refactoring: add initial pipeline configuration and model classes

research/models/random_forest_model.py

@@ -0,0 +1,40 @@
+import numpy as np
+import pandas as pd
+from sklearn.base import BaseEstimator
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+
+from research.traditional_model import TraditionalModel
+
+
+class RandomForestModel(TraditionalModel):
+    """Random Forest with engineered features"""
+
+    def build_model(self) -> BaseEstimator:
+
+        params = self.config.model_params
+
+        return RandomForestClassifier(
+            n_estimators=params.get("n_estimators", 100),
+            max_depth=params.get("max_depth", None),
+            random_state=self.config.random_seed,
+        )
+
+    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
+        features = []
+
+        for feature_type in self.config.features:
+            if feature_type.value in X.columns:
+                column = X[feature_type.value]
+
+                # Handle different feature types
+                if feature_type.value in ["name_length", "word_count"]:
+                    # Numerical features
+                    features.append(column.fillna(0).values.reshape(-1, 1))
+                else:
+                    # Categorical features (encode them)
+                    le = LabelEncoder()
+                    encoded = le.fit_transform(column.fillna("unknown").astype(str))
+                    features.append(encoded.reshape(-1, 1))
+
+        return np.hstack(features) if features else np.array([]).reshape(len(X), 0)