Facing Attributeerror: 'list' Object Has No Attribute 'lower'

I have posted my sample train data as well as test data along with my code. I'm trying to use Naive Bayes algorithm to train the model. But, in the reviews I'm getting list of list

Solution 1:

I have applied a few modifications to your code. The one posted below works; I added comments on how to debug the one you posted above.

# These three will not used, do not import them# from sklearn.preprocessing import MultiLabelBinarizer # from sklearn.model_selection import train_test_split # from sklearn.metrics import confusion_matrix# This performs the classification task that you want with your input data in the format providedfrom sklearn.naive_bayes import MultinomialNB 

from sklearn.feature_extraction.text import CountVectorizer

defload_data(filename):
    """ This function works, but you have to modify the second-to-last line from
    reviews.append(line[0].split()) to reviews.append(line[0]).
    CountVectorizer will perform the splits by itself as it sees fit, trust him :)"""
    reviews = list()
    labels = list()
    withopen(filename) as file:
        file.readline()
        for line in file:
            line = line.strip().split(',')
            labels.append(line[1])
            reviews.append(line[0])

    return reviews, labels

X_train, y_train = load_data('train.txt')
X_test, y_test = load_data('test.txt')

vec = CountVectorizer() 
# Notice: clf means classifier, not vectorizer. # While it is syntactically correct, it's bad practice to give misleading names to your objects. # Replace "clf" with "vec" or something similar.# Important! you called only the fit method, but did not transform the data # afterwards. The fit method does not return the transformed data by itself. You # either have to call .fit() and then .transform() on your training data, or just fit_transform() once.

X_train_transformed =  vec.fit_transform(X_train) 

X_test_transformed = vec.transform(X_test)

clf= MultinomialNB()
clf.fit(X_train_transformed, y_train)

score = clf.score(X_test_transformed, y_test)
print("score of Naive Bayes algo is :" , score)

The output of this code is:

score of Naive Bayes algo is : 0.5