Model Card for Infinitode/BPPM-OPEN-ARC

Repository: https://github.com/Infinitode/OPEN-ARC/

Model Description

OPEN-ARC-BPP is a simple XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to determine a person's basic personality type (introvert/extrovert) based on several personal questions.

Architecture:

• XGBClassifer: n_estimators=200, learning_rate=0.1, max_depth=4, eval_metric="logloss", use_label_encoder=False, random_state=42.
• Framework: XGBoost
• Training Setup: Trained using selected params.

Uses

• Identifying a person's basic personality type through a series of personal questions.
• Enhancing knowledge and research in psychology and human behavior.

Limitations

• Might lead to inaccurate evaluations of personality types because of exceptions and inconsistencies.

Training Data

• Dataset: Personality Dataset (introvert or Extrovert) dataset from Kaggle.
• Source URL: https://www.kaggle.com/datasets/hardikchhipa28/personality-dataset-introvert-or-extrovert
• Content: Personality-determining features such as hours spent alone per day, etc.
• Size: The size is unknown as the dataset is no longer publicly available.
• Preprocessing: Preprocessing involved techniques such as number and category imputation, one-hot encoding, and label encoding.

Training Procedure

• Metrics: accuracy, precision, recall, F1, ROC AUC
• Train/Testing Split: 75% train, 25% testing.

Evaluation Results

Metric	Value
Testing Accuracy	92.0%
Testing Weighted Average Precision	92%
Testing Weighted Average Recall	92%
Testing Weighted Average F1	92%
Testing ROC AUC	95.5%

How to Use

import joblib
import pandas as pd
import numpy as np

# Load artifacts
art = joblib.load("personality_artifacts.pkl")
model          = art["model"]
num_imputer    = art["num_imputer"]
cat_imputer    = art["cat_imputer"]
ohe            = art["ohe"]
le             = art["label_encoder"]
num_cols       = art["num_cols"]
cat_cols       = art["cat_cols"]

def ask(prompt, cast=str, options=None):
    """Tiny helper to get clean input."""
    while True:
        try:
            val = cast(input(prompt))
            if options and val not in options:
                raise ValueError(f"Must be one of {options}")
            return val
        except Exception as e:
            print(f"Error: {e}. Try again.\n")

def predict_personality():
    print("\n🎭  Introvert vs Extrovert Predictor")
    print("Answer a few quick questions:\n")

    # Gather answers
    answers = {
        "Time_spent_Alone":       ask("Hours spent alone per day (0‑24): ", float),
        "Stage_fear":             ask("Stage fear? (Yes/No): ", str.title, ["Yes", "No"]),
        "Social_event_attendance":ask("Social events per week (0‑10): ", int),
        "Going_outside":          ask("Trips outside per day (0‑10): ", int),
        "Drained_after_socializing": ask("Feel drained after socializing? (Yes/No): ",
                                         str.title, ["Yes", "No"]),
        "Friends_circle_size":    ask("Number of close friends (0‑30): ", int),
        "Post_frequency":         ask("Social‑media posts per week (0‑30): ", int),
    }

    # Build one‑row DataFrame in correct column order
    row = pd.DataFrame([answers])[num_cols + cat_cols]

    # --- Re‑run exact preprocessing -------------------------------------------------
    X_num = num_imputer.transform(row[num_cols])
    X_cat = cat_imputer.transform(row[cat_cols])
    X_cat_enc = ohe.transform(X_cat)
    X_ready = np.hstack([X_num, X_cat_enc])

    # --- Predict --------------------------------------------------------------------
    proba = model.predict_proba(X_ready)[0]
    idx = proba.argmax()
    pred_label = le.inverse_transform([idx])[0]
    confidence = proba[idx]

    print(f"\n🔮 You are likely an **{pred_label}** (confidence {confidence:.0%}).")

predict_personality()

Contact

For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact.