python - Use Logistic Regression for Tweets Topic Classification -

i have problem regarding use of logistic regression. i'm making tweets topic classification in python. far i'm able read train data mysql table using pandas, clean train tweets using nltk , create feature vectors using countvectorizer. here's code below..

import pandas pd sqlalchemy import * nltk.tokenize import regexptokenizer nltk.corpus import stopwords import re nltk.stem import snowballstemmer sklearn.feature_extraction.text import countvectorizer sklearn.linear_model import logisticregression  #connect database , training data engine = create_engine('mysql+mysqlconnector://root:root@localhost:3306/machinelearning') tweet = pd.read_sql_query('select label, tweets tweetstable', engine, index_col='label')  #text preprocessing (remove html markup, remove punctuation, tokenizing, remove stop words, stemming)  def preprocessing(pptweets):     pptweets = pptweets.lower()     urlrtweets = re.sub(r'https:.*$', ":", pptweets)     rpptweets = urlrtweets.replace("_", " ")     tokenizer = regexptokenizer(r'\w+')     tokens = tokenizer.tokenize(rpptweets)     filteredwords = [w w in tokens if not w in stopwords.words('english')]     stemmer = snowballstemmer("english")     stweets = [stemmer.stem(tokens) tokens in filteredwords]     return " ".join(stweets)  #initialize empty list hold clean reviews cleantweets = []  #loop on each review, create index goes 0 length of tweets list in range(0, len(tweet["tweets"])):     cleantweets.append(preprocessing(tweet["tweets"][i]))  #initialize "countvectorizer" object, scikit-learn's bow tools vectorizer = countvectorizer(analyzer="word",                              tokenizer=none,                              preprocessor=none,                              stop_words=none,                              max_features=5000)  #fit_transform() 2 functions: first, fits model #and learns vocabulary; second, transforms our training data #into feature vectors. input fit_transform should list of strings traindatafeatures = vectorizer.fit_transform(cleantweets)  #numpy arrays easy work with, convert result array traindatafeatures = traindatafeatures.toarray() 

the problem i'm facing right is.. don't know how use logistic regression learn train data. here's code use fit train data logistic regression classifier.

#train model logmodel = logisticregression() logmodel.fit(traindatafeatures, tweet["label"])  #check trained model intercept  print(logmodel.intercept_) #check trained model coefficients print(logmodel.coef_) 

i pass traindatafeatures input x , tweet["label"] label/class y each tweet logistic regression classifier can learn when run full code error this:

traceback (most recent call last):   file "c:\users\indra\anaconda3\lib\site-packages\pandas\indexes\base.py", line 1945, in get_loc     return self._engine.get_loc(key)   file "pandas\index.pyx", line 137, in pandas.index.indexengine.get_loc (pandas\index.c:4154)   file "pandas\index.pyx", line 159, in pandas.index.indexengine.get_loc (pandas\index.c:4018)   file "pandas\hashtable.pyx", line 675, in pandas.hashtable.pyobjecthashtable.get_item (pandas\hashtable.c:12368)   file "pandas\hashtable.pyx", line 683, in pandas.hashtable.pyobjecthashtable.get_item (pandas\hashtable.c:12322) keyerror: 'label' 

during handling of above exception, exception occurred:

traceback (most recent call last):   file "c:/users/indra/pycharmprojects/textclassifier/textclassifier.py", line 52, in <module>     logmodel.fit(traindatafeatures, tweet["label"])   file "c:\users\indra\anaconda3\lib\site-packages\pandas\core\frame.py", line 1997, in __getitem__     return self._getitem_column(key)   file "c:\users\indra\anaconda3\lib\site-packages\pandas\core\frame.py", line 2004, in _getitem_column     return self._get_item_cache(key)   file "c:\users\indra\anaconda3\lib\site-packages\pandas\core\generic.py", line 1350, in _get_item_cache     values = self._data.get(item)   file "c:\users\indra\anaconda3\lib\site-packages\pandas\core\internals.py", line 3290, in     loc = self.items.get_loc(item)   file "c:\users\indra\anaconda3\lib\site-packages\pandas\indexes\base.py", line 1947, in get_loc     return self._engine.get_loc(self._maybe_cast_indexer(key))   file "pandas\index.pyx", line 137, in pandas.index.indexengine.get_loc (pandas\index.c:4154)   file "pandas\index.pyx", line 159, in pandas.index.indexengine.get_loc (pandas\index.c:4018)   file "pandas\hashtable.pyx", line 675, in pandas.hashtable.pyobjecthashtable.get_item (pandas\hashtable.c:12368)   file "pandas\hashtable.pyx", line 683, in pandas.hashtable.pyobjecthashtable.get_item (pandas\hashtable.c:12322) keyerror: 'label' 

can me solve problem? :( i've been searching tutorials haven't found far.