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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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Authors
Kavita Agrawal
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Keywords
Logistic regression, Naive Bayes classifiers, XGBoost, Decision Tree
Abstract
Personality is a parameter that makes one individual different from the other individual. Predicting personality has many applications in real world. The main objective of this paper is to take textual data as input from the user and then run the trained machine learning model on this data to predict his 4 personality traits which are Introversion vs Extroversion, Sensing vs Intuition, Thinking vs Feeling, Judging vs Perceiving. The main objective is to build an application where users can answer the questions which are processed and analyzed to output his personality traits. The output is a string of 4 characters where each character determines a personality trait, total of 16 personality types are possible. The Machine learning model XGboost is used to classify the text and output four personality traits. Processing of large textual data is to be done using Natural Language Processing (NLP) techniques with the help of nltk libraries to process and categorize the data. In order to increase the performance of the model hyper parameter tuning along with cross fold validation is done
IJCRT's Publication Details
Unique Identification Number - IJCRT2106344
Paper ID - 208410
Author type - Indian Author
Page Number(s) - d1-d7
Pubished in - Volume 9 | Issue 6 | June 2021
DOI (Digital Object Identifier) - http://doi.one/10.1729/Journal.27467
No Of Downloads - 1133
Author Country - India, 500089, hyderabsd, hyderabsd, 500089, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Kavita Agrawal, "ANALYSIS OF HUMAN TRAITS USING MACHINE LEARNING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 6, pp.d1-d7, June 2021, Available at :http://www.ijcrt.org/papers/IJCRT2106344.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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