From 812792b1fba126cf9b31357643757c05c4df0c4c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adu=20Alex=20G=C3=B6llnitz?=
 <adualex.goellnitz@stud.fhgr.ch>
Date: Wed, 19 Nov 2025 16:46:01 +0100
Subject: [PATCH] svm_modell.py aktualisiert

---
 svm_modell.py | 42 ++++++++++++++++++------------------------
 1 file changed, 18 insertions(+), 24 deletions(-)

diff --git a/svm_modell.py b/svm_modell.py
index 53ab0f7..3e8291d 100644
--- a/svm_modell.py
+++ b/svm_modell.py
@@ -15,38 +15,32 @@ import warnings
 warnings.filterwarnings('ignore')
 
 def load_and_preprocess_data():
-  
-    # CSV-Dateien laden
     activities = pd.read_csv("Activities_rohdaten.csv", sep=None, engine="python")
-    
-    # Daten bereinigen und konsolidieren
     df = activities.copy()
-    
-    # Spaltennamen bereinigen
     df.columns = df.columns.str.strip()
-    
-    # Sportarten für binäre Klassifikation definieren
-    # Wir klassifizieren zwischen 'Ausdauersport' und 'Kraftsport'
-    endurance_sports = ['Laufen', 'Rennradfahren', 'Schwimmen', 'Radfahren']
-    strength_sports = ['Krafttraining']
-    
-    # Binäre Zielvariable erstellen
-    df['sport_category'] = df['Aktivitätstyp'].apply(lambda x: 1 
-                                                     if x in endurance_sports 
-                                                     else (0 if x in strength_sports else -1))
-    
-    # Nur gültige Kategorien behalten
+
+    def classify_activity(x):
+        x = str(x).lower()
+        if 'cardio' in x or 'gehen' in x or 'multisport' in x:
+            return -1
+        if 'kraft' in x:
+            return 0
+        if ('rad' in x or 'bike' in x or 'cycling' in x or 'velo' in x or 'schwimm' in x or 'laufen' in x or 'run' in x):
+            return 1
+        return -1
+
+    df['sport_category'] = df['Aktivitätstyp'].apply(classify_activity)
+
     df = df[df['sport_category'] != -1]
-    
-    # Features auswählen:
+
     numeric_features = ['Distanz', 'Kalorien', 'Ø Herzfrequenz', 'Maximale Herzfrequenz', 'Aerober TE', 'Training Stress Score®']
-    
-    # Nur verfügbare numerische Features auswählen
+
     available_features = [col for col in numeric_features if col in df.columns]
-    
+
     return df, available_features
 
-def feature_engineering(df, features):  # Führt Feature Engineering durch
+
+def feature_engineering(df, features): 
     
     # Kopie erstellen
     df_processed = df.copy()