Important Feature Selection for Predicting Human Freedom Index Score using Machine Learning Algorithms
Poonam Kumari1, S Prasad Babu Vagolu2, Sunil Chandolu3
1Poonam Kumari*,Currently Pursuing Master of Computer Applications in Department of Computer Science, GIS, GITAM (Deemed to be University), Viskahapatnam, India.
2S Prasad Babu Vagolu, Currently an Assistant Professor in Department of Computer Science, GIS, GITAM (Deemed to be University), Viskahapatnam, India.
3Sunil Chandolu, Currently an Assistant Professor in Department of Computer Science, GIS, GITAM (Deemed to be University), Viskahapatnam, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on April 01, 2020. | Manuscript published on April 10, 2020. | PP: 630-632 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3881049620/2020©BEIESP | DOI: 10.35940/ijitee.F3881.049620
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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human freedom index refers to the state of human freedom in various countries based their personal and economic attributes. Human freedom can help us identify nobility of citizens in a country. For an individual of a country freedom is of great value and hence it is worthy to measure. Though there are many attributes to measure the human freedom index both in personal as well as in economic factors, here we are interested to find only those features which contribute the most and are relevant to predict the outcome i.e. human freedom index score. We will go through various features engineering process like removing strongly correlated attributes, filtering method using Mutual Information (Entropy) and then use Select KBest algorithm to select top features filtered through Mutual information. These steps will help reduce the training time, increase accuracy and reduce overfitting when model is created to predict the human freedom index score which is a Machine Learning Regression problem.
Keywords: Corelated Feature, Decision Tree Regression, Feature Engineering, Human Freedom Index, Linear Regression, Machine Learning, Mutual Information, Random Forest Regression, Regression, Select K Best.
Scope of the Article: Artificial Intelligence and machine learning