Item recommender

import numpy as np
import pandas as pd


#ratings_data = pd.read_csv("data_test.csv")
ratings_data = pd.read_excel("data_test.xlsx")
ratings_data.head()

#ratings_data.groupby('title')['rating'].mean().head()

ratings_mean_count = pd.DataFrame(ratings_data.groupby('name')['rating'].mean())
ratings_mean_count['rating_counts'] = pd.DataFrame(ratings_data.groupby('name')['rating'].count())
#ratings_mean_count.head()


user_location_rating = ratings_data.pivot_table(index='user_id', columns='name', values='rating')
print('User location rating \n ', user_location_rating.head())

rating_like_this_one = user_location_rating['Johns Restaurant']

print("Rating like this one \n", rating_like_this_one.head())

location_like_this_one = user_location_rating.corrwith(rating_like_this_one)

corr_kasteel_van_arnhem = pd.DataFrame(location_like_this_one, columns=['Correlation'])
corr_kasteel_van_arnhem.dropna(inplace=True)
print(corr_kasteel_van_arnhem.head())
corr_kasteel_van_arnhem.sort_values('Correlation', ascending=False).head(10)
corr_kasteel_van_arnhem = corr_kasteel_van_arnhem.join(ratings_mean_count['rating_counts'])
print(corr_kasteel_van_arnhem.head())
#print("We have recommended for you: ", corr_kasteel_van_arnhem.sort_values('Correlation', ascending=False).head(1)
#print(corr_kasteel_van_arnhem[corr_kasteel_van_arnhem ['rating_counts'] > 50].sort_values('Correlation', ascending=False).head())