Create train.py

train.py

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+import pandas as pd
+import joblib
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+
+# Load data
+df = pd.read_csv("data/transactions.csv")
+
+# Feature engineering
+df["hour"] = pd.to_datetime(df["time"], format="%H:%M").dt.hour
+df.drop(columns=["check_id", "time"], inplace=True)
+
+# Encode categorical variables
+categorical_cols = ["employee_id", "terminal_id"]
+encoders = {}
+
+for col in categorical_cols:
+    enc = LabelEncoder()
+    df[col] = enc.fit_transform(df[col])
+    encoders[col] = enc
+
+# Features and target
+X = df.drop(columns=["suspicious"])
+y = df["suspicious"]
+
+# Train/test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Save model and encoders
+joblib.dump(model, "model/model.pkl")
+joblib.dump(encoders, "model/encoders.pkl")
+
+print("Training complete. Model saved.")