This project predicts Myers-Briggs personality types using natural language processing and classification modeling. Capstone project I completed as a student in the Data Science Immersive program at General Assembly

The Myers Briggs Type Indicator (or MBTI for short) is a personality type system that divides everyone into 16 distinct personality types across 4 axis: Introversion (I) – Extroversion (E) Intuition (N) – Sensing (S) Thinking (T) – Feeling (F) Judging (J) – Perceiving (P) (More can be learned about what these mean here) So for example, someone who prefers introversion, intuition, thinking and perceiving would be labelled an INTP in the MBTI system, and there are lots of personality based components that would model or describe this person’s preferences or behaviour based on the label. It is one of, if not the, the most popular personality test in the world. It is used in businesses, online, for fun, for research and lots more. A simple google search reveals all of the different ways the test has been used over time. It’s safe to say that this test is still very relevant in the world in terms of its use. From scientific or psychological perspective it is based on the work done on cognitive functions by Carl Jung i.e. Jungian Typology. This was a model of 8 distinct functions, thought processes or ways of thinking that were suggested to be present in the mind. Later this work was transformed into several different personality systems to make it more accessible, the most popular of which is of course the MBTI. Recently, its use/validity has come into question because of unreliability in experiments surrounding it, among other reasons. But it is still clung to as being a very useful tool in a lot of areas, and the purpose of this dataset is to help see if any patterns can be detected in specific types and their style of writing, which overall explores the validity of the test in analysing, predicting or categorising behaviour. Content This dataset contains over 8600 rows of data, on each row is a person’s: Type (This persons 4 letter MBTI code/type) A section of each of the last 50 things they have posted (Each entry separated by "|||" (3 pipe characters)) Acknowledgements This data was collected through the PersonalityCafe forum, as it provides a large selection of people and their MBTI personality type, as well as what they have written. Inspiration Some basic uses could include: Use machine learning to evaluate the MBTIs validity and ability to predict language styles and behaviour online. Production of a machine learning algorithm that can attempt to determine a person’s personality type based on some text they have written.

Understanding personality traits like introversion and extroversion

This project focuses on predicting personality types using the Myers-Briggs Type Indicator (MBTI) framework through the utilization of Cross-Validation (CV) techniques.

An exploratory data science and machine learning project focused on predicting an individual's personality type (introvert or extrovert) based on behavioral data.

Machine learning pipeline for predicting personality type (Extrovert vs Introvert) , Covers cleaning, feature extraction, univariate & bivariate analysis, data preprocessing , and testing multiple models before selecting a high-accuracy StackingClassifier ensemble

Predicting MBTI personality type using machine learning approach

Predicting Author's MBTI Personality Type based on written text.

TwiType is a university research project aimed at predicting MBTI personality types from Twitter data using psychological and linguistic analysis. The tool offers an interactive platform for personality exploration and behavioral research.

The ​Myers-Briggs Type Indicator (​MBTI)​ is a self-report questionnaire designed to identify a person's personality type between 16 possibilities. Our goal is to develop a machine learning model capable of predicting a person's personality type by analyzing their social media posts​ (supervised classification task).

his repository contains a robust, end-to-end machine learning pipeline for predicting personality types (Extrovert vs. Introvert) from behavioral survey data. The workflow includes advanced data cleaning, missing value imputation, class balancing, feature engineering, model selection, and ensemble learning. Built for the Kaggle Playground Series S.

Predicting the MBTI personality types of Harry Potter characters and houses

This project includes three main stages. Data collection, data modeling and implementation. The data set found for this project contains more than 8600 rows of data. trained and tested our data using different algorithms of machine learning and we were able to get an accuracy of over 80%.

Utilized data from: https://www.kaggle.com/datasets/rakeshkapilavai/extrovert-vs-introvert-behavior-data/data to build and train machine learning models to predict whether a person is an introvert or extrovert based on social behavior patterns.

Predicting personality types

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A report written for a data mining class about using data mining techniques to predict a person's personality type.

Predicting personality type (from Myers-Briggs 16 personality types) using logistic regression and Naïve Bayes' classification.

predicting personality types based on posts made on social media, using natural language processing.

UIUC CS 410 Project

No description available

ML project on predicting MBTi personality types

Categorise people according to the characteristics that make up their personalities Uses NLP and machine learning algorithms to predict the HEXACO personality traits and MBTI personality type based on the input given by the user

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Python project for the Predicting Personality Type Challenge

Predicting personality types (Introvert/Extrovert) from behavioral traits

SENG 474 data mining project - predicting MBTI personality type

Predicting Myers-Briggs personality types by analyzing social-media posts.

Kaggle Playground Series - Season 5, Episode 7 - Predicting personality type

End-to-end ML project for predicting personality type (Introvert/Extrovert)