aniruddha27

class MyStreamListener(tweepy.StreamListener):
    
    def __init__(self, time_limit=300):
        self.start_time = time.time()
        self.limit = time_limit
        super(MyStreamListener, self).__init__()
    
    def on_connect(self):
        print("Connected to Twitter API.")
        

aniruddha27

# confusion matrix in sklearn
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

# actual values
actual = [1,0,0,1,0,0,1,0,0,1]
# predicted values
predicted = [1,0,0,1,0,0,0,1,0,0]

# confusion matrix

aniruddha27

# import spacy
import spacy

# load english language model
nlp = spacy.load('en_core_web_sm',disable=['ner','textcat'])

text = "This is a sample sentence."

# create spacy 
doc = nlp(text)

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# data standardization with  sklearn
from sklearn.preprocessing import StandardScaler

# copy of datasets
X_train_stand = X_train.copy()
X_test_stand = X_test.copy()

# numerical features
num_cols = ['Item_Weight','Item_Visibility','Item_MRP','Outlet_Establishment_Year']

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# Query stats
pprint(db.restaurants.find({'cuisine':'French','grades.score':{'$gt':5}}).explain()['executionStats'])

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# Query stats 
pprint(db.restaurants.find({'cuisine':'American'}).explain()['executionStats'])

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pprint(db.restaurants.find().explain())

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# Multiple token search 
db.restaurants.find_one({"$text": {"$search": "Chinese -Restaurant"}})

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# Multiple token search 
db.restaurants.find_one({"$text": {"$search": "Chinese Kitchen"}})

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# Find restaurants with Kitchen in their name
db.restaurants.find_one({"$text": {"$search": "Kitchen"}})