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Personality is useful for recognizing how people lead, influence, communicate, collaborate, negotiate business and manage stress. Personality is one of the important main features that determines how people interact with outside world. This project is helpful where we have data related to personal behaviour. This personal behaviour data can be useful for identifying person based on his/her personality traits. The personality characteristics will be already stored in database. Later when user enters his personality characteristics his personality is examined in database and system will detect the personality of user, It is based on Big Five Personality Traits Personality is one feature that determines how people interact with the outside world. This data can be helpful to classify persons using Automated personality classification (APC). This learning can now be used to classify/predict user personality based on past classifications. This system is useful to social networks as well as various ad selling online networks to classify user personality and sell more relevant ads. This system will be helpful for organizations as well as other agencies who would be recruiting applicants based on their personality rather than their technical knowledge. In this project, we propose a system which analyses the personality of an applicant.

Key Words: Personality, Behaviour, Logistic regression, Decision tree, Support Vector Machine, Big Five personality Traits.


Personality identification of a human being by their nature an old technique. Earlier these were done manually by spending lot of time to predict the nature of the person. Data mining is primarily used today by companies with a strong consumer focus- retail, financial, communication, and marketing organizations. Methods used to analyse the data include surveys, interviews, questionnaires, classroom activities, shopping website data, social network data about the user experiences and problems they are facing. But these traditional methods are time consuming and very limited in scale. Our Proposed system will provide information about the personality of the user. Based on the personality traits provided by the user, System will match the personality traits with the data stored in database. System will automatically classify the user’s personality and will match the pattern with the stored data. System will examine the data stored in database and will match the personality traits of the user with the data in database. Then system will detect the personality of the user. Based on the personality traits of the user, system will provide other features that are relevant to the user’s personality.

Personality can also affect his/her interaction with the outside world and his/her environment. Personality can also be used as an additional feature during recruitment process, career counselling, health counselling, etc. Predicting personality by analyzing the behaviour of the person is an old technique. This manual method of personality prediction required a lot of time and resources. Analyzing personality based on one’s nature was a tedious task and a lot of human effort would be required to do such analysis. Also, this manual analysis did not give accurate results while analyzing the personality of a user from their nature and behaviour. Since analysis was done manually, it affects the accuracy of the results as humans prone to be prejudice and generally see the things accordingly.

Supervised learning:

In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

Unsupervised learning:

This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined. Examples of Unsupervised Learning: Apriori algorithm, K-means.

Semi-supervised learning:

This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.

Reinforcement learning:

Data scientists typically use reinforcement learning to teach a machine to complete a multi- step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way. Example of Reinforcement Learning: Markov Decision Process.

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