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Created 4 years ago
import pandas as pd
msg=pd.read_csv('datasets/6.csv',names=['message','label'])
print('The dimensions of the dataset',msg.shape)
msg['labelnum']=msg.label.map({'pos':1,'neg':0})
X=msg.message
y=msg.labelnum
print(X)
print(y)
from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest=train_test_split(X,y)
print(xtest.shape)
print(xtrain.shape)
print(ytest.shape)
print(ytrain.shape)
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
xtrain_dtm = count_vect.fit_transform(xtrain)
xtest_dtm=count_vect.transform(xtest)
from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB().fit(xtrain_dtm,ytrain)
predicted = clf.predict(xtest_dtm)
from sklearn import metrics
print('Accuracy metrics')
print('Accuracy of the classifer is',metrics.accuracy_score(ytest,predicted))
print('Confusion matrix')
print(metrics.confusion_matrix(ytest,predicted))
print('Recall and Precison ')
print(metrics.recall_score(ytest,predicted))
print(metrics.precision_score(ytest,predicted))The dimensions of the dataset (19, 2)
0 I love this sandwich
1 This is an amazing place
2 I feel very good about these beers
3 This is my best work
4 What an awesome view
5 I do not like this restaurant
6 I am tired of this stuff
7 I can't deal with this
8 He is my sworn enemy
9 My boss is horrible
10 This is an awesome place
11 I do not like the taste of this juice
12 I love to dance
13 I am sick and tired of this place
14 What a great holiday
15 That is a bad locality to stay
16 We will have good fun tomorrow
17 I went to my enemy's house today
18 i like to eat food
Name: message, dtype: object
0 1
1 1
2 1
3 1
4 1
5 0
6 0
7 0
8 0
9 0
10 1
11 0
12 1
13 0
14 1
15 0
16 1
17 0
18 1
Name: labelnum, dtype: int64
(5,)
(14,)
(5,)
(14,)
Accuracy metrics
Accuracy of the classifer is 0.8
Confusion matrix
[[2 1]
[0 2]]
Recall and Precison
1.0
0.6666666666666666