training script added

train.py

@@ -0,0 +1,80 @@
+import pandas as pd
+from sklearn.preprocessing import PowerTransformer
+from sklearn.cluster import KMeans
+import joblib
+import os
+from tqdm import tqdm
+import time
+
+DATA_PATH = "wallet_dataset.csv"
+PREPROCESSOR_PATH = "wallet_power_transformer.pkl"
+MODEL_PATH = "kmeans_model.pkl"
+OUTPUT_LABELED_DATA_PATH = "wallet_dataset_labeled.csv"
+N_CLUSTERS = 4
+RANDOM_STATE = 42
+
+FEATURES = [
+    'tx_count', 'active_days', 'avg_tx_per_day', 'total_gas_spent',
+    'total_nft_buys', 'total_nft_sells', 'total_nft_volume_usd',
+    'unique_nfts_owned', 'dex_trades', 'avg_trade_size_usd',
+    'total_traded_usd', 'erc20_receive_usd', 'erc20_send_usd',
+    'native_balance_delta'
+]
+
+PERSONA_MAPPING = {
+    0: "High-Frequency Bots / Automated Traders",
+    1: "High-Value NFT & Crypto Traders (Degen Whales)",
+    2: "Active Retail Users / Everyday Traders",
+    3: "Ultra-Whales / Institutional & Exchange Wallets"
+}
+
+def train_model():
+    print("Starting model training process...")
+    
+    steps = [
+        "Load Data",
+        "Preprocessing",
+        "Train KMeans",
+        "Apply Mapping",
+        "Save Data"
+    ]
+    
+    with tqdm(total=len(steps), desc="Training Pipeline") as pbar:
+        
+        pbar.set_description(f"Step: {steps[0]}")
+        try:
+            df = pd.read_csv(DATA_PATH)
+        except FileNotFoundError:
+            print(f"Error: {DATA_PATH} not found. Please ensure the raw data is available.")
+            return
+        pbar.update(1)
+
+        X = df[FEATURES]
+
+        pbar.set_description(f"Step: {steps[1]}")
+        preprocessor = PowerTransformer(method='yeo-johnson')
+        X_transformed = preprocessor.fit_transform(X)
+
+        joblib.dump(preprocessor, PREPROCESSOR_PATH)
+        pbar.update(1)
+
+        pbar.set_description(f"Step: {steps[2]}")
+        kmeans = KMeans(n_clusters=N_CLUSTERS, random_state=RANDOM_STATE, n_init='auto')
+        df['Cluster_labels'] = kmeans.fit_predict(X_transformed)
+
+        joblib.dump(kmeans, MODEL_PATH)
+        pbar.update(1)
+
+        pbar.set_description(f"Step: {steps[3]}")
+
+        df['Persona'] = df['Cluster_labels'].map(PERSONA_MAPPING)
+        pbar.update(1)
+
+        pbar.set_description(f"Step: {steps[4]}")
+        df.to_csv(OUTPUT_LABELED_DATA_PATH, index=False)
+        pbar.update(1)
+        
+    print("\nModel training and data labeling complete.")
+
+if __name__ == "__main__":
+    train_model()