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Sistem Rekomendasi Restoran menggunakan Python

import numpy as np
import pandas as pd
from sklearn.feature_extraction import text
from sklearn.metrics.pairwise import cosine_similarity

data = pd.read_csv("TripAdvisor_RestauarantRecommendation.csv")
print(data.head())

Name       Street Address  \
0  Betty Lou's Seafood and Grill     318 Columbus Ave   
1              Coach House Diner        55 State Rt 4   
2               Table Talk Diner  2521 South Rd Ste C   
3                    Sixty Vines     3701 Dallas Pkwy   
4                   The Clam Bar    3914 Brewerton Rd   

                       Location                                          Type  \
0  San Francisco, CA 94133-3908   Seafood, Vegetarian Friendly, Vegan Options   
1     Hackensack, NJ 07601-6337          Diner, American, Vegetarian Friendly   
2   Poughkeepsie, NY 12601-5476          American, Diner, Vegetarian Friendly   
3          Plano, TX 75093-7777       American, Wine Bar, Vegetarian Friendly   
4            Syracuse, NY 13212                        American, Bar, Seafood   

            Reviews No of Reviews  \
0  4.5 of 5 bubbles   243 reviews   
1    4 of 5 bubbles    84 reviews   
2    4 of 5 bubbles   256 reviews   
3  4.5 of 5 bubbles   235 reviews   
4    4 of 5 bubbles   285 reviews   

                                            Comments   Contact Number  \
0                                                NaN  +1 415-757-0569   
1  Both times we were there very late, after 11 P...  +1 201-488-4999   
2  Waitress was very friendly but a little pricey...  +1 845-849-2839   
3  Not sure why I went there for the second time....  +1 469-620-8463   
4  Doesn't look like much from the outside but wa...  +1 315-458-1662   

                                    Trip_advisor Url  \
0  https://www.tripadvisor.com//Restaurant_Review...   
1  https://www.tripadvisor.com//Restaurant_Review...   
2  https://www.tripadvisor.com//Restaurant_Review...   
3  https://www.tripadvisor.com//Restaurant_Review...   
4  https://www.tripadvisor.com//Restaurant_Review...   

                                        Menu Price_Range  
0               Check The Website for a Menu    $$ - $$$  
1               Check The Website for a Menu    $$ - $$$  
2  http://tabletalkdiner.com/menu/breakfast/    $$ - $$$  
3      https://sixtyvines.com/menu/plano-tx/    $$ - $$$  
4               Check The Website for a Menu    $$ - $$$

Selanjutnya, kita akan memilih dua kolom dari dataset (Name, Type):

data = data[["Name", "Type"]]
print(data.head())

Name                                          Type
0  Betty Lou's Seafood and Grill   Seafood, Vegetarian Friendly, Vegan Options
1              Coach House Diner          Diner, American, Vegetarian Friendly
2               Table Talk Diner          American, Diner, Vegetarian Friendly
3                    Sixty Vines       American, Wine Bar, Vegetarian Friendly
4                   The Clam Bar                        American, Bar, Seafood

Sebelum melanjutkan, yuk mari kita lihat apakah data tadi berisi nilai nol atau tidak:

print(data.isnull().sum())

Name     0
Type    13
dtype: int64

Nah, menurut output di atas, ada data yang memiliki beberapa nilai null di kolom Type. Oleh karena itu kita akan menghapus baris yang berisi nilai nol sebelum melanjutkan:

data = data.dropna()

Jenis restoran adalah fitur yang berharga dalam data untuk membangun sistem rekomendasi. Kolom tipe di sini mewakili kategori restoran. Misalnya, jika pelanggan menyukai restoran yang ramah vegetarian, dia hanya akan melihat rekomendasi jika mereka juga seorang vegetarian. Jadi kita akan menggunakan kolom Type sebagai feature untuk merekomendasikan restoran serupa kepada pelanggan:

feature = data["Type"].tolist()
tfidf = text.TfidfVectorizer(input=feature, stop_words="english")
tfidf_matrix = tfidf.fit_transform(feature)
similarity = cosine_similarity(tfidf_matrix)

Sekarang kita akan mengatur nama restoran sebagai indeks sehingga kita dapat menemukan restoran serupa dengan memberikan nama restoran sebagai input:

indices = pd.Series(data.index, index=data['Name']).drop_duplicates()

Nah, sekarang begini cara menulis fungsi untuk merekomendasikan restoran yang serupa:

def restaurant_recommendation(name, similarity = similarity):
    index = indices[name]
    similarity_scores = list(enumerate(similarity[index]))
    similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)
    similarity_scores = similarity_scores[0:10]
    restaurantindices = [i[0] for i in similarity_scores]
    return data['Name'].iloc[restaurantindices]

print(restaurant_recommendation("Market Grill"))

23                   The Lion's Share
154                        Houlihan's
518            Midgley's Public House
568                 Aspen Creek Grill
770              Pete's Sunset Grille
1190     Paul Martin's American Grill
1581                   Aviation Grill
1872                   Aviation Grill
2193                Crest Bar & Grill
2612    Tahoe Joe's Famous Steakhouse
Name: Name, dtype: object

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