feat(notebooks): Enhance STFT notebook and model selection functionality

- Updated paths in the STFT notebook to reflect new data files.
- Improved plotting aesthetics for combined plots and added grid lines.
- Introduced a 3D spectrogram visualization for better data representation.
- Refactored model training function to include error handling and model export functionality.
- Adjusted model training calls to include export paths for saved models. Closes #90
- Added additional markdown cells for better documentation and clarity in the notebook.

code/src/ml/model_selection.py

@@ -55,3 +55,101 @@ def create_ready_data(
         y = np.array([])
 
     return X, y
+
+
+def train_and_evaluate_model(
+    model, model_name, sensor_label, x_train, y_train, x_test, y_test, export=None
+):
+    """
+    Train a machine learning model, evaluate its performance, and optionally export it.
+
+    This function trains the provided model on the training data, evaluates its
+    performance on test data using accuracy score, and can save the trained model
+    to disk if an export path is provided.
+
+    Parameters
+    ----------
+    model : estimator object
+        The machine learning model to train.
+    model_name : str
+        Name of the model, used for the export filename and in the returned results.
+    sensor_label : str
+        Label identifying which sensor's data the model is being trained on.
+    x_train : array-like or pandas.DataFrame
+        The training input samples.
+    y_train : array-like
+        The target values for training.
+    x_test : array-like or pandas.DataFrame
+        The test input samples.
+    y_test : array-like
+        The target values for testing.
+    export : str, optional
+        Directory path where the trained model should be saved. If None, model won't be saved.
+
+    Returns
+    -------
+    dict
+        Dictionary containing:
+        - 'model': model_name (str)
+        - 'sensor': sensor_label (str)
+        - 'accuracy': accuracy percentage (float)
+
+    Example
+    -------
+    >>> from sklearn.svm import SVC
+    >>> from sklearn.model_selection import train_test_split
+    >>> X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)
+    >>> result = train_and_evaluate_model(
+    ...     SVC(),
+    ...     "SVM",
+    ...     "sensor1",
+    ...     X_train,
+    ...     y_train,
+    ...     X_test,
+    ...     y_test,
+    ...     export="models/sensor1"
+    ... )
+    >>> print(f"Model accuracy: {result['accuracy']:.2f}%")
+    """
+    from sklearn.metrics import accuracy_score
+
+    result = {"model": model_name, "sensor": sensor_label, "success": False}
+
+    try:
+        # Train the model
+        model.fit(x_train, y_train)
+
+        try:
+            y_pred = model.predict(x_test)
+        except Exception as e:
+            result["error"] = f"Prediction error: {str(e)}"
+            return result
+
+        # Calculate accuracy
+        try:
+            accuracy = accuracy_score(y_test, y_pred) * 100
+            result["accuracy"] = accuracy
+        except Exception as e:
+            result["error"] = f"Accuracy calculation error: {str(e)}"
+            return result
+
+        # Export model if requested
+        if export:
+            try:
+                import joblib
+
+                full_path = os.path.join(export, f"{model_name}.joblib")
+                os.makedirs(os.path.dirname(full_path), exist_ok=True)
+                joblib.dump(model, full_path)
+                print(f"Model saved to {full_path}")
+            except Exception as e:
+                print(f"Warning: Failed to export model to {export}: {str(e)}")
+                result["export_error"] = str(e)
+                # Continue despite export error
+
+        result["success"] = True
+        return result
+
+    except Exception as e:
+        result["error"] = f"Training error: {str(e)}"
+        return result