====== LU02.L02 - ML Programmierung ======

===== Voraussetzung =====

<code python>
pip install pandas scikit-learn joblib
</code>

===== Python-Skript: ml_basics_shop.py =====
<code python>
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import joblib
 
# -----------------------------  
# Daten laden
# -----------------------------
data = pd.read_csv("shop_data.csv")
X = data.drop("buy", axis=1)
y = data["buy"]

# -----------------------------
# Train / Test Split
# -----------------------------
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42)

# -----------------------------
# Modell 1: Logistische Regression
# -----------------------------
log_reg_pipeline = Pipeline([
    ("scaler", StandardScaler()),
    ("model", LogisticRegression())
])

log_reg_pipeline.fit(X_train, y_train)
y_pred_lr = log_reg_pipeline.predict(X_test)
#
print("Logistische Regression")
print("Accuracy:", accuracy_score(y_test, y_pred_lr))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_lr))
print("Classification Report:\n", classification_report(y_test, y_pred_lr))

# -----------------------------
# Modell 2: Decision Tree
# -----------------------------
tree_model = DecisionTreeClassifier(random_state=42)
tree_model.fit(X_train, y_train)
y_pred_tree = tree_model.predict(X_test)

print("\nDecision Tree")
print("Accuracy:", accuracy_score(y_test, y_pred_tree))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred_tree))
print("Classification Report:\n", classification_report(y_test, y_pred_tree))

# -----------------------------
# Bestes Modell speichern
# -----------------------------
joblib.dump(log_reg_pipeline, "best_model.joblib")

# -----------------------------
# Neue Vorhersage
# -----------------------------
new_customer = pd.DataFrame([{
    "age": 32,
    "past_purchases": 5,
    "minutes_on_page": 6.5
}])

loaded_model = joblib.load("best_model.joblib")
prediction = loaded_model.predict(new_customer)

print("\nVorhersage fuer neuen Kunden:", prediction[0])
</code>
  
===== Modellvergleich =====
^ Kriterium ^ Logistische Regression ^ Decision Tree ^
| Interpretierbarkeit | hoch | mittel | 
| Overfitting-Gefahr | gering | hoch | 
| Skalierung | nötig	ja | nein | 
| Didaktisch | sinnvoll	sehr | ja | 

===== Fazit =====
  * Bei kleinen, sauberen Datensätzen ist die Logistische Regression meist stabiler. 
  * Decision Trees sind anschaulich, aber übermotiviert – sie merken sich gern alles bzw. "lernen einen Fall auswendig".

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[[https://creativecommons.org/licenses/by-nc-sa/4.0/|{{https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png}}]] Volkan Demir