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TRAIT ANALYTICS USING MACHINE LEARNING

Akash Matkar, Pramod Jadhao

Master Of Computer Application, TrinityAcademy of Engineering, Pune

Abstract: With applications in a variety of fields, such as marketing, human resources, and psychology, trait analytics is an exciting area of study. This project's goal is to use machine learning approaches to predict personality traits from individual textual input. The project makes use of a data set that includes text samples, such emails, essays, or postings on social media, together with personality labels that are taken from well-known models, such as the Big Five personality traits. Data preprocessing, feature extraction, model selection, and performance evaluation are some of the crucial processes in the project. The data is cleaned and prepared using text preprocessing methods such tokenization, stop-word removal, and abbreviation expansion. For personality prediction, a variety of machine learning models are taken into consideration, such as neural networks, logistic regression, and random forests. The models are assessed and contrasted using performance criteria such F1 score, recall, accuracy, and precision.  The model with the best overall performance is chosen by the system.  The project's outcomes show that utilizing machine learning to predict personality traits from textual data is feasible.  The experiment also emphasizes how crucial careful feature selection and model validation are to producing precise forecasts.  Moving from basic personality prediction to more sophisticated trait analytics, future research could investigate deep learning models and sophisticated text processing approaches to further improve prediction performance.