import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Load and prepare data
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']
# Train model
X_train, X_test = train_test_split(X, y)
model = RandomForestClassifier()
model.fit(X_train, y_train)
SELECT
customer_id,
SUM(amount) as total_spent,
COUNT(*) as orders
FROM transactions
GROUP BY customer_id
HAVING total_spent > 1000
ORDER BY total_spent DESC;
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