Fozan Ahmed
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Personality Distribution Analysis

Data Science
Python
Data Analysis
Pandas
Jupyter
Visualization

A comprehensive data analysis project exploring MBTI personality distributions within AequorTech CTRL to optimize team dynamics and engagement.

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Overview

This project focuses on analyzing the MBTI (Myers-Briggs Type Indicator) personality types of volunteering participants at AequorTech CTRL. By leveraging a curated dataset, the study aims to understand the psychological makeup of the team to inform better decision-making regarding team composition, role alignment, and engagement strategies.

The analysis moves beyond synthetic data, utilizing real participant records to provide actionable insights into the organization's human capital.

Objectives

The primary goals of this research were:

  • Team Design: To understand the dominant and recessive personality traits within the group to balance teams effectively.
  • Engagement: To tailor communication and engagement strategies based on the aggregate personality profile of the volunteers.
  • Role Alignment: To assist in matching individuals to roles that naturally fit their personality predispositions.

Methodology

The project follows a rigorous Exploratory Data Analysis (EDA) pipeline:

  1. Data Ingestion: Loading participant data from participants_mbti.csv, which includes fields for Name, Age, Gender, City, Country, MBTI, and Occupation.
  2. Normalization: Standardizing MBTI codes (e.g., converting to uppercase) to ensure consistency.
  3. Computation: Calculating counts and percentage distributions of different personality types.
  4. Visualization: Generating clear, interpretable charts (e.g., participants_personality_distribution.png) to communicate findings.

Tech Stack

  • Language: Python 3.12
  • Libraries: Pandas (Data Manipulation), Matplotlib/Seaborn (Visualization)
  • Environment: Jupyter Notebook for interactive analysis and documentation.

Key Features

  • Reproducibility: The analysis is deterministic; running the script on the same dataset yields identical results, ensuring scientific validity.
  • Advanced Analysis: Includes cross-tabulations (e.g., MBTI × Gender, MBTI × Occupation) and age distribution analysis in the advanced notebook.
  • Scalability: The pipeline is designed to accommodate additional features like tenure or interests for future, richer analyses.

Future Scope

The research lays the groundwork for more advanced applications, such as:

  • Building an interactive dashboard using Streamlit.
  • Implementing clustering algorithms to find hidden subgroups.
  • Expanding the dataset to include performance metrics for predictive modeling.