diff --git a/twitter.py b/twitter.py
index 52a64e0..d340896 100644
--- a/twitter.py
+++ b/twitter.py
@@ -1,9 +1,12 @@
 # %%
+# from platform import java_ver
 import pandas as pd
 import os
 import re
 import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text \
+    import CountVectorizer, TfidfTransformer, TfidfVectorizer, HashingVectorizer
+from sklearn.pipeline import Pipeline
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import confusion_matrix,classification_report
 from copy import deepcopy
@@ -17,9 +20,6 @@ data_all = pd.read_csv(filepath, header=0,
     delimiter=',', 
     # encoding_errors='surrogateescape'
     )
-# data.columns = ['index', 'id','date', 'query', 'user', 'text']
-# %%
-# data = data_all.loc[:,['Tweet', 'Sentiment']]
 # %% [markdown]
 ### Function definitions
 # %%
@@ -82,31 +82,41 @@ data_model['random_number'] = np.random.randn(len(idx))
 train_set = data_model[data_model['random_number'] <= 0.8]
 test_set = data_model[data_model['random_number'] > 0.8]
 # %%
-vectorizer = CountVectorizer(token_pattern=r'\b\w+\b')
-train_matrix = vectorizer.fit_transform(train_set['Tweet'])
-test_matrix = vectorizer.transform(test_set['Tweet'])
+def train_model_and_predict(train_set, test_set, 
+                            vectorizer, vectorizer_name,
+                            model, 
+                            colname_text = 'Tweet',
+                            colname_sent = 'sent_score'):
+    train_matrix = vectorizer.fit_transform(train_set[colname_text])
+    test_matrix = vectorizer.transform(test_set[colname_text])
+    X_train = train_matrix
+    X_test = test_matrix
+    y_train = train_set[colname_sent]
+    y_test = test_set[colname_sent]
+    model.fit(X_train,y_train)
+    predictions = model.predict(X_test)
+    y_test_arr = np.asarray(y_test)
+    print(f"{vectorizer_name}")
+    # print("Confussion matrix")
+    # print(confusion_matrix(predictions,y_test_arr))
+    print("Classification report")
+    print(classification_report(predictions,y_test_arr))
 # %%
-lr = LogisticRegression()
-X_train = train_matrix
-X_test = test_matrix
-y_train = train_set['sent_score']
-y_test = test_set['sent_score']
-lr.fit(X_train,y_train)
+vectorizers = [
+    ("CountVectorizer", CountVectorizer(token_pattern=r'\b\w+\b')),
+    ("HashingVectorizer, n_features=2**15", HashingVectorizer(n_features=2**15, analyzer='word', token_pattern=r'\b\w+\b')),
+    # ("HashingVectorizer, n_features=2**20", HashingVectorizer(n_features=2**20, analyzer='word', token_pattern=r'\b\w+\b')),
+    ("TfidfVectorizer", TfidfVectorizer()),
+    ("TfidfVectorizer, smooth_idf=False", TfidfVectorizer(smooth_idf=False)),
+    ("TfidfVectorizer, sublinear_tf=True", TfidfVectorizer(sublinear_tf=True)),
+    ("TfidfVectorizer, norm=None", TfidfVectorizer(norm=None)),
+    ]
 # %%
-predictions = lr.predict(X_test)
-# %%
-y_test_arr = np.asarray(y_test)
-confusion_matrix(predictions,y_test_arr)
-# %%
-print(classification_report(predictions,y_test))
-# %% [markdown]
-#               precision    recall  f1-score   support
-
-#         -1.0       0.91      0.96      0.94      1188
-#          0.0       0.99      0.97      0.98      4733
-#          1.0       0.97      0.98      0.98      4799
-
-#     accuracy                           0.97     10720
-#    macro avg       0.96      0.97      0.96     10720
-# weighted avg       0.97      0.97      0.97     10720
+for vec in vectorizers:
+    train_model_and_predict(train_set, test_set,
+                            vectorizer = vec[1],
+                            vectorizer_name = vec[0],
+                            model = LogisticRegression(max_iter=1000),
+                            colname_text = 'Tweet',
+                            colname_sent = 'sent_score')
 # %%