MBTI Personality Type Prediction

This project focuses on predicting the Myers-Briggs Personality Type Indicator (MBTI) using various machine learning techniques. MBTI is a type indicator that categorizes individuals into one of 16 personality types based on their preferences in four dimensions: Introversion/Extraversion, Sensing/Intuition, Thinking/Feeling, and Judging/Perceiving.

Dataset

The dataset used in this project is sourced from [source], containing posts from individuals along with their MBTI type. The data preprocessing steps include cleaning, normalization, and splitting into training and testing sets.

Requirements

• Python 3.x
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit-learn
• Keras
• XGBoost
• WordCloud

To install all the necessary packages, run:

pip install pandas numpy matplotlib seaborn scikit-learn keras xgboost wordcloud

Implementation

• Data Exploration and Visualization: Understanding the distribution of MBTI types and the common words used by each type.
• Preprocessing: Cleaning the text data and preparing it for model training.
• Feature Engineering: Extracting features from the text data that are useful for the prediction of MBTI types.
• Model Training: Implementing different models, including Artificial Neural Networks (ANN) and AdaBoost, to predict the MBTI type based on text data.
• Evaluation: Comparing the performance of different models using accuracy metrics and determining the best-performing model.

Usage

To run this project, execute the Jupyter Notebook cells in sequence. The notebook includes comments and markdown explanations to guide you through the process.

Contributing

Feel free to fork this project, make improvements, and submit a pull request. We're open to any contributions that improve the project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.