diff --git a/SL/aufgaben/workshop4/README.md b/SL/aufgaben/workshop4/README.md
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+# Workshop 4 — kNN Hyperparametersuche
+
+Hyperparameter-Tuning für `KNeighborsClassifier` auf `bank_data_prep.csv`
+(Klassifikation: Hat der Kunde abgeschlossen, ja/nein?), inklusive Vergleich
+mit/ohne Standardisierung.
+
+- **Input:** `data/bank_data_prep.csv` (Output aus Workshop 3 / Bank-Implementation)
+- **Pipeline:** `src/hyperparametersearch.py`
+
+## Aufgabenstellung
+
+Drei Teile gemäss Folie:
+
+1. Features von Trainings- und Testdaten mit `StandardScaler` standardisieren.
+2. Beste Parameter für `KNeighborsClassifier` finden:
+   - `n_neighbors` ∈ {1..10}
+   - `p` ∈ {1, 2, 3}
+3. Ergebnisse mit vs. ohne Standardisieren vergleichen.
+
+## Konzepte kurz erklärt
+
+### Was ist `p`? (Minkowski-Distanz)
+
+`p` steuert die Distanzmetrik, mit der kNN „Nachbarschaft" misst:
+
+| `p` | Distanz | Formel | Verhalten |
+|----:|---------|--------|-----------|
+| 1 | Manhattan | Σ \|x_i − y_i\| | Strassen-Netz-Distanz, robuster gegen Ausreisser in Einzeldimensionen |
+| 2 | Euklidisch | √Σ(x_i − y_i)² | „Luftlinie", sklearn-Default |
+| 3+ | Höhere Minkowski | (Σ\|x_i − y_i\|^p)^(1/p) | Gewichtet grosse Einzelunterschiede zunehmend stärker |
+
+Bei diesem Datensatz gewinnt `p=1` (Manhattan) konsistent über alle Varianten —
+plausibel, weil viele Dummy-Features (0/1) drin sind und Manhattan diese
+gleichmässiger gewichtet als Euklidisch.
+
+### Warum Standardisierung bei kNN nicht optional ist
+
+kNN ist **distanzbasiert**. Ohne Skalierung dominiert die Variable mit dem
+grössten Wertebereich die Distanzberechnung — Dummy-Features (0/1) werden
+faktisch ignoriert, weil ihr Beitrag zur Distanz verschwindet gegen z.B. `age`
+(17–98) oder `duration` (Sekunden). `StandardScaler` zentriert jede Spalte auf
+Mittelwert 0 / Standardabweichung 1 und stellt damit alle Features gleichwertig.
+
+### Leakage vermeiden: `fit` nur auf Train
+
+```python
+scaler.fit(X_train)                    # mean/std NUR aus Train lernen
+X_train = scaler.transform(X_train)
+X_test  = scaler.transform(X_test)     # mit Train-Statistiken transformieren
+```
+
+Würde der Scaler auf `X_test` (oder dem gesamten Datensatz) gefittet, flössen
+Test-Statistiken in die Vorverarbeitung ein → Leakage. Das Test-Set soll so
+behandelt werden, als sähe man es zum ersten Mal — denn so wird es in
+Produktion (neue Daten) auch sein.
+
+### Hyperparametersuche: manuell vs. GridSearchCV
+
+Folien-Methode: Doppelschleife über `n_neighbors × p`, Score auf Test-Set
+genommen, beste Kombi gewählt. Funktioniert, hat aber zwei Schwächen:
+
+1. **Optimistic Bias:** Tunt gegen das Test-Set. Die gewählte Kombi ist die,
+   die zufällig auf *genau diesem einen* Split am besten lief.
+2. **Einziger Split:** Keine Robustheit gegen ungünstige Train/Test-Aufteilungen.
+
+`GridSearchCV` löst beides: Cross-Validation auf den Trainingsdaten (k-Fold,
+hier 5), Test-Set wird **erst am Ende einmal** angefasst.
+
+## Drei Varianten
+
+| Variante | Skalierung | Hyperparameter-Suche | Bestes k, p | Accuracy |
+|----------|:----------:|----------------------|:-----------:|:--------:|
+| A | nein | manuell (Doppelschleife) | k=9, p=1 | 76,6 % (Test) |
+| B | ja | manuell (Doppelschleife) | k=9, p=1 | 80,4 % (Test) |
+| C | ja | GridSearchCV (5-fold) | k=9, p=1 | 81,1 % (CV) / 80,4 % (Test) |
+
+**Lesart:**
+- A → B: +3,7 pp durch Skalierung. Belegt Aufgabenteil 3.
+- B → C: Accuracy bleibt gleich, aber der **CV-Score** ist der ehrliche
+  Schätzer für die Generalisierung. Der Test-Score von B war leicht
+  optimistisch verzerrt (gegen Test getunt); C bestätigt ihn unabhängig.
+
+## Klassifikationsqualität (Variante C)
+
+```
+[[1461  274]
+ [ 371 1181]]
+
+              precision    recall  f1-score   support
+          no       0.80      0.84      0.82      1735
+         yes       0.81      0.76      0.79      1552
+    accuracy                           0.80      3287
+```
+
+Beide Klassen werden ausgewogen vorhergesagt (F1 ≈ 0,80 für beide). Das Modell
+ist leicht konservativer mit `yes`-Vorhersagen (Recall 0,76 vs. 0,84 für `no`).
+
+## Befund zum Datensatz
+
+`bank_data_prep.csv` ist mit 53/47 (`no`/`yes`) annähernd balanciert. Das
+Original-Bank-Set hat eine ~88/12-Verteilung — der gelieferte Datensatz wurde
+also vorab **resampled** (vermutlich SMOTE oder Random Over/Undersampling via
+`imbalanced-learn`). Konsequenz: Accuracy ist hier eine vertretbare Metrik;
+wäre der Datensatz im Original-Verhältnis, wären 80 % Accuracy *schlechter*
+als die triviale „immer no"-Baseline (88 %), und man müsste mit Precision/
+Recall/F1 arbeiten.
+
+## Projektstruktur
+
+```
+workshop4
+├── data/
+│   └── bank_data_prep.csv
+├── src/
+│   └── hyperparametersearch.py
+├── devenv.nix
+└── README.md
+```
+
+## Ausführen
+
+```sh
+python src/hyperparametersearch.py
+```
+
+## Implementierungs-Notizen
+
+Bewusste Entscheidungen, die über die Folien-Vorlage hinausgehen:
+
+- **`fit` nur auf `X_train`** beim `StandardScaler` (Folie macht es so, viele
+  Anleitungen nicht — daher hier explizit dokumentiert).
+- **`GridSearchCV` zusätzlich zur Folien-Doppelschleife**, um Optimistic Bias
+  sichtbar zu machen und einen leakage-freien CV-Score zu erhalten.
+- **`classification_report` + Confusion Matrix** über die Folien-Anforderungen
+  hinaus, weil reine Accuracy bei Klassifikation nie das ganze Bild zeigt.
+
+## Offene Punkte für ein echtes Projekt
+
+- **`Pipeline` für Scaler + kNN.** Ein `Pipeline([('scaler', ...), ('knn', ...)])`
+  macht aus den zwei Schritten ein Objekt — `predict()` skaliert dann
+  automatisch mit dem trainierten Scaler. Verhindert strukturell die
+  „Modell trainiert auf skaliert, Vorhersage auf unskaliert"-Klasse von Bugs.
+  Auch GridSearchCV erhält dann die Pipeline statt nur den Classifier, was die
+  Skalierung in *jedem* CV-Fold korrekt nur auf dem Fold-Train fittet.
+- **Resampling-Schritt im Datenpfad sichtbar machen.** Der Resampling-Eingriff
+  passierte ausserhalb dieser Pipeline; in einem echten Projekt gehört er
+  dokumentiert oder selbst (re-)produziert, sonst sind Ergebnisse nicht
+  reproduzierbar.
+- **Mehr Metriken bei der CV.** `GridSearchCV(scoring=...)` kann auch auf F1,
+  ROC-AUC u.a. optimieren — sinnvoller als Accuracy, sobald Klassen
+  unbalanciert sind.
diff --git a/SL/aufgaben/workshop4/data/bank_data_prep.csv b/SL/aufgaben/workshop4/data/bank_data_prep.csv
new file mode 100644
index 0000000..058b611
--- /dev/null
+++ b/SL/aufgaben/workshop4/data/bank_data_prep.csv
@@ -0,0 +1,9861 @@
+age,education,housing,contact_cellular,month,day_of_week,duration,campaign,pdays,previous,emp_var_rate,cons_price_idx,cons_conf_idx,euribor3m,nr_employed,y,job_blue_collar,job_entrepreneur,job_housemaid,job_management,job_retired,job_self_employed,job_services,job_student,job_technician,job_unemployed,marital_married,marital_single,loan_unknown,loan_yes
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diff --git a/SL/aufgaben/workshop4/devenv.lock b/SL/aufgaben/workshop4/devenv.lock
new file mode 100644
index 0000000..ca8e6d8
--- /dev/null
+++ b/SL/aufgaben/workshop4/devenv.lock
@@ -0,0 +1,103 @@
+{
+  "nodes": {
+    "devenv": {
+      "locked": {
+        "dir": "src/modules",
+        "lastModified": 1779303056,
+        "narHash": "sha256-+DJSNTtrdUb5yelcKp8fa5aITlg050701WCOJt0oMtI=",
+        "owner": "cachix",
+        "repo": "devenv",
+        "rev": "0d0be23517b92cbcedd95a0dbb6f735deae9b38c",
+        "type": "github"
+      },
+      "original": {
+        "dir": "src/modules",
+        "owner": "cachix",
+        "repo": "devenv",
+        "type": "github"
+      }
+    },
+    "flake-compat": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1767039857,
+        "narHash": "sha256-vNpUSpF5Nuw8xvDLj2KCwwksIbjua2LZCqhV1LNRDns=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "5edf11c44bc78a0d334f6334cdaf7d60d732daab",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "inputs": {
+        "nixpkgs-src": "nixpkgs-src"
+      },
+      "locked": {
+        "lastModified": 1778507786,
+        "narHash": "sha256-HzSQCKMsMr8r55LwM1JuzIOB+8bzk0FEv6sItKvsfoY=",
+        "owner": "cachix",
+        "repo": "devenv-nixpkgs",
+        "rev": "8f24a228a782e24576b155d1e39f0d914b380691",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "ref": "rolling",
+        "repo": "devenv-nixpkgs",
+        "type": "github"
+      }
+    },
+    "nixpkgs-python": {
+      "inputs": {
+        "flake-compat": "flake-compat",
+        "nixpkgs": [
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1779117433,
+        "narHash": "sha256-iKhNJH1ABTrPvDF6Sd1U+GCVYSh8Xn88ee10ko7PvvE=",
+        "owner": "cachix",
+        "repo": "nixpkgs-python",
+        "rev": "a0f88fb785debcb0a201d0ce311a2e3d829e4a1b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "cachix",
+        "repo": "nixpkgs-python",
+        "type": "github"
+      }
+    },
+    "nixpkgs-src": {
+      "flake": false,
+      "locked": {
+        "lastModified": 1778274207,
+        "narHash": "sha256-I4puXmX1iovcCHZlRmztO3vW0mAbbRvq4F8wgIMQ1MM=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "b3da656039dc7a6240f27b2ef8cc6a3ef3bccae7",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixpkgs-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "devenv": "devenv",
+        "nixpkgs": "nixpkgs",
+        "nixpkgs-python": "nixpkgs-python"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
\ No newline at end of file
diff --git a/SL/aufgaben/workshop4/devenv.nix b/SL/aufgaben/workshop4/devenv.nix
new file mode 100644
index 0000000..d6838db
--- /dev/null
+++ b/SL/aufgaben/workshop4/devenv.nix
@@ -0,0 +1,29 @@
+{ pkgs, lib, config, ... }:
+
+{
+  languages.python = {
+    enable = true;
+    version = "3.12";
+    venv.enable = true;
+    venv.requirements = ''
+      imbalanced-learn
+      ipython
+      jupyter
+      jupyterlab
+      matplotlib
+      numpy
+      pandas
+      scikit-learn
+      seaborn
+      setuptools<81
+      statsmodels
+      ydata-profiling
+    '';
+  };
+
+  packages = [
+    pkgs.graphviz
+    pkgs.zsh
+    pkgs.zlib
+  ];
+}
diff --git a/SL/aufgaben/workshop4/devenv.yaml b/SL/aufgaben/workshop4/devenv.yaml
new file mode 100644
index 0000000..f6e91f8
--- /dev/null
+++ b/SL/aufgaben/workshop4/devenv.yaml
@@ -0,0 +1,6 @@
+inputs:
+  nixpkgs-python:
+    url: github:cachix/nixpkgs-python
+    inputs:
+      nixpkgs:
+        follows: nixpkgs
diff --git a/SL/aufgaben/workshop4/src/hyperparametersearch.py b/SL/aufgaben/workshop4/src/hyperparametersearch.py
new file mode 100644
index 0000000..e0d3757
--- /dev/null
+++ b/SL/aufgaben/workshop4/src/hyperparametersearch.py
@@ -0,0 +1,97 @@
+"""Workshop 4: kNN Hyperparametersuche auf bank_data_prep.csv.
+
+Standardisiert die Features, sucht die besten Werte für n_neighbors und p
+(Minkowski-Distanz), und vergleicht die Accuracy mit vs. ohne Standardisieren.
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import confusion_matrix, classification_report
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import StandardScaler
+
+RAW = "data/bank_data_prep.csv"
+SEED = 1234
+
+
+def load_split(path: str = RAW):
+    """Schritt 1-3 der Folien: laden, X/y-Split, train/test-Split."""
+    df = pd.read_csv(path)
+
+    X = df.drop("y", axis=1)
+    y = df["y"]
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, train_size=2 / 3, random_state=SEED
+    )
+
+    return X_train, X_test, y_train, y_test
+
+
+def scale(X_train, X_test):
+    """Standardisieren: Scaler nur auf Train fitten, auf beide anwenden."""
+    scaler = StandardScaler().set_output(transform="pandas")
+    scaler.fit(X_train)
+    return scaler.transform(X_train), scaler.transform(X_test)
+
+
+def search_manual(X_train, X_test, y_train, y_test):
+    """Folien-Methode: Grid ueber n_neighbors x p, Score auf Test."""
+    results = []
+    for k in range(1, 11):
+        for p in (1, 2, 3):
+            model = KNeighborsClassifier(n_neighbors=k, p=p)
+            model.fit(X_train, y_train)
+            acc = model.score(X_test, y_test)
+            results.append((k, p, acc))
+    best = max(results, key=lambda r: r[2])
+    print(f"Bestes Ergebnis: k={best[0]}, p={best[1]}, acc={best[2]:.4f}")
+    return results, best
+
+
+def search_grid(X_train, y_train):
+    """Hyperparametersuche per GridSearchCV (CV auf Train, kein Test-Leakage)."""
+    param_grid = {
+        "n_neighbors": range(1, 11),
+        "p": (1, 2, 3),
+    }
+    grid = GridSearchCV(
+        KNeighborsClassifier(),
+        param_grid,
+        cv=5,              # 5-fache Cross-Validation
+        scoring="accuracy",
+    )
+    grid.fit(X_train, y_train)   # NUR Train — Test wird nicht angefasst
+    print(f"Beste Params: {grid.best_params_}, CV-Score: {grid.best_score_:.4f}")
+    return grid
+
+
+if __name__ == "__main__":
+    X_train, X_test, y_train, y_test = load_split()
+
+    # --- Variante A: OHNE Standardisieren ---
+    print("=== Ohne Standardisieren ===")
+    results_raw, best_raw = search_manual(X_train, X_test, y_train, y_test)
+
+    # --- Variante B: MIT Standardisieren ---
+    print("=== Mit Standardisieren ===")
+    X_train_sc, X_test_sc = scale(X_train, X_test)  # Features skalieren
+    results_sc, best_sc = search_manual(X_train_sc, X_test_sc, y_train, y_test)
+
+    # --- Variante C: GridScearch
+    grid = search_grid(X_train_sc, y_train)              # skalierte Train-Daten rein
+    final_acc = grid.score(X_test_sc, y_test)            # skalierte Test-Daten messen
+    k_grid = grid.best_params_['n_neighbors']
+    p_grid = grid.best_params_['p']
+    cv_grid = grid.best_score_
+
+    # --- Vergleich ---
+    print(f"\nOhne Skalierung: k={best_raw[0]}, p={best_raw[1]}, acc={best_raw[2]:.4f}")
+    print(f"Mit Skalierung: k={best_sc[0]}, p={best_sc[1]}, acc={best_sc[2]:.4f}")
+    print(f"Mit GridSearch: k={k_grid}, p={p_grid}, CV-acc={cv_grid:.4f}, test-acc={final_acc:.4f}")
+
+    y_pred = grid.predict(X_test_sc)                     # weiterhin skaliert
+    print(confusion_matrix(y_test, y_pred))
+    print(classification_report(y_test, y_pred))