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38aee6c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | 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()
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