%0 Journal Article %1 noauthororeditor %A Pragnya, Swayanshu Shanti %A Priyadarshi, Shashwat %D 2019 %J International Journal of Computer Science & Engineering Survey (IJCSES) %K Correlation Feature_engineering Feature_selection K_nearest_neighbour PCA RFE logistic_regression %N 2/3 %P 1-11 %T The Implication of Statistical Analysis and Feature Engineering for Model Building Using Machine Learning Algorithms %U http://airccse.org/journal/ijcses/current2019.html %V 10 %X Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.

@article{noauthororeditor, abstract = {Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.}, added-at = {2019-07-10T06:45:46.000+0200}, author = {Pragnya, Swayanshu Shanti and Priyadarshi, Shashwat}, biburl = {https://www.bibsonomy.org/bibtex/2d130fa1e8972dc9dd2c061f4de09c44e/ijcses}, interhash = {60228c1774fd9cc46d808f2fcee2a6b5}, intrahash = {d130fa1e8972dc9dd2c061f4de09c44e}, journal = {International Journal of Computer Science & Engineering Survey (IJCSES)}, keywords = {Correlation Feature_engineering Feature_selection K_nearest_neighbour PCA RFE logistic_regression}, language = {English}, month = {June}, number = {2/3}, pages = {1-11}, timestamp = {2019-07-10T06:45:46.000+0200}, title = {The Implication of Statistical Analysis and Feature Engineering for Model Building Using Machine Learning Algorithms}, url = {http://airccse.org/journal/ijcses/current2019.html}, volume = 10, year = 2019 }