diff --git a/AI Credit Card scam detector b/AI Credit Card scam detector
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+++ b/AI Credit Card scam detector	
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+"""
+AI Credit Card Scam Detector
+
+This script trains a machine learning model to detect credit card scams,
+stores transactions and predictions in a local SQLite database,
+and provides an interface to add new transactions for instant prediction.
+
+Author: Jlevy1106
+Date: 2025-08-04
+"""
+
+import sqlite3
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from sklearn.preprocessing import StandardScaler
+
+# =========================
+# === SAMPLE DATA BLOCK ===
+# =========================
+# You can replace this block with your own data or expand it.
+data = {
+    'amount': [100, 1500, 20, 5000, 77, 450, 3300, 15, 120, 2700],
+    'location': [1, 0, 1, 0, 1, 1, 0, 1, 1, 0],  # 1 = local, 0 = foreign
+    'is_night': [0, 1, 0, 1, 0, 1, 1, 0, 0, 1],  # 1 = night, 0 = day
+    'card_age_months': [12, 2, 40, 1, 20, 6, 3, 60, 24, 4],
+    'is_online': [1, 1, 0, 1, 0, 1, 1, 0, 0, 1],
+    'scam': [0, 1, 0, 1, 0, 0, 1, 0, 0, 1]
+}
+df = pd.DataFrame(data)
+
+# ===========================
+# === FEATURE EXTRACTION ====
+# ===========================
+X = df.drop('scam', axis=1)
+y = df['scam']
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# ===========================
+# === MODEL TRAINING BLOCK ==
+# ===========================
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# ===========================
+# === MODEL EVALUATION ======
+# ===========================
+print("=== Model Evaluation ===")
+predictions = model.predict(X_test)
+print(classification_report(y_test, predictions))
+
+# ===========================
+# === SQLITE DATABASE SETUP =
+# ===========================
+conn = sqlite3.connect('credit_card_scam_ai.db')
+c = conn.cursor()
+c.execute('''
+CREATE TABLE IF NOT EXISTS transactions (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    amount REAL,
+    location INTEGER,
+    is_night INTEGER,
+    card_age_months INTEGER,
+    is_online INTEGER,
+    predicted_scam INTEGER
+)
+''')
+conn.commit()
+
+def add_transaction(amount, location, is_night, card_age_months, is_online):
+    """
+    Adds a transaction to the database and predicts if it is a scam.
+    Parameters:
+        amount (float): Transaction amount
+        location (int): 1 for local, 0 for foreign
+        is_night (int): 1 for night, 0 for day
+        card_age_months (int): Age of card in months
+        is_online (int): 1 for online, 0 for offline
+    """
+    features = np.array([[amount, location, is_night, card_age_months, is_online]])
+    features_scaled = scaler.transform(features)
+    predicted_scam = int(model.predict(features_scaled)[0])
+    c.execute('''
+        INSERT INTO transactions (amount, location, is_night, card_age_months, is_online, predicted_scam)
+        VALUES (?, ?, ?, ?, ?, ?)
+    ''', (amount, location, is_night, card_age_months, is_online, predicted_scam))
+    conn.commit()
+    print(f"Transaction added. Scam prediction: {'SCAM' if predicted_scam else 'NOT SCAM'}")
+
+# ===========================
+# === SAMPLE USAGE ==========
+# ===========================
+if __name__ == "__main__":
+    print("\n=== Adding Example Transaction ===")
+    add_transaction(2000, 0, 1, 2, 1)  # Foreign, night, new card, online
+
+    print("\n=== All Transactions in Database ===")
+    for row in c.execute('SELECT * FROM transactions'):
+        print(row)
+
+    conn.close()