shreyash05

shreyash05

shreyash05

import pandas as pd
import boto3

def group_response():
    group = []
    client = boto3.client('cognito-idp','us-east-2')
    response = client.list_groups(UserPoolId="us-east-2_FRhf7xm7F")
    while(response["NextToken"]!=""):
        response = client.list_groups(UserPoolId="us-east-2_FRhf7xm7F",NextToken=response['NextToken'])
        group.append(response['Groups']) 

shreyash05

import pandas as pd
import boto3
import sys
import botocore
import botocore.exceptions

def group_response():
    group = []
    client = boto3.client('cognito-idp','us-east-2')
    response = client.list_groups(UserPoolId="us-east-2_FRhf7xm7F")

shreyash05

import pandas as pd
import boto3
import sys
import botocore
import botocore.exceptions
##########################################################################################################################
def group_response():
    group = []
    client = boto3.client('cognito-idp','us-east-2')
    response = client.list_groups(UserPoolId="us-east-2_FRhf7xm7F")

shreyash05

import pandas as pd
import boto3
import sys
import botocore
import botocore.exceptions
##########################################################################################################################
def group_response():
    group = []
    client = boto3.client('cognito-idp','us-east-2')
    response = client.list_groups(UserPoolId="us-east-2_FRhf7xm7F")

shreyash05

import spacy
from spacy import displacy

def find_subject_sentence(str):
    nlp = spacy.load('en_core_web_sm',disable=['ner','textcat'])
    text = str
    doc = nlp(text)
    displacy.render(doc, style='dep',jupyter=True)

    for token in doc:

shreyash05

import spacy
from spacy import displacy
def find_subject_sentence(str):
    nlp = spacy.load('en_core_web_sm',disable=['ner','textcat'])
    text = str
    doc = nlp(text)
    displacy.render(doc, style='dep',jupyter=True)
    sub = '\0'
    for token in doc:
        # extract subject

shreyash05

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def find_keyword_sentence(str):
    icnt = 0
    keyword = ''
    for element in str:
        if element == ' ':

shreyash05

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
from statistics import *
class AdvertisementAgency:
	def __init__(self):
		self.advertisement_dataset = pd.read_csv("Advertising.csv")
		self.x_axis_values = self.advertisement_dataset.TV
		self.y_axis_values = self.advertisement_dataset.sales
		self.mean_x = mean(self.x_axis_values)