MBTI Personality Identification

EDA, Analysis, and ML Deployment: Navigating MBTI Personalities. From in-depth exploration to modeling and deployment, join my journey in understanding and predicting human behaviors using Myers-Briggs Type Indicator.

Project Overview

MBTI Personality Identification is a project aimed at exploring and predicting human behaviors based on the Myers-Briggs Type Indicator (MBTI). Here's an overview of the project components:

1. Data Collection: Gathering data related to MBTI types from various sources, such as social media platforms, forums, or surveys.

2. Preprocessing and Feature Extraction: Cleaning the collected data, extracting relevant features, and preparing it for analysis and modeling.

3. Exploratory Data Analysis (EDA): Conducting in-depth exploration of the data to uncover patterns, correlations, and insights related to MBTI personality types.

4. Machine Learning Models: Developing machine learning models to predict MBTI personality types based on the collected data and extracted features.

5. Model Deployment: Deploying the trained models to make predictions and provide insights into human behaviors.

6. User Interface and Integration: Building a user-friendly interface to interact with the deployed models and integrate them into existing systems or platforms.

7. Continuous Improvement: Iteratively improving the models based on feedback and new data to enhance prediction accuracy and usability.

Overall, MBTI Personality Identification aims to provide a comprehensive solution for understanding and predicting human behaviors using the MBTI framework.

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Built With

This project utilizes several libraries and frameworks to accomplish its objectives:

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Roadmap

• Data collection and preprocessing
• Exploratory Data Analysis (EDA)
• Feature extraction and selection
• Model training and evaluation
• Model deployment and integration
• Continuous improvement and refinement

Next steps:

• Gather additional data sources to improve model performance
• Enhance user interface for better interaction
• Incorporate feedback for model refinement

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Contact

M. Masdar Mahasin

• LinkedIn: M. Masdar Mahasin
• Email: mmasadar@gmail.com
• Portfolio Website: mahasin.xyz

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