Personality Prediction ML Model Development

Jainish Prajapati

ML Engineer

Matplotlib

pandas

scikit-learn

Artificial Intelligence

Personality Prediction ML

A machine learning model that predicts personality types (Introvert/Extrovert) using behavioral patterns and Random Forest classification with Flask REST API implementation.

Overview

This project uses machine learning to analyze behavioral patterns and predict whether someone is more likely to be an introvert or extrovert. It leverages a Random Forest Classifier trained on various behavioral indicators and provides predictions through a REST API.

Features

Predicts personality type based on 7 behavioral indicators

Random Forest Classifier with optimized performance

REST API implementation using Flask

Comprehensive data visualization and analysis

Data preprocessing and feature scaling

Model persistence using pickle

Tech Stack

Python 3.x

scikit-learn

Flask

pandas

numpy

matplotlib

seaborn

Project Structure

personality-prediction-ml/
│
├── models/                    # Saved model files
│   ├── random_forest_model.pkl
│   ├── logistic_regression_model.pkl
│   ├── svm_model.pkl
│   └── scaler.pkl
│
├── main.ipynb                # Main analysis notebook
├── api.py                    # Flask API implementation
├── personality_dataset.csv   # Dataset
├── requirements.txt          # Project dependencies
└── README.md

API Endpoints

POST /predict

Predicts personality type based on behavioral inputs.

Request Body:

{
    "time_spent_alone": 7.0,        # Range: 0-10
    "stage_fear": "Yes",            # Options: "Yes"/"No"
    "social_event_attendance": 2.0,  # Range: 0-10
    "going_outside": 3.0,           # Range: 0-7
    "drained_after_socializing": "Yes", # Options: "Yes"/"No"
    "friends_circle_size": 4.0,     # Range: 0-15
    "post_frequency": 2.0           # Range: 0-10
}

Response:

{
    "personality": "Introvert",  # or "Extrovert"
    "success": true
}

Setup Guide

Create virtual environment:

python -m venv .venv
source .venv/bin/activate  # Linux/Mac

Install dependencies:

pip install -r requirements.txt

Dependencies

Create requirements.txt with:

flask==3.0.0
numpy==1.24.3
pandas==2.1.1
scikit-learn==1.3.0
matplotlib==3.8.0
seaborn==0.13.0

Model Details

Algorithm: Random Forest Classifier

Features: 7 behavioral indicators

Preprocessing: StandardScaler for feature scaling

Performance: Evaluated using accuracy and classification report

Persistence: Models saved using pickle format

Data Analysis

The project includes comprehensive data analysis in main.ipynb:

Feature importance visualization

Correlation analysis

Distribution plots

Personality type distributions

Model performance comparisons

API Implementation

The Flask API (api.py) provides:

RESTful endpoint for predictions

Input validation

Error handling

Model loading and preprocessing

JSON response formatting

Environment

Python 3.x

Linux/Unix environment

Required packages listed in requirements.txt

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Posted Jun 24, 2025

Developed a personality prediction ML model using Random Forest and Flask API.

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