽  
Related Research ◽  
Promotion And Tenure ◽  
Academic Communities ◽  
Lively Debate ◽  
Evaluation Of Data

ABSTACTThe dynamic intersection of the emerging field of Data Science with the established academic communities of Statistics and Biostatistics continues to generate lively debate, often with the two fields playing the role of an upstart (but brilliant), tech-savvy prodigy and an established (but brilliant), curmudgeonly expert, respectively. Like any new discipline, Data Science brings new perspectives and new tools to address new questions requiring new perspectives on traditionally established concepts. In this paper, we explore a specific component of this discussion, namely the documentation and evaluation of Data Science-related research, teaching, and service contributions for faculty members seeking promotion and tenure within traditional departments of statistics and Biostatistics.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

EVALUATION OF DATA FROM 1D-PSD USED IN TOF METHOD

1986 ◽  
Vol 47 (C5) ◽  
pp. C5-129-C5-136 ◽  
Author(s):  
N. NIIMURA
Keyword(s):  
Evaluation Of Data
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