Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+- roc_auc
+pipeline_tag: tabular-classification
+tags:
+- classification
+- psychology
+---
+
+# 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
+
+```python
+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.