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])