aydinnyunus

# get current date in required format 
import datetime 
  
# store the birthdates of your contacts 
import json 
   
from selenium import webdriver 
   
# add a delay so that all elements of 
# the webpage are loaded before proceeding 

aydinnyunus

import numpy as np
from sklearn.preprocessing import Imputer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
from sklearn import cross_validation

veri = pd.read_csv("cancer.data")

veri.replace("?",-99999,inplace=True)

aydinnyunus

import json
from difflib import get_close_matches

data = json.load(open("data.json"))

def translate(word):
    word = word.lower()
    if word in data:
        return data[word]
    elif word.title() in data:

aydinnyunus

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel

ds = pd.read_csv("sample-data.csv")

tf = TfidfVectorizer(analyzer='word', ngram_range=(1, 3), min_df=0, stop_words='english')
tfidf_matrix = tf.fit_transform(ds['description'])

cosine_similarities = linear_kernel(tfidf_matrix, tfidf_matrix)

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from numpy import exp,array,random,dot

class NeuralNetwork():
	def __init__(self):
		random.seed(1)
		self.synaptic_weights = 2* random.random((3,1))-1

	def __sigmoid(self,x):
		return 1/(1+exp(-x))

aydinnyunus

from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np

df = pd.read_csv('W.csv',sep = ',')
df['Height']= df.Height*2.54
df['Weight'] = df.Weight*0.45
Gender = df[df.columns[0]]
HW = df[df.columns[1:3]]