diff --git a/rosie/chamber_of_deputies/adapter.py b/rosie/chamber_of_deputies/adapter.py
index 4a57282c2..8c7452c4b 100644
--- a/rosie/chamber_of_deputies/adapter.py
+++ b/rosie/chamber_of_deputies/adapter.py
@@ -2,10 +2,18 @@
 
 import numpy as np
 import pandas as pd
-from serenata_toolbox.ceap_dataset import CEAPDataset
+from serenata_toolbox.chamber_of_deputies.chamber_of_deputies_dataset import ChamberOfDeputiesDataset
 from serenata_toolbox.datasets import fetch
 
 
+COLUMNS = {
+    'category': 'subquota_description',
+    'net_value': 'total_net_value',
+    'recipient_id': 'cnpj_cpf',
+    'recipient': 'supplier',
+}
+
+
 class Adapter:
     COMPANIES_DATASET = '2016-09-03-companies.xz'
 
@@ -15,39 +23,62 @@ def __init__(self, path):
     @property
     def dataset(self):
         self.update_datasets()
-        reimbursements = self.get_reimbursements()
+        self.get_reimbursements()
         companies = self.get_companies()
-        return pd.merge(reimbursements, companies,
-                        how='left',
-                        left_on='cnpj_cpf',
-                        right_on='cnpj')
+        self._dataset = self._dataset.merge(companies,
+                                            how='left',
+                                            left_on='cnpj_cpf',
+                                            right_on='cnpj')
+        self.prepare_dataset()
+        return self._dataset
+
+    def prepare_dataset(self):
+        self.rename_columns()
+        self.rename_categories()
+
+    def rename_columns(self):
+        columns = {v: k for k, v in COLUMNS.items()}
+        self._dataset.rename(columns=columns, inplace=True)
+
+    def rename_categories(self):
+        # There's no documented type for `3`, thus we assume it's an input error
+        self._dataset['document_type'].replace({3: None}, inplace=True)
+        self._dataset['document_type'] = self._dataset['document_type'].astype(
+            'category')
+        types = ['bill_of_sale', 'simple_receipt', 'expense_made_abroad']
+        self._dataset['document_type'].cat.rename_categories(
+            types, inplace=True)
+        # Classifiers expect a more broad category name for meals
+        self._dataset['category'] = self._dataset['category'].replace(
+            {'Congressperson meal': 'Meal'})
+        self._dataset['is_party_expense'] = \
+            self._dataset['congressperson_id'].isnull()
 
     def update_datasets(self):
         os.makedirs(self.path, exist_ok=True)
-        ceap = CEAPDataset(self.path)
-        ceap.fetch()
-        ceap.convert_to_csv()
-        ceap.translate()
-        ceap.clean()
+        chamber_of_deputies = ChamberOfDeputiesDataset(self.path)
+        chamber_of_deputies.fetch()
+        chamber_of_deputies.convert_to_csv()
+        chamber_of_deputies.translate()
+        chamber_of_deputies.clean()
         fetch(self.COMPANIES_DATASET, self.path)
 
     def get_reimbursements(self):
-        dataset = \
-            pd.read_csv(os.path.join(self.path, 'reimbursements.xz'),
-                        dtype={'applicant_id': np.str,
-                               'cnpj_cpf': np.str,
-                               'congressperson_id': np.str,
-                               'subquota_number': np.str},
-                        low_memory=False)
-        dataset['issue_date'] = pd.to_datetime(dataset['issue_date'],
-                                               errors='coerce')
-        return dataset
+        path = os.path.join(self.path, 'reimbursements.xz')
+        self._dataset = pd.read_csv(path,
+                                    dtype={'applicant_id': np.str,
+                                           'cnpj_cpf': np.str,
+                                           'congressperson_id': np.str,
+                                           'subquota_number': np.str},
+                                    low_memory=False)
+        self._dataset['issue_date'] = pd.to_datetime(
+            self._dataset['issue_date'], errors='coerce')
+        return self._dataset
 
     def get_companies(self):
-        dataset = pd.read_csv(os.path.join(self.path, self.COMPANIES_DATASET),
-                              dtype={'cnpj': np.str},
-                              low_memory=False)
+        path = os.path.join(self.path, self.COMPANIES_DATASET)
+        dataset = pd.read_csv(path, dtype={'cnpj': np.str}, low_memory=False)
         dataset['cnpj'] = dataset['cnpj'].str.replace(r'\D', '')
-        dataset['situation_date'] = pd.to_datetime(dataset['situation_date'],
-                                                   errors='coerce')
+        dataset['situation_date'] = pd.to_datetime(
+            dataset['situation_date'], errors='coerce')
         return dataset
diff --git a/rosie/chamber_of_deputies/classifiers/election_expenses_classifier.py b/rosie/chamber_of_deputies/classifiers/election_expenses_classifier.py
index b625078e7..91a876b05 100644
--- a/rosie/chamber_of_deputies/classifiers/election_expenses_classifier.py
+++ b/rosie/chamber_of_deputies/classifiers/election_expenses_classifier.py
@@ -5,6 +5,17 @@
 
 
 class ElectionExpensesClassifier(TransformerMixin):
+    """
+    Election Expenses classifier.
+
+    Check a `legal_entity` field for the presency of the political candidacy
+    category in the Brazilian Federal Revenue.
+
+    Dataset
+    -------
+    legal_entity : string column
+        Brazilian Federal Revenue category of companies, preceded by its code.
+    """
 
     def fit(self, X):
         return self
diff --git a/rosie/chamber_of_deputies/classifiers/irregular_companies_classifier.py b/rosie/chamber_of_deputies/classifiers/irregular_companies_classifier.py
index 92a117b44..b2574ffc5 100644
--- a/rosie/chamber_of_deputies/classifiers/irregular_companies_classifier.py
+++ b/rosie/chamber_of_deputies/classifiers/irregular_companies_classifier.py
@@ -5,6 +5,24 @@
 
 
 class IrregularCompaniesClassifier(TransformerMixin):
+    """
+    Irregular Companies classifier.
+
+    Check for the official state of the company in the
+    Brazilian Federal Revenue and reports for rows with companies unauthorized
+    to sell products or services.
+
+    Dataset
+    -------
+    issue_date : datetime column
+        Date when the expense was made.
+
+    situation : string column
+        Situation of the company according to the Brazilian Federal Revenue.
+
+    situation_date : datetime column
+        Date when the situation was last updated.
+    """
 
     def fit(self, X):
         return self
diff --git a/rosie/chamber_of_deputies/classifiers/meal_price_outlier_classifier.py b/rosie/chamber_of_deputies/classifiers/meal_price_outlier_classifier.py
index 861cfd2f0..862027176 100644
--- a/rosie/chamber_of_deputies/classifiers/meal_price_outlier_classifier.py
+++ b/rosie/chamber_of_deputies/classifiers/meal_price_outlier_classifier.py
@@ -7,14 +7,31 @@
 
 
 class MealPriceOutlierClassifier(TransformerMixin):
+    """
+    Meal Price Outlier classifier.
 
+    Dataset
+    -------
+    applicant_id : string column
+        A personal identifier code for every person making expenses.
+
+    category : category column
+        Category of the expense. The model will be applied just in rows where
+        the value is equal to "Meal".
+
+    net_value : float column
+        The value of the expense.
+
+    recipient_id : string column
+        A CNPJ (Brazilian company ID) or CPF (Brazilian personal tax ID).
+    """
 
     HOTEL_REGEX = r'hote(?:(?:ls?)|is)'
     CLUSTER_KEYS = ['mean', 'std']
 
     def fit(self, X):
         _X = X[self.__applicable_rows(X)]
-        companies = _X.groupby('cnpj_cpf').apply(self.__company_stats) \
+        companies = _X.groupby('recipient_id').apply(self.__company_stats) \
             .reset_index()
         companies = companies[self.__applicable_company_rows(companies)]
 
@@ -34,17 +51,17 @@ def transform(self, X=None):
     def predict(self, X):
         _X = X.copy()
         companies = _X[self.__applicable_rows(_X)] \
-            .groupby('cnpj_cpf').apply(self.__company_stats) \
+            .groupby('recipient_id').apply(self.__company_stats) \
             .reset_index()
         companies['cluster'] = \
             self.cluster_model.predict(companies[self.CLUSTER_KEYS])
         companies = pd.merge(companies,
                              self.clusters[['cluster', 'threshold']],
                              how='left')
-        _X = pd.merge(_X, companies[['cnpj_cpf', 'threshold']], how='left')
+        _X = pd.merge(_X, companies[['recipient_id', 'threshold']], how='left')
         known_companies = companies[self.__applicable_company_rows(companies)]
         known_thresholds = known_companies \
-            .groupby('cnpj_cpf') \
+            .groupby('recipient_id') \
             .apply(lambda x: x['mean'] + 3 * x['std']) \
             .reset_index() \
             .rename(columns={0: 'cnpj_threshold'})
@@ -55,21 +72,21 @@ def predict(self, X):
         _X['y'] = 1
         is_outlier = self.__applicable_rows(_X) & \
             _X['threshold'].notnull() & \
-            (_X['total_net_value'] > _X['threshold'])
+            (_X['net_value'] > _X['threshold'])
         _X.loc[is_outlier, 'y'] = -1
         return _X['y']
 
     def __applicable_rows(self, X):
-        return (X['subquota_description'] == 'Congressperson meal') & \
-            (X['cnpj_cpf'].str.len() == 14) & \
-            (~X['supplier'].apply(self.__normalize_string).str.contains(self.HOTEL_REGEX))
+        return (X['category'] == 'Meal') & \
+            (X['recipient_id'].str.len() == 14) & \
+            (~X['recipient'].apply(self.__normalize_string).str.contains(self.HOTEL_REGEX))
 
     def __applicable_company_rows(self, companies):
         return (companies['congresspeople'] > 3) & (companies['records'] > 20)
 
     def __company_stats(self, X):
-        stats = {'mean': np.mean(X['total_net_value']),
-                 'std': np.std(X['total_net_value']),
+        stats = {'mean': np.mean(X['net_value']),
+                 'std': np.std(X['net_value']),
                  'congresspeople': len(np.unique(X['applicant_id'])),
                  'records': len(X)}
         return pd.Series(stats)
diff --git a/rosie/chamber_of_deputies/classifiers/monthly_subquota_limit_classifier.py b/rosie/chamber_of_deputies/classifiers/monthly_subquota_limit_classifier.py
index 8da52d10c..93b991d5d 100644
--- a/rosie/chamber_of_deputies/classifiers/monthly_subquota_limit_classifier.py
+++ b/rosie/chamber_of_deputies/classifiers/monthly_subquota_limit_classifier.py
@@ -6,6 +6,18 @@
 
 
 class MonthlySubquotaLimitClassifier(TransformerMixin):
+    """
+    Monthly Subquota Limit classifier.
+
+    Dataset
+    -------
+    issue_date : datetime column
+        Date when the expense was made.
+
+    net_value : float column
+        The value of the expense.
+    """
+
     KEYS = ['applicant_id', 'month', 'year']
 
     def fit(self, X):
@@ -62,7 +74,7 @@ def predict_proba(self, X=None):
 
 
     def __create_columns(self):
-        self._X['net_value_int'] = (self._X['total_net_value'] * 100).apply(int)
+        self._X['net_value_int'] = (self._X['net_value'] * 100).apply(int)
 
         self._X['coerced_issue_date'] = \
             pd.to_datetime(self._X['issue_date'], errors='coerce')
diff --git a/rosie/chamber_of_deputies/classifiers/traveled_speeds_classifier.py b/rosie/chamber_of_deputies/classifiers/traveled_speeds_classifier.py
index 9b222db47..2b54c324b 100644
--- a/rosie/chamber_of_deputies/classifiers/traveled_speeds_classifier.py
+++ b/rosie/chamber_of_deputies/classifiers/traveled_speeds_classifier.py
@@ -8,6 +8,31 @@
 
 
 class TraveledSpeedsClassifier(TransformerMixin):
+    """
+    Traveled Speeds classifier.
+
+    Dataset
+    -------
+    applicant_id : category column
+        A personal identifier code for every person making expenses.
+
+    category : category column
+        Category of the expense. The model will be applied just in rows where
+        the value is equal to "Meal".
+
+    is_party_expense : bool column
+        If the row corresponds to a party expense or not. The model will be
+        applied just in rows where the value is equal to `False`.
+
+    issue_date : datetime column
+        Date when the expense was made.
+
+    latitude : float column
+        Latitude of the place where the expense was made.
+
+    longitude : float column
+        Longitude of the place where the expense was made.
+    """
 
     AGG_KEYS = ['applicant_id', 'issue_date']
 
@@ -61,10 +86,11 @@ def __classify_dataset(self, X):
         return X
 
     def __applicable_rows(self, X):
-        return (X['subquota_description'] == 'Congressperson meal') & \
+        return (X['category'] == 'Meal') & \
             (-73.992222 < X['longitude']) & (X['longitude'] < -34.7916667) & \
             (-33.742222 < X['latitude']) & (X['latitude'] < 5.2722222) & \
-            X[['congressperson_id', 'latitude', 'longitude']].notnull().all(axis=1)
+            ~X['is_party_expense'] & \
+            X[['latitude', 'longitude']].notnull().all(axis=1)
 
     def __calculate_sum_distances(self, X):
         coordinate_list = X[['latitude', 'longitude']].values
diff --git a/rosie/chamber_of_deputies/settings.py b/rosie/chamber_of_deputies/settings.py
index 8d62be196..22769288f 100644
--- a/rosie/chamber_of_deputies/settings.py
+++ b/rosie/chamber_of_deputies/settings.py
@@ -16,5 +16,3 @@
 }
 
 UNIQUE_IDS = ['applicant_id', 'year', 'document_id']
-
-VALUE = 'total_net_value'
diff --git a/rosie/chamber_of_deputies/tests/fixtures/irregular_companies_classifier.csv b/rosie/chamber_of_deputies/tests/fixtures/irregular_companies_classifier.csv
index 49f29ea03..e814a2cdf 100644
--- a/rosie/chamber_of_deputies/tests/fixtures/irregular_companies_classifier.csv
+++ b/rosie/chamber_of_deputies/tests/fixtures/irregular_companies_classifier.csv
@@ -1,4 +1,4 @@
-cnpj_cpf,situation,situation_date,issue_date
+recipient_id,situation,situation_date,issue_date
 02989654001197,ABERTA,2013-01-03,2013-01-30
 02989654001197,BAIXADA,2013-01-03,2013-01-30
 02989654001197,NULA,2013-01-03,2013-01-30
diff --git a/rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv b/rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv
index 75b77c525..b9c5f3b0f 100644
--- a/rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv
+++ b/rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv
@@ -1,82 +1,82 @@
-applicant_id,subquota_description,cnpj_cpf,supplier,total_net_value
-999,Congressperson meal,47667414122,João da Silva,9999999
-999,Congressperson meal,24624607000100,Hotel X,30
-999,Congressperson meal,24624607000101,Y Hotels,30
-999,Congressperson meal,24624607000102,X hotéis,30
-999,Congressperson meal,24624607000103,Hotel X,9999999
-999,Congressperson meal,24624607000104,Y Hotels,9999999
-999,Congressperson meal,24624607000105,X hotéis,9999999
-111,Congressperson meal,08378940000120,A Restaurant,3
-111,Congressperson meal,08378940000120,A Restaurant,30
-222,Congressperson meal,08378940000120,A Restaurant,40
-333,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,60
-444,Congressperson meal,08378940000120,A Restaurant,70
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-444,Congressperson meal,08378940000120,A Restaurant,50
-111,Congressperson meal,08378940000120,A Restaurant,900
-111,Congressperson meal,08378940000120,A Restaurant,80
-222,Congressperson meal,08378940000120,A Restaurant,70
-333,Congressperson meal,08378940000120,A Restaurant,60
-444,Congressperson meal,08378940000120,A Restaurant,70
-444,Congressperson meal,08378940000120,A Restaurant,80
-444,Congressperson meal,08378940000120,A Restaurant,90
-111,Congressperson meal,67661714000111,B Restaurant,130
-222,Congressperson meal,67661714000111,B Restaurant,140
-333,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,160
-444,Congressperson meal,67661714000111,B Restaurant,170
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-444,Congressperson meal,67661714000111,B Restaurant,150
-111,Congressperson meal,81387409000104,C Restaurant,330
-222,Congressperson meal,81387409000104,C Restaurant,340
-333,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,360
-444,Congressperson meal,81387409000104,C Restaurant,370
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,81387409000104,C Restaurant,350
-444,Congressperson meal,22472225000183,GOL,400
+applicant_id,category,recipient_id,recipient,net_value
+999,Meal,47667414122,João da Silva,9999999
+999,Meal,24624607000100,Hotel X,30
+999,Meal,24624607000101,Y Hotels,30
+999,Meal,24624607000102,X hotéis,30
+999,Meal,24624607000103,Hotel X,9999999
+999,Meal,24624607000104,Y Hotels,9999999
+999,Meal,24624607000105,X hotéis,9999999
+111,Meal,08378940000120,A Restaurant,3
+111,Meal,08378940000120,A Restaurant,30
+222,Meal,08378940000120,A Restaurant,40
+333,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,60
+444,Meal,08378940000120,A Restaurant,70
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+444,Meal,08378940000120,A Restaurant,50
+111,Meal,08378940000120,A Restaurant,900
+111,Meal,08378940000120,A Restaurant,80
+222,Meal,08378940000120,A Restaurant,70
+333,Meal,08378940000120,A Restaurant,60
+444,Meal,08378940000120,A Restaurant,70
+444,Meal,08378940000120,A Restaurant,80
+444,Meal,08378940000120,A Restaurant,90
+111,Meal,67661714000111,B Restaurant,130
+222,Meal,67661714000111,B Restaurant,140
+333,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,160
+444,Meal,67661714000111,B Restaurant,170
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+444,Meal,67661714000111,B Restaurant,150
+111,Meal,81387409000104,C Restaurant,330
+222,Meal,81387409000104,C Restaurant,340
+333,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,360
+444,Meal,81387409000104,C Restaurant,370
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,81387409000104,C Restaurant,350
+444,Meal,22472225000183,GOL,400
 444,Flight ticket issue,22472225000183,GOL,9999999
 444,Flight ticket issue,22472225000183,GOL,5
diff --git a/rosie/chamber_of_deputies/tests/fixtures/monthly_subquota_limit_classifier.csv b/rosie/chamber_of_deputies/tests/fixtures/monthly_subquota_limit_classifier.csv
index fd6ae2c02..edd4e550c 100644
--- a/rosie/chamber_of_deputies/tests/fixtures/monthly_subquota_limit_classifier.csv
+++ b/rosie/chamber_of_deputies/tests/fixtures/monthly_subquota_limit_classifier.csv
@@ -1,4 +1,4 @@
-applicant_id,subquota_number,issue_date,year,month,total_net_value
+applicant_id,subquota_number,issue_date,year,month,net_value
 1,120,2015-03-01,2015,3,10500
 1,120,2015-03-01,2015,3,401
 2,120,2015-04-01,2015,4,10500
diff --git a/rosie/chamber_of_deputies/tests/fixtures/reimbursements.xz b/rosie/chamber_of_deputies/tests/fixtures/reimbursements.xz
index 06bf968fa..86bf1067b 100644
Binary files a/rosie/chamber_of_deputies/tests/fixtures/reimbursements.xz and b/rosie/chamber_of_deputies/tests/fixtures/reimbursements.xz differ
diff --git a/rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv b/rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv
index 08ad5f06e..2668c9351 100644
--- a/rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv
+++ b/rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv
@@ -1,30 +1,30 @@
-applicant_id,congressperson_id,issue_date,subquota_description,cnpj_cpf,latitude,longitude
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Flight ticket issue,08378940000120,-29.2310464,-51.1597365
-999,,2016-01-01,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,-9.9753770,
-999,999,2016-01-02,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-03,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-03,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-04,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-04,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-04,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-04,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,14047033000100,-10.6519807,-68.4995996
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-05,Congressperson meal,08378940000120,-9.9753770,-67.8248977
-999,999,2016-01-01,Congressperson meal,08378940000120,6.9753770,-33.8248977
+applicant_id,is_party_expense,issue_date,category,recipient_id,latitude,longitude
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Flight ticket issue,08378940000120,-29.2310464,-51.1597365
+999,true,2016-01-01,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,-9.9753770,
+999,false,2016-01-02,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-03,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-03,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-04,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-04,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-04,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-04,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,14047033000100,-10.6519807,-68.4995996
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-05,Meal,08378940000120,-9.9753770,-67.8248977
+999,false,2016-01-01,Meal,08378940000120,6.9753770,-33.8248977
diff --git a/rosie/chamber_of_deputies/tests/test_adapter.py b/rosie/chamber_of_deputies/tests/test_adapter.py
new file mode 100644
index 000000000..c738e9efd
--- /dev/null
+++ b/rosie/chamber_of_deputies/tests/test_adapter.py
@@ -0,0 +1,57 @@
+import shutil
+import os
+from tempfile import mkdtemp
+from unittest import TestCase
+from unittest.mock import patch
+from shutil import copy2
+
+import pandas as pd
+
+from rosie.chamber_of_deputies.adapter import Adapter as subject_class
+from rosie.chamber_of_deputies.adapter import COLUMNS as ADAPTER_COLUMNS
+
+
+class TestAdapter(TestCase):
+
+    def setUp(self):
+        self.temp_path = mkdtemp()
+        self.fixtures_path = os.path.join('rosie', 'chamber_of_deputies', 'tests', 'fixtures')
+        copies = (
+            ('companies.xz', subject_class.COMPANIES_DATASET),
+            ('reimbursements.xz', 'reimbursements.xz')
+        )
+        for source, target in copies:
+            copy2(os.path.join(self.fixtures_path, source), os.path.join(self.temp_path, target))
+        self.subject = subject_class(self.temp_path)
+
+    def tearDown(self):
+        shutil.rmtree(self.temp_path)
+
+    @patch('rosie.chamber_of_deputies.adapter.ChamberOfDeputiesDataset')
+    @patch('rosie.chamber_of_deputies.adapter.fetch')
+    def test_get_performs_a_left_merge_between_reimbursements_and_companies(self, fetch, chamber_of_deputies):
+        self.assertEqual(6, len(self.subject.dataset))
+        self.assertEqual(1, self.subject.dataset['legal_entity'].isnull().sum())
+
+    @patch('rosie.chamber_of_deputies.adapter.ChamberOfDeputiesDataset')
+    @patch('rosie.chamber_of_deputies.adapter.fetch')
+    def test_prepare_dataset(self, fetch, chamber_of_deputies):
+        """
+        * Rename columns.
+        * Make `document_type` a category column.
+        * Rename values for `category`.
+        * Create `is_party_expense` column.
+        """
+        dataset = self.subject.dataset
+        self.assertTrue(set(ADAPTER_COLUMNS.keys()).issubset(set(dataset.columns)))
+        document_types = ['bill_of_sale', 'simple_receipt', 'expense_made_abroad']
+        self.assertEqual(document_types,
+                         dataset['document_type'].cat.categories.tolist())
+        fixture = pd.read_csv(os.path.join(self.fixtures_path, 'reimbursements.xz'))
+        meal_rows = fixture \
+            .query('subquota_description == "Congressperson meal"').index
+        self.assertEqual(['Meal'],
+                         dataset.loc[meal_rows, 'category'].unique().tolist())
+        party_expense_rows = fixture[fixture['congressperson_id'].isnull()].index
+        self.assertEqual([True],
+                         dataset.loc[party_expense_rows, 'is_party_expense'].unique().tolist())
diff --git a/rosie/chamber_of_deputies/tests/test_chamber_of_deputies.py b/rosie/chamber_of_deputies/tests/test_chamber_of_deputies.py
index 69fd23eb4..3bab53a5e 100644
--- a/rosie/chamber_of_deputies/tests/test_chamber_of_deputies.py
+++ b/rosie/chamber_of_deputies/tests/test_chamber_of_deputies.py
@@ -17,10 +17,6 @@ class TestChamberOfDeputies(TestCase):
     def setUp(self):
         row = pd.Series({'applicant_id': 444,
                          'document_id': 999,
-                         'subquota_description': 'Congressperson meal',
-                         'cnpj_cpf': '67661714000111',
-                         'supplier': 'B Restaurant',
-                         'total_net_value': 178,
                          'year': 2016})
         self.dataset = pd.DataFrame().append(row, ignore_index=True)
         self.temp_dir = mkdtemp()
diff --git a/rosie/chamber_of_deputies/tests/test_dataset.py b/rosie/chamber_of_deputies/tests/test_dataset.py
deleted file mode 100644
index e5c81ee92..000000000
--- a/rosie/chamber_of_deputies/tests/test_dataset.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import shutil
-import os
-from tempfile import mkdtemp
-from unittest import TestCase
-from unittest.mock import patch
-from shutil import copy2
-
-from rosie.chamber_of_deputies.adapter import Adapter
-
-
-class TestDataset(TestCase):
-
-    def setUp(self):
-        self.temp_path = mkdtemp()
-        fixtures = os.path.join('rosie', 'chamber_of_deputies', 'tests', 'fixtures')
-        copies = (
-            ('companies.xz', Adapter.COMPANIES_DATASET),
-            ('reimbursements.xz', 'reimbursements.xz')
-        )
-        for source, target in copies:
-            copy2(os.path.join(fixtures, source), os.path.join(self.temp_path, target))
-        self.subject = Adapter(self.temp_path)
-
-    def tearDown(self):
-        shutil.rmtree(self.temp_path)
-
-    @patch('rosie.chamber_of_deputies.adapter.CEAPDataset')
-    @patch('rosie.chamber_of_deputies.adapter.fetch')
-    def test_get_performs_a_left_merge_between_reimbursements_and_companies(self, fetch, ceap):
-        self.assertEqual(5, len(self.subject.dataset))
-        self.assertEqual(1, self.subject.dataset['legal_entity'].isnull().sum())
diff --git a/rosie/chamber_of_deputies/tests/test_meal_price_outlier_classifier.py b/rosie/chamber_of_deputies/tests/test_meal_price_outlier_classifier.py
index 91769667a..ce8db8b7e 100644
--- a/rosie/chamber_of_deputies/tests/test_meal_price_outlier_classifier.py
+++ b/rosie/chamber_of_deputies/tests/test_meal_price_outlier_classifier.py
@@ -12,7 +12,7 @@ class TestMealPriceOutlierClassifier(TestCase):
 
     def setUp(self):
         self.dataset = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/meal_price_outlier_classifier.csv',
-                                   dtype={'cnpj_cpf': np.str})
+                                   dtype={'recipient_id': np.str})
         self.subject = MealPriceOutlierClassifier()
         self.subject.fit(self.dataset)
 
@@ -24,40 +24,40 @@ def test_predict_returns_a_prediction_for_each_observation(self, kmeans_mock):
 
     def test_predict_outlier_for_common_cnpjs_when_value_is_greater_than_mean_plus_3_stds(self):
         row = pd.Series({'applicant_id': 444,
-                         'subquota_description': 'Congressperson meal',
-                         'cnpj_cpf': '67661714000111',
-                         'supplier': 'B Restaurant',
-                         'total_net_value': 178})
+                         'category': 'Meal',
+                         'recipient_id': '67661714000111',
+                         'recipient': 'B Restaurant',
+                         'net_value': 178})
         X = pd.DataFrame().append(row, ignore_index=True)
         prediction = self.subject.predict(X)
         self.assertEqual(-1, prediction[0])
 
     def test_predict_inlier_for_common_cnpjs_when_value_is_less_than_mean_plus_3_stds(self):
         row = pd.Series({'applicant_id': 444,
-                         'subquota_description': 'Congressperson meal',
-                         'cnpj_cpf': '67661714000111',
-                         'supplier': 'B Restaurant',
-                         'total_net_value': 177})
+                         'category': 'Meal',
+                         'recipient_id': '67661714000111',
+                         'recipient': 'B Restaurant',
+                         'net_value': 177})
         X = pd.DataFrame().append(row, ignore_index=True)
         prediction = self.subject.predict(X)
         self.assertEqual(1, prediction[0])
 
     def test_predict_outlier_for_non_common_cnpjs_when_value_is_greater_than_mean_plus_4_stds(self):
         row = pd.Series({'applicant_id': 444,
-                         'subquota_description': 'Congressperson meal',
-                         'cnpj_cpf': '22412242000125',
-                         'supplier': 'D Restaurant',
-                         'total_net_value': 178})
+                         'category': 'Meal',
+                         'recipient_id': '22412242000125',
+                         'recipient': 'D Restaurant',
+                         'net_value': 178})
         X = pd.DataFrame().append(row, ignore_index=True)
         prediction = self.subject.predict(X)
         self.assertEqual(-1, prediction[0])
 
     def test_predict_inlier_for_non_common_cnpjs_when_value_is_less_than_mean_plus_4_stds(self):
         row = pd.Series({'applicant_id': 444,
-                         'subquota_description': 'Congressperson meal',
-                         'cnpj_cpf': '22412242000125',
-                         'supplier': 'D Restaurant',
-                         'total_net_value': 177})
+                         'category': 'Meal',
+                         'recipient_id': '22412242000125',
+                         'recipient': 'D Restaurant',
+                         'net_value': 177})
         X = pd.DataFrame().append(row, ignore_index=True)
         prediction = self.subject.predict(X)
         self.assertEqual(1, prediction[0])
diff --git a/rosie/chamber_of_deputies/tests/test_traveled_speeds_classifier.py b/rosie/chamber_of_deputies/tests/test_traveled_speeds_classifier.py
index 3a25bd9bc..d0fab5c33 100644
--- a/rosie/chamber_of_deputies/tests/test_traveled_speeds_classifier.py
+++ b/rosie/chamber_of_deputies/tests/test_traveled_speeds_classifier.py
@@ -12,7 +12,7 @@ class TestTraveledSpeedsClassifier(TestCase):
 
     def setUp(self):
         self.dataset = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/traveled_speeds_classifier.csv',
-                                   dtype={'cnpj_cpf': np.str})
+                                   dtype={'recipient_id': np.str})
         self.subject = TraveledSpeedsClassifier()
         self.subject.fit(self.dataset)
 
diff --git a/rosie/core/classifiers/invalid_cnpj_cpf_classifier.py b/rosie/core/classifiers/invalid_cnpj_cpf_classifier.py
index fa2b20b0e..df2a5fb49 100644
--- a/rosie/core/classifiers/invalid_cnpj_cpf_classifier.py
+++ b/rosie/core/classifiers/invalid_cnpj_cpf_classifier.py
@@ -6,6 +6,20 @@
 
 
 class InvalidCnpjCpfClassifier(TransformerMixin):
+    """
+    Invalid CNPJ/CPF classifier.
+
+    Validate a `recipient_id` field by calculating its expected check digit
+    and verifying the authenticity of the provided ones.
+
+    Dataset
+    -------
+    document_type : category column
+        Validate rows with values 'bill_of_sale' or 'simple_receipt'.
+
+    recipient_id : string column
+        A CNPJ (Brazilian company ID) or CPF (Brazilian personal tax ID).
+    """
 
     def fit(self, X):
         return self
@@ -17,4 +31,5 @@ def predict(self, X):
         return np.r_[X.apply(self.__is_invalid, axis=1)]
 
     def __is_invalid(self, row):
-        return (row['document_type'] in [0, 1]) & (not cpfcnpj.validate(str(row['cnpj_cpf'])))
+        return (row['document_type'] in ['bill_of_sale', 'simple_receipt']) \
+            & (not cpfcnpj.validate(str(row['recipient_id'])))
diff --git a/rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv b/rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv
index 1fca62d2a..2b74497cc 100644
--- a/rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv
+++ b/rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv
@@ -1,10 +1,10 @@
-cnpj_cpf,document_type
-22472225000183,0
-22472225000180,0
-,0
-,2
-22472225000183,2
-22472225000180,2
-57725723501,0
-11111111111,0
-22472225000180,3
+recipient_id,document_type
+22472225000183,bill_of_sale
+22472225000180,bill_of_sale
+,bill_of_sale
+,expense_made_abroad
+22472225000183,expense_made_abroad
+22472225000180,expense_made_abroad
+57725723501,bill_of_sale
+11111111111,bill_of_sale
+22472225000180,
diff --git a/rosie/core/tests/test_invalid_cnpj_cpf_classifier.py b/rosie/core/tests/test_invalid_cnpj_cpf_classifier.py
index 614e2abc0..ed8eef160 100644
--- a/rosie/core/tests/test_invalid_cnpj_cpf_classifier.py
+++ b/rosie/core/tests/test_invalid_cnpj_cpf_classifier.py
@@ -10,7 +10,7 @@ class TestInvalidCnpjCpfClassifier(TestCase):
 
     def setUp(self):
         self.dataset = pd.read_csv('rosie/core/tests/fixtures/invalid_cnpj_cpf_classifier.csv',
-                                   dtype={'cnpj_cpf': np.str})
+                                   dtype={'recipient_id': np.str})
         self.subject = InvalidCnpjCpfClassifier()
 
     def test_is_valid_cnpj(self):