diff --git a/synthpop/processor/data_processor.py b/synthpop/processor/data_processor.py
index 2116cb6..1d86b7d 100644
--- a/synthpop/processor/data_processor.py
+++ b/synthpop/processor/data_processor.py
@@ -55,15 +55,24 @@ def _preprocess(self, data: pd.DataFrame) -> pd.DataFrame:
 
         for col, dtype in self.metadata.items():
             if dtype == "categorical":
-                # Use Label Encoding for small categories, OneHot for larger
-                encoder = LabelEncoder() if len(data[col].unique()) < 10 else OneHotEncoder(sparse=False, drop="first")
+                # Choose encoder based on cardinality
+                n_unique = len(data[col].unique())
+                if n_unique < 10:
+                    encoder = LabelEncoder()
+                elif n_unique < 50:
+                    encoder = OneHotEncoder(sparse=False, drop="first")
+                else:
+                    # Frequency encoding
+                    value_counts = data[col].value_counts(normalize=True)
+                    encoder = {'type': 'frequency', 'mapping': value_counts.to_dict()}
+                
                 transformed_data = self._encode_categorical(data[col], encoder)
                 self.encoders[col] = encoder
                 data.drop(columns=[col], inplace=True)
                 data = pd.concat([data, transformed_data], axis=1)
 
             elif dtype == "numerical":
-                scaler = StandardScaler(with_mean= False, with_std= False)
+                scaler = StandardScaler(with_mean=False, with_std=False)
                 data[col] = scaler.fit_transform(data[[col]])
                 self.scalers[col] = scaler
 
@@ -71,7 +80,9 @@ def _preprocess(self, data: pd.DataFrame) -> pd.DataFrame:
                 data[col] = data[col].astype(int)  # Convert True/False to 1/0
 
             elif dtype == "datetime":
-                data[col] = data[col].apply(lambda x: x.timestamp() if pd.notnull(x) else np.nan)  # Convert to Unix timestamp
+                data[col] = data[col].apply(
+                    lambda x: x.timestamp() if pd.notnull(x) else np.nan
+                )  # Convert to Unix timestamp
             
             elif dtype == "timedelta": 
                 data[col] = pd.to_timedelta(data[col]).dt.total_seconds()
@@ -125,11 +136,23 @@ def validate(self, data: pd.DataFrame):
     def _encode_categorical(self, series: pd.Series, encoder):
         """Encode categorical columns."""
         if isinstance(encoder, LabelEncoder):
-            return pd.DataFrame(encoder.fit_transform(series), columns=[series.name])
+            return pd.DataFrame(
+                encoder.fit_transform(series), 
+                columns=[series.name]
+            )
         elif isinstance(encoder, OneHotEncoder):
             encoded_array = encoder.fit_transform(series.values.reshape(-1, 1))
-            encoded_df = pd.DataFrame(encoded_array, columns=encoder.get_feature_names_out([series.name]))
+            encoded_df = pd.DataFrame(
+                encoded_array, 
+                columns=encoder.get_feature_names_out([series.name])
+            )
             return encoded_df
+        elif isinstance(encoder, dict) and encoder['type'] == 'frequency':
+            # Frequency encoding
+            encoded_values = series.map(encoder['mapping'])
+            return pd.DataFrame(encoded_values, columns=[series.name])
+        else:
+            raise TypeError(f"Unsupported encoder type: {type(encoder)}")
 
     def _decode_categorical(self, encoded: pd.Series or pd.DataFrame, encoder):
         """
@@ -170,6 +193,23 @@ def _decode_categorical(self, encoded: pd.Series or pd.DataFrame, encoder):
             cats = encoder.categories_[0]
             return pd.Series(cats[idx], index=getattr(encoded, "index", None))
 
+        # FREQUENCY ENCODER CASE
+        elif isinstance(encoder, dict) and encoder['type'] == 'frequency':
+            # For frequency encoding, we need to map the encoded values back to categories
+            # We'll use the inverse mapping (frequency -> category)
+            inverse_mapping = {v: k for k, v in encoder['mapping'].items()}
+            # Find the closest frequency for each encoded value
+            encoded_values = encoded.values.flatten()
+            decoded = []
+            for val in encoded_values:
+                if pd.isna(val):
+                    decoded.append(np.nan)
+                else:
+                    # Find the category with the closest frequency
+                    closest_freq = min(inverse_mapping.keys(), key=lambda x: abs(x - val))
+                    decoded.append(inverse_mapping[closest_freq])
+            return pd.Series(decoded, index=getattr(encoded, "index", None))
+
         else:
             raise TypeError(f"Unsupported encoder type: {type(encoder)}")