LU02.L02 - ML Programmierung

pip install pandas scikit-learn joblib

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])

Fazit: Bei kleinen, sauberen Datensaetzen ist die Logistische Regression meist stabiler. Decision Trees sind anschaulich, aber uebermotiviert – sie merken sich gern alles.