emrepun

from recommender_engine import RecommenderEngine
import json

culture_keywords = "history historical art architecture city culture"
beach_n_sun_keywords = "beach beaches park nature holiday sea seaside sand sunshine sun sunny"
nightlife_keywords = "nightclub nightclubs nightlife bar bars pub pubs party beer"

def get_recommendations(keywords):
    result = RecommenderEngine.get_recommendations(keywords)
    return result

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

emrepun

from math import e

class RatingExtractor:
    def __init__(self):
        print("rating initialized")

    # Version-4

    #Returns overall rating score
    #Parameters:

emrepun

from recommender_engine import RecommenderEngine
import json

culture_keywords = "history historical art architecture city culture"
beach_n_sun_keywords = "beach beaches park nature holiday sea seaside sand sunshine sun sunny"
nightlife_keywords = "nightclub nightclubs nightlife bar bars pub pubs party beer"

def get_recommendations(keywords):
    result = RecommenderEngine.get_recommendations(keywords)
    return result

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

emrepun

from recommender_engine import RecommenderEngine
import json

culture_keywords = "history historical art architecture city culture"
beach_n_sun_keywords = "beach beaches park nature holiday sea seaside sand sunshine sun sunny"
nightlife_keywords = "nightclub nightclubs nightlife bar bars pub pubs party beer"

def get_recommendations(keywords):
    result = RecommenderEngine.get_recommendations(keywords)
    return result

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

emrepun

from recommender_engine import RecommenderEngine
import json

culture_keywords = "history historical art architecture city culture"
beach_n_sun_keywords = "beach beaches park nature holiday sea seaside sand sunshine sun sunny"
nightlife_keywords = "nightclub nightclubs nightlife bar bars pub pubs party beer"

def get_recommendations(keywords):
    result = RecommenderEngine.get_recommendations(keywords)
    return result

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

emrepun

import re, math
from collections import Counter

class CosineSimilarity:
    def __init__(self):
        print("Cosine Similarity initialized")

    @staticmethod
    def cosine_similarity_of(text1, text2):
        #get words first