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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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- roc_auc |
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pipeline_tag: tabular-classification |
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tags: |
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- classification |
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- psychology |
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--- |
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# Model Card for Infinitode/BPPM-OPEN-ARC |
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Repository: https://github.com/Infinitode/OPEN-ARC/ |
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## Model Description |
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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. |
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**Architecture**: |
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- **XGBClassifer**: `n_estimators=200`, `learning_rate=0.1`, `max_depth=4`, `eval_metric="logloss"`, `use_label_encoder=False`, `random_state=42`. |
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- **Framework**: XGBoost |
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- **Training Setup**: Trained using selected params. |
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## Uses |
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- Identifying a person's basic personality type through a series of personal questions. |
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- Enhancing knowledge and research in psychology and human behavior. |
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## Limitations |
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- Might lead to inaccurate evaluations of personality types because of exceptions and inconsistencies. |
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## Training Data |
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- Dataset: Personality Dataset (introvert or Extrovert) dataset from Kaggle. |
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- Source URL: https://www.kaggle.com/datasets/hardikchhipa28/personality-dataset-introvert-or-extrovert |
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- Content: Personality-determining features such as hours spent alone per day, etc. |
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- Size: The size is unknown as the dataset is no longer publicly available. |
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- Preprocessing: Preprocessing involved techniques such as number and category imputation, one-hot encoding, and label encoding. |
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## Training Procedure |
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- Metrics: accuracy, precision, recall, F1, ROC AUC |
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- Train/Testing Split: 75% train, 25% testing. |
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## Evaluation Results |
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| Metric | Value | |
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| ------ | ----- | |
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| Testing Accuracy | 92.0% | |
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| Testing Weighted Average Precision | 92% | |
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| Testing Weighted Average Recall | 92% | |
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| Testing Weighted Average F1 | 92% | |
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| Testing ROC AUC | 95.5% | |
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## How to Use |
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```python |
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import joblib |
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import pandas as pd |
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import numpy as np |
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# Load artifacts |
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art = joblib.load("personality_artifacts.pkl") |
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model = art["model"] |
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num_imputer = art["num_imputer"] |
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cat_imputer = art["cat_imputer"] |
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ohe = art["ohe"] |
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le = art["label_encoder"] |
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num_cols = art["num_cols"] |
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cat_cols = art["cat_cols"] |
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def ask(prompt, cast=str, options=None): |
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"""Tiny helper to get clean input.""" |
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while True: |
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try: |
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val = cast(input(prompt)) |
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if options and val not in options: |
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raise ValueError(f"Must be one of {options}") |
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return val |
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except Exception as e: |
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print(f"Error: {e}. Try again.\n") |
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def predict_personality(): |
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print("\n🎭 Introvert vs Extrovert Predictor") |
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print("Answer a few quick questions:\n") |
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# Gather answers |
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answers = { |
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"Time_spent_Alone": ask("Hours spent alone per day (0‑24): ", float), |
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"Stage_fear": ask("Stage fear? (Yes/No): ", str.title, ["Yes", "No"]), |
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"Social_event_attendance":ask("Social events per week (0‑10): ", int), |
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"Going_outside": ask("Trips outside per day (0‑10): ", int), |
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"Drained_after_socializing": ask("Feel drained after socializing? (Yes/No): ", |
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str.title, ["Yes", "No"]), |
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"Friends_circle_size": ask("Number of close friends (0‑30): ", int), |
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"Post_frequency": ask("Social‑media posts per week (0‑30): ", int), |
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} |
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# Build one‑row DataFrame in correct column order |
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row = pd.DataFrame([answers])[num_cols + cat_cols] |
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# --- Re‑run exact preprocessing ------------------------------------------------- |
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X_num = num_imputer.transform(row[num_cols]) |
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X_cat = cat_imputer.transform(row[cat_cols]) |
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X_cat_enc = ohe.transform(X_cat) |
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X_ready = np.hstack([X_num, X_cat_enc]) |
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# --- Predict -------------------------------------------------------------------- |
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proba = model.predict_proba(X_ready)[0] |
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idx = proba.argmax() |
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pred_label = le.inverse_transform([idx])[0] |
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confidence = proba[idx] |
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print(f"\n🔮 You are likely an **{pred_label}** (confidence {confidence:.0%}).") |
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predict_personality() |
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``` |
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## Contact |
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For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact. |
