Personality prediction using machine learning classifiers

Personality is a fundamental basis of human behaviour. At most basic, personality including patterns of thought, feeling, behaviours that make an individual unique. Personality will directly or indirectly influence the interaction or preferences of a person. This research using different learning algorithms and concepts of data mining to mine on the data features and learn from the pattern. The aim of this experiment is to explore different options of the algorithm on modifying the personality prediction source code by using logistic regression algorithm, and to find whether the accuracy of the classification can be improved. There are five characteristics of different people that are known as the Big Five characteristic, which is openness, neuroticism, conscientiousness, agreeableness and extraversion that have been stored in the dataset used for training. Then, an overview and comparison will be provided on the different measures taken to reduce the issues faced by researchers in this field. Classification methods implemented are Support Vector Machine, Ridge Algorithm, Naive Bayes, Logistic Regression and Voting Classifier. Testing results showed that the Logistic Regression still outperformed the other methods.