diff --git a/train_model_linux.py b/train_model_linux.py
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index 0000000..26ab402
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+++ b/train_model_linux.py
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+import pandas as pd
+import numpy as np
+import holidays
+import xgboost as xgb
+from sklearn.metrics import mean_absolute_error
+
+print("Start training Model 1.5...")
+
+# --- 1. DATA LADEN ---
+# We gebruiken de CSV die we al hadden
+try:
+    df = pd.read_csv("training_set_2024_2025.csv")
+    df['datum_tijd'] = pd.to_datetime(df['datum_tijd'])
+    df = df.set_index('datum_tijd').sort_index()
+    print(f"Data geladen: {len(df)} rijen.")
+except FileNotFoundError:
+    print("Zorg dat 'training_set_2024_2025.csv' in de map staat.")
+    raise
+
+# --- 2. FEATURE ENGINEERING (v1.5) ---
+print("Feature Engineering (v1.5) gestart...")
+
+# --- 2a. FEESTDAGEN FEATURE ---
+# Maak een lijst van Nederlandse feestdagen voor de relevante jaren
+nl_holidays = holidays.Netherlands(years=[2024, 2025])
+df['is_feestdag'] = df.index.to_series().apply(lambda x: 1 if x in nl_holidays else 0)
+print("Feestdagen feature toegevoegd.")
+
+# --- 2b. TIJD-FEATURES (Basis) ---
+df['uur_van_de_dag'] = df.index.hour
+df['dag_van_de_week'] = df.index.dayofweek # 0=Maandag, 6=Zondag
+df['maand'] = df.index.month
+df['dag_van_het_jaar'] = df.index.dayofyear
+
+# --- 2c. ONE-HOT ENCODING (DE BELANGRIJKSTE FIX) ---
+# Converteer 'dag_van_de_week' en 'uur_van_de_dag' naar losse kolommen
+print("One-Hot Encoding toepassen...")
+df = pd.get_dummies(df, columns=['dag_van_de_week', 'uur_van_de_dag'], 
+                    prefix=['dag', 'uur'])
+
+# --- 2d. Basis Lag/Rolling Features (deze hadden we al) ---
+df['prijs_1u_geleden'] = df['gemiddelde_prijs'].shift(1)
+df['prijs_24u_geleden'] = df['gemiddelde_prijs'].shift(24)
+df['temp_avg_3u'] = df['temperatuur'].rolling(window=3).mean()
+df['prijs_avg_6u'] = df['gemiddelde_prijs'].rolling(window=6).mean()
+
+# --- 2e. Opschonen ---
+# We verliezen nu maar 24 rijen (van de 'prijs_24u_geleden' lag)
+print(f"Rijen vóór opschonen: {len(df)}")
+df_clean = df.dropna()
+print(f"Rijen ná opschonen: {len(df_clean)}")
+
+# Toon ons de nieuwe features
+print("\nVoorbeeld van de nieuwe 'dag' features:")
+print(df_clean.filter(like='dag_').head())
+
+# --- 3. DEFINIEER FEATURES (X) en TARGET (y) ---
+TARGET = 'gemiddelde_prijs'
+# Automatisch alle kolommen als feature gebruiken
+FEATURES = [col for col in df_clean.columns if col not in [TARGET]]
+
+print(f"\nModel wordt getraind met {len(FEATURES)} features.")
+
+X = df_clean[FEATURES]
+y = df_clean[TARGET]
+
+# --- 4. CHRONOLOGISCHE SPLIT ---
+# We splitsen op 1 jan 2025, net als de vorige keer
+SPLIT_DATE = '2025-01-01 00:00:00'
+train_mask = X.index < SPLIT_DATE
+test_mask = X.index >= SPLIT_DATE
+
+X_train, y_train = X[train_mask], y[train_mask]
+X_test, y_test = X[test_mask], y[test_mask]
+
+print(f"Trainingset: {len(X_train)} rijen")
+print(f"Testset: {len(X_test)} rijen")
+
+# --- 5. MODEL TRAINEN ---
+xgb_model_v1_5 = xgb.XGBRegressor(
+    n_estimators=1000,
+    learning_rate=0.01,
+    early_stopping_rounds=50
+)
+
+print("\nModel v1.5 aan het trainen...")
+xgb_model_v1_5.fit(
+    X_train, y_train,
+    eval_set=[(X_test, y_test)],
+    verbose=False # Zet op 100 om voortgang te zien
+)
+print("Training voltooid.")
+
+# --- 6. EVALUATIE ---
+voorspellingen_v1_5 = xgb_model_v1_5.predict(X_test)
+mae_v1_5 = mean_absolute_error(y_test, voorspellingen_v1_5)
+print(f"\nNieuwe Model (v1.5) MAE: {mae_v1_5:.4f}")
+
+# --- 7. MODEL OPSLAAN ---
+MODEL_FILE = 'price_forecast_model_v1_5.json'
+xgb_model_v1_5.save_model(MODEL_FILE)
+print(f"\nModel 1.5 opgeslagen als: {MODEL_FILE}")
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