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from sklearn.svm import SVC
clf=SVC()
clf.fit(x_train_tfidf,y_train)

y_pred=clf.predict(x_test_tfidf)
print(y_pred, y_test)

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from sklearn.metrics import accuracy_score, classification_report,confusion_matrix
print("Accuracy score:",accuracy_score(y_pred,y_test))
print("Confusion matrix:",confusion_matrix(y_pred,y_test))
print("Classification report:",classification_report(y_pred,y_test))

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message=["Congratulations on building your first Sentiment Analysis model! You're going great!"]
message=tfidf.transform(message).toarray()
clf.predict(message)[0]

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nltk.download('stopwords')
stopword_list=stopwords.words('english')
stopword_list.remove('no')
stopword_list.remove('not')

df.review=df.review.apply(lambda x : " ".join(x for x in x.split() if x not in stopword_list))
df['review'][5]

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import matplotlib.pyplot as plt
x = [5,7,8,10,3,17,4,9,7,9,8,9,6]
y = [40,36,47,48,120,46,67,48,31,134,50,35,56]
plt.scatter(x, y)
plt.show()

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import matplotlib.pyplot as plt
data =[20,25,27,75,40,67,62,75,78,71,32,82,127,140,78,67,132,82,87,66,56,52]         
plt.boxplot(data,vert=False)
plt.show()

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import numpy as np
outliers=[]
dataset=[11,10,12,14,12,15,14,13,15,102,12,14,17,19,107,10,13,12,14,12,108,12,11,14,13,15,10,15,12,10,14,13,15,10]

def detect_outliers(data):
  threshold=3
	mean=np.mean(data)
	std=np.std(data)
	
	for i in dataset:

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dataset=[11,10,12,14,12,15,14,13,15,102,12,14,17,19,107,10,13,12,14,12,108,12,11,14,13,15,10,15,12,10,14,13,15,10]

dataset=sorted(dataset)
q1, q3= np.percentile(dataset,[25,75])
iqr = q3 - q1
lower_bound = q1 -(1.5 * iqr) 
upper_bound = q3 +(1.5 * iqr) 
print('lower_bound={},upper_bound={}'.format(lower_bound,upper_bound))

outliers_pt=[]

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!pip install opencv-python

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img = cv2.imread('Zebra.jpg') 
plt.figure(figsize = (10,6))
plt.imshow(img)